9-11/03/2020 - AI3SD, Dial-a-Molecule & Directed Assembly: AI for Reaction Outcome and Synthetic Route Prediction - DeVere Tortworth Court Hotel, Gloucestershire

posted 13 Aug 2019, 02:39 by Samantha Kanza   [ updated 4 Sep 2019, 07:18 ]




This is a joint meeting between the Dial-a-Molecule, Directed Assembly and AI3SD (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) Networks. The meeting will examine the state of the art and future opportunities in the use of Artificial Intelligence to predict the outcome of unknown chemical reactions, and consequently design optimum synthetic routes to desired molecules.  A wide variety of AI approaches will be illustrated including expert systems, statistical methods, mechanism based and Machine Learning.

The meeting will also consider:
  • Data sourcing, sharing, and quality.
  • Automated experimentation to generate reaction knowledge.  
  • Theoretical calculations to enrich or replace experimental data.
The meeting will include talks to introduce the breadth of the area to all participants. Discussion sessions and opportunities to develop collaborations will be a key aspect of the meeting.

Plenary speakers include:
Other speakers include:
Oral and poster contributions are invited.

18-19/11/2019 - AI3SD Network+ Conference - Holiday Inn Winchester & Winchester Science Centre

posted 26 Feb 2019, 08:31 by Samantha Kanza   [ updated 17 Sep 2019, 06:21 ]


Description:
We are the AI3SD Network+ (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery). The network+ is funded by EPSRC and hosted by the University of Southampton and aims to bring together researchers looking to show how cutting edge artificial and augmented intelligence technologies can be used to push the boundaries of scientific discovery. We launched in December 2018, and this conference marks the end of our first year. This is a two day event with a mixture of keynote talks from experts in the different areas of AI for Scientific Discovery, and discussions around different research areas. There will be dedicated time for networking and we will be implementing a smart badge system whereby attendees can mark their badges according to whether they are looking for a collaborator, employment, job candidates, PhD students etc. We will report on the activities of AI3SD over the last year, including the workhops and hackathons we have run and attended, and there will be an opportunity to hear from the projects we funded in AI3SD-FundingCall1. On the evening of the 18th there will be drinks and networking in the Winchester Science Centre followed by a pre dinner talk by famous Science Communicator Steve Mould! This will be followed by a formal conference dinner at the Holiday Inn. 

If you wish to submit a poster (for presentation in Session 2) or a talk (for presentation in Session 6) please fill in our AI3SD 2019 Poster Submission Form or our AI3SD 2019 Abstract Submission Form. The deadline for these submissions is the 1st October at Midnight and we will send out notification of the accepted posters and talks by the 7th October. 

Invited Keynote Speakers:
  • Professor Tim Albrecht - University of Birmingham: Tim joined the School of Chemistry at the University of Birmingham from Imperial College in 2017, as Chair in Physical Chemistry, and became the School's Director of Research in 2018. His research interests cover a broad range of topics with focus on charge transport at the nanoscale, single-molecule thermoelectrics, single-molecule biosensing using nanopores and nanopipettes, automation, data analysis and machine learning, in particular for unsupervised data classification and sensing.

  • Professor Nigel Clarke - University of Sheffield: Nigel joined the Department of Physics and Astronomy at The University of Sheffield as Professor of Soft Matter Theory in 2011, following over 10 years in The Department of Chemistry at Durham University. Nigel is also a University of Sheffield alumnus, having earned his first degree and PhD at the university in 1991 and 1994, respectively. Nigel's experience spans a number of disciplines, including physics, mathematics, material sciences and chemistry.  The range of his research contributions include topics as diverse as current/voltage characteristics in organic photovoltaics, dynamics and structure in polymer nanocomposites, instabilities and pattern formation in thin films, mechanical properties of organo-gels and the cytoskeleton, blood flow through vein valves, phase separation and microstructure evolution in polymer blends and the coupling between phase transitions and flow.  Nigel’s direct contributions encompass theoretical and computational science, simulations and experimental science. 

  • Dr Lucy Colwell - University of Cambridge

  • Dr Olga Egorova - University of Southampton

  • Professor Juan P. Garrahan: University of Nottingham: Juan P. Garrahan has held a Chair in Physics at the University of Nottingham since 2007. His research covers a broad area of theoretical statistical physics and its applications, with particular interests in the dynamics of complex and slow relaxing materials such as supercooled liquids and glasses, molecular self-assembly, quantum non-equilibrium systems, and the theory of large deviations. He obtained his PhD from the University of Buenos Aires in 1997, was Glasstone Fellow at the University of Oxford, an EPSRC Advanced Fellow, and a visiting professor at UC Berkeley in 2007. At the University of Nottingham he is currently the head of the Centre for Quantum Non-Equilibrium Systems (CQNE) and the director of the Machine Learning in Science Initiative (MLiS) of the Faculty of Science.

  • Professor Brian Hayden - University of Southampton: Brian Hayden (FRSC, FIOP) obtained his PhD in Bristol in 1979 was a postdoctoral fellow at the Fritz Haber Institute of the Max Planck Society, Berlin, and appointed lecturer at the University of Bath (1983) and Southampton (1988) where he was appointed to a Personal Chair in 1995. In 2000, he extended thin film MBE based methodologies to the combinatorial synthesis and high-throughput screening of materials. He is a founder (2004) and Chief Scientific Officer of Ilika plc involved in materials discovery and development for the electronics and energy sectors. He is author of over 150 refereed papers {h-index 39}, and 30 active patent families.

  • Professor Ross King - University of Manchester

  • Dr Reinhard Maurer - University of Warwick

  • Professor Matthew Todd - University College London: Mat Todd obtained his PhD in organic chemistry from Cambridge University in 1999, was a Wellcome Trust postdoc at The University of California, Berkeley, a college fellow back at Cambridge University, a lecturer at Queen Mary, University of London and between 2005 and 2018 was at the School of Chemistry, The University of Sydney. He is now Chair of Drug Discovery at University College London. His research interests include the development of new ways to make molecules, particularly how to make chiral molecules with new catalysts. He is also interested in making metal complexes that do unusual things when they meet biological molecules or metal ions. His lab motto is "To make the right molecule in the right place at the right time", and his students are currently trying to work out what this means. He has a significant interest in open science, and how it may be used to accelerate research, with particular emphasis on open source discovery of new medicines. He founded and currently leads the Open Source Malaria (OSM) and Open Source Mycetoma (MycetOS) consortia, and is a founder of a broader Open Source Pharma movement. In 2011 he was awarded a NSW Scientist of the Year award in the Emerging Research category for his work in open science and in 2012 the OSM consortium was awarded one of three Wellcome Trust/Google/PLoS Accelerating Science Awards. For his open source research, Mat was selected for the Medicine Maker's Power List in 2017 and 2018. He is on the Editorial Boards of PLoS One, ChemistryOpen and Nature Scientific Reports. 
Selected Submitted Speakers:
  • Dr Vitaliy Kurlin - University of Liverpool: Vitaliy is a Computer Scientist at the Materials Innovation Factory in Liverpool, where he facilitates the collaboration between Chemists and Computer Scientists. He was awarded the Marie Curie International Incoming Fellowship (2005-2007) and the EPSRC grant “Persistent Topological Structures in Noisy Images" (2011-2013). In 2014-2016 he has gained industrial experience through Knowledge Transfer Secondments in the Computer Vision group at Microsoft Research, Cambridge, UK. From 2018 he leads the Liverpool team on a £2.8M EPSRC 5-year grant “Application-Driven Topological Data Analysis” (with Oxford and Swansea). His research group includes one postdoc and five PhD students working on applications of topology and geometry to Materials Science, Computer Vision and Climate. 

  • Dr Benedict Irwin - Optibrium:

  • Miloslav Torda - Leverhulme Research Centre for Functional Materials Design:
Abstracts: 

Review of AI across UKRI – Dr Anna Angus-Smyth
 
Abstract coming soon.

Predicting the Activity of Drug Candidates where there is No Target  Professor Matthew Todd

Abstract coming soon.

'Next-next' Generation Quantum DNA Sequencing with Chemical Surface Design and Capsule Nets  Professor Tim Albrecht

In the project “'Next-next' Generation Quantum DNA Sequencing with Chemical Surface Design and Capsule Nets”, we combine quantum tunnelling-based biosensing with advanced Machine Learning methods. DNA sequencing based on quantum mechanical tunnelling in principle allows for the label-free identification of nucleotides, based on their intrinsic electronic properties, and thus in some ways constitutes the ultimate limit in single-molecule sensing and sequencing. While the sensor performance is affected by many factors, including the design of the tunnelling junction and the surface chemistry of the (metal) electrodes, in this project the main focus is on maximising the level of information that is extracted from the data. For example, we have been able to demonstrate a significantly improved error rate when employing Convolutional Neural networks for “base calling”, compared to Support Vector Machines, and are now exploring Capsule Nets for further improvements.[1,2]

[1] T. Albrecht, E. Alonso et al., “Deep learning for single-molecule science”, Nanotechnology 2017, (42), 423001. 
[2] A. Vladyka, T. Albrecht et al., manuscript in preparation

Deep Learning Enhanced Quantum Chemistry: Pushing the limits of Materials Discovery  Dr Reinhard J. Maurer

Atomistic simulation based on quantum mechanics (QM) is currently being revolutionized by artificial intelligence and machine-learning (ML) methods. This involves approaches to efficiently predict materials and molecules with specific properties within the vast space of possible chemical compounds. It also involves efficient regression in high-dimensional parameter spaces to accelerate computationally demanding quantum chemical calculations of molecular properties such as the thermodynamic stability or spectroscopic signatures while retaining the predictive power of QM.Most previous approaches have used ML to predict measurable observables that arise from the QM wave function of molecules. However, all properties derive from the wave function, therefore an AI model able to predict the wave function, has the potential to predict all molecular properties. In this talk, I will explore ML approaches to directly represent wave functions for the purpose of developing methods that use AI and quantum chemistry in synergy. After presenting approaches to encode physical symmetries into deep learning infrastructures, I will present our recent efforts to use data-driven deep learning to develop a highly efficient Density-Functional Tight-binding simulation method to describe hybrid metal-organic materials.

Non-equilibrium Physics and Machine Learning  Professor Juan P. Garrahan
I will discuss work at the interface of current questions in non-equilibrium physics and machine learning methods. I will focus on the general statistical mechanics issue of accessing and characterising rare dynamical events in stochastic systems. I will describe the connection between trajectory ensemble methods - often based on the mathematics of large deviations - and reinforcement learning (RL) in Markov decision processes (MDPs). I will explain how the problem of "making rare events typical" in a stochastic system corresponds to finding the optimal dynamics in an MDP. The results I will present illustrate the many possible synergies between statistical physics and machine and deep learning.

Keynote - Dr Lucy Colwell
Title and Abstract coming soon.

Materials Development in the Energy and Electronics Sectors through Combinatorial Synthesis, High-Throughput Screening and Machine Learning – Professor Brian Hayden

The combinatorial synthesis of solid-state materials combined with high-throughput characterization and screening provides an opportunity to develop increasingly large materials data-bases. Machine learning approaches are crucial in several aspects of the building, interpretation and explitation of such data-bases: These can also be constructed to include physical and chemical descriptors from, for example, ab-initio calculation. The challenge is to ensure an audited content and consistent format of the data. Examples of how machine learning is being developed in the interpretation of raw data sets is presented using data from high throughput investigations of electrocatalysts mediating the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) for the development of reversible fuel cells and rechargeable metal-air batteries. The results provide an insight into the potential opportunities of machine learning in the future in the predictive development of functional materials.

Machine Learning for Modelling Microstructure Evolution – Professor Nigel Clarke

Our aim is to enhance our modelling capabilities for microstructure evolution with machine learning. In particular, we focus on Gaussian processes (GPs), a popular non-parametric class of models used extensively in ML and uncertainty quantification, which have well documented predictive abilities. In our preliminary studies, we apply existing GP methodology to microstructure evolution to determine the feasibility of generating an emulator to supplement more traditional, computationally intense, approaches. As an exemplar, we focus on the non-linear Cahn-Hilliard equation for describing phase separation in blends. Spatio-temporal problems are particularly challenging for ML due to their high dimensionaility, hence we use a method recently proposed for using machine learning to predict video images, based on the idea of light cones, in which the present is only dependent on the past in the immediate spatial neighbourhood, analogous to real-space time-stepping numerical schemes for PDEs. We will present results which highlight the both the strengths and challenges of using ML for modelling microstructure.

The Automation of Science: Robot Scientists for Chemistry and Biology – Professor Ross King
A Robot Scientist is a physically implemented robotic system that applies techniques from artificial intelligence to execute cycles of automated scientific experimentation. A Robot Scientist can automatically execute cycles of hypothesis formation, selection of efficient experiments to discriminate between hypotheses, execution of experiments using laboratory automation equipment, and analysis of results. The motivation for developing Robot Scientists is to both to better understand the scientific method, and to make scientific research more efficient.  The Robot Scientist ‘Adam’ was the first machine to autonomously discover scientific knowledge: both formed and experimentally confirmed novel hypotheses.  Adam worked in the domain of yeast functional genomics. The Robot Scientist ‘Eve’ was originally developed to automate early-stage drug development: active machine learning for Quantitative Structure Activity Relationship (QSAR) learning. More recently my colleagues and I have adapted Eve to work on yeast systems biology, and cancer. We argue that it is likely that advances in AI and lab automation will drive the development of ever-smarter Robot Scientists. The Physics Nobel Frank Wilczek is on record as saying that in 100 years’ time the best physicist will be a machine. If this comes to pass it will transform our understanding of science and the Universe.

Active Learning for Computational Polymorph Landscape Analysis - Dr Olga Egorova
Abstract coming soon. 

Isometric classifications of periodic crystals - Dr Vitaliy Kurlin
Solid crystalline materials (briefly, crystals) have numerous applications from high-temperature superconductors to gas capture. A periodic crystal is an infinite arrangement of atoms or molecules obtained by translating a unit cell (a non-rectangular box) in 3 independent directions. The Crystal Structure Prediction (CSP) aims to discover solid crystal that is based on a given chemical composition and has several target properties, most importantly the energy of its thermodynamic stability. Prof Sally Price (UCL) has summarised the state-of-the-art in CSP as "embarrassment of over-prediction", because modern software outputs 1000s of simulated crystals without identifying only few most promising candidates for synthesising in a lab. The underlying problem is the enormous ambiguity of crystal representation via conventional unit cells, because many different unit cells can define identical (up to a rigid motion) or nearly identical crystals. Reduced cells compare crystals only exactly (giving an answer yes/no) without quantifying a similarity between crystals in a continuous way. We propose a new classification of crystals by geometric invariants that are continuously changing under perturbations (atomic vibrations of atoms). These invariants will provide a well-defined distance between crystals that can be used for visualising large datasets of simulated crystals by continuously varying a threshold for similarity. This work is joint with several colleagues from the groups of Prof Andy Cooper FRS and Prof Matt Rosseinsky FRS at the Materials Innovation Factory, University of Liverpool.

Practical applications of deep learning to imputation of drug discovery data - Dr Benedict Irwin 
We describe a novel deep learning method for completing sparse data matrices that accepts both molecular descriptors and sparse experimental data as inputs to exploit the correlations between experimentally measured endpoints, as well as structure-activity relationships (SAR). The method can robustly estimate the confidence in each prediction and often greatly improves the accuracy of prediction over conventional quantitative SAR models. We describe practical applications to drug discovery, including pharma-scale collections comprised of up to one million compounds as well as smaller, project-specific data sets. We illustrate how the filling in of missing data, combined with the ability to focus on the most confident predictions, guides the selection of compounds and prioritisation of experimental resources in hit-to-lead and lead optimisation.

Dense periodic packings in the light of crystal structure prediction - Miloslav Torda
One of the methods in the design of new materials is to predict the crystal structure of a new compound from it’s molecular composition. This process involves generating many hypothetical structures based on lattice energy optimization. A different approach based only on the geometry of a molecule with it’s potential to speed up classical crystal structure prediction computations will be presented. Our preliminary results with regard to the periodic packing of a geometric representations of the pentacene molecule using Monte-Carlo molecular dynamic simulations will be also shown. Limitations and downsides of presented approach will be discussed, and future directions will be proposed.

Draft Programme: 

Day 1 [Holiday Inn then Winchester Science Centre]
- Optional Ethics & AI Workshop [Holiday Inn]
  • 09:00-09:30: Workshop Registration & Coffee
  • 09:30-11:30: Workshop

- Main Conference [Holiday Inn]
  • 11:00 – 11:30: Registration & Coffee

  • 11:30 – 12:00: Introduction & Welcome

  • 12:00 – 12:30: Session 1 - AI3SD Network Retrospective – What have we done in 2019 - Professor Jeremy Frey

  • 12:30 – 13:30: Lunch + Networking

  • 13:30 – 14:00: Review of AI across UKRI – Dr Anna Angus-Smyth

  • 14:00 - 15:00: Session 2: Flash Talks for Online Posters

  • 15:00 - 15:30: Tea & Coffee + Networking

  • 15:30 – 17:00: Session 3 - Network funded projects interim reports

    • 15:30 – 16:00: Predicting the Activity of Drug Candidates where there is No Target - Professor Matthew Todd

    • 16:00 – 16:30: 'Next-next' Generation Quantum DNA Sequencing with Chemical Surface Design and Capsule Nets - Professor Tim Albrecht

    • 16:30 – 17:00: Deep Learning Enhanced Quantum Chemistry: Pushing the limits of Materials Discovery - Dr Reinhard J. Maurer

- Evening Activities [Winchester Science Centre]
  • 17:15 – 19:00: Drinks & Networking in the Science Centre

  • 19:00 – 20:00: Illusions & How the brain works by Steve Mould

- Dinner [Holiday Inn]
  • 20:15 – 22:00: Dinner

  • 22:00: End of Day 1

Day 2 [Holiday Inn]
  • 08:30 – 09:00: Tea & Coffee

  • 09:00 – 09:30: Keynote [TBC] 
  • 09:30 – 10:00: Non-equilibrium Physics and Machine Learning - Professor Juan P. Garrahan
  • 10:00 – 11:30: Session 4 – AI , Molecules & Materials

    • 10:00 – 10:30: Keynote - Dr Lucy Colwell

    • 10:30 - 11:00: Materials Development in the Energy and Electronics Sectors through Combinatorial Synthesis, High-Throughput Screening and Machine Learning – Professor Brian Hayden

    • 11:00 – 11:30: Discussion Session

  • 11:30 – 12:00: Tea & Coffee Break + Networking

  • 12:00 – 13:15: Session 5 – Talks from EPSRC AI Feasibility studies

    • 12:00 – 12:25: Machine Learning for Modelling Microstructure Evolution – Professor Nigel Clarke

    • 12:25 – 12:50: The Automation of Science: Robot Scientists for Chemistry and Biology – Professor Ross King
    • 12:50 – 13:15: Active Learning for Computational Polymorph Landscape Analysis - Dr Olga Egorova

  • 13:15 - 14:00: Lunch & Networking

  • 14:00 – 16:00: Session 6 – Contributed Talks

    • 14:00 – 14:20: Isometric classifications of periodic crystals - Dr Vitaliy Kurlin
    • 14:20 – 14:40: Practical applications of deep learning to imputation of drug discovery data: Dr Benedict Irwin 
    • 14:40 – 15:00: Dense periodic packings in the light of crystal structure prediction - Miloslav Torda
  • 16:00 – 16:30: Tea & Coffee Break + Networking

  • 16:30 – 17:00: Summing up and end of conference

Evening Activities on the 18th:
Our evening activities will begin with drinks and networking in the Winchester Science Centre. There will be a free drink on arrival, and the top floor of the science centre has been reserved for conference attendees to view and interact with the displays. This will be followed by a fascinating pre dinner talk by famous Science Communicator Steve Mould who will be talking on Illusions and How the Brain works. This will be followed by a formal conference dinner back at the Holiday Inn. 

Accommodation: 
We have reserved a number of rooms at the Winchester Holiday Inn on the nights of the 17th and 18th November for conference attendees at a rate of £100. You will be able to ring up and book one of these rooms until the 7th October, upon which they will be released and anybody looking to book a room after this point will be quoted the current selling rate. The details for the Winchester Holiday Inn can be found here. Please quote the University of Southampton and the 18th November when you ring up to book.

FAQs
  1. Who should attend?
    Anyone with an interest in Artificial Intelligence, Augmented Intelligence, Automated Investigations, Machine Learning, Scientific Discovery, Materials Discovery, and the Philosophical and Ethical Implications of Artificial Intelligence. AI3SD includes members from academia, industry and government and we welcome new members from each of these sectors to add to our growing Network+ so that we can form new interdiscplinary partnerships and work together to futher the field of scientific discovery using AI techniques.
  2.  What will I get out of it?
    You will be able to network with likeminded people who have research interests that complement yours. You will find out about the work our Network+ has done over the past year (including funded projects, conferences, workshops, and hackathons) and hear about the opportunities we have available for 2020. 
  3. What are the aims of the Conference?
    This conference is aiming to help the Network+ to drive progress in the areas of AI for Scientific Discovery and facilitate collaboration by introducing people to make new interdisciplinary teams, and to produce new grant applications. To achieve this we may commission literature reviews, papers, or small scale investigations to test out new ideas. We welcome ideas and suggestions about how to go forward in this area and how best to achieve our aims. This conference will also report on the progress the Network+ has made over the last year so that current and new members can see what has been achieved and contribute to discussions about the activities of the Network+ in 2020. 
  4. What are the themes of the Conference?
    The main themes addressed in this conference are AI, Molecules, & Materials, and the current state and recent advancements in AI and Machine Learning. Alongside these will be contributed talks on research relevant to AI3SD, and reports on pilot projects funded by or relevant to AI3SD.

17/10/2019 - AI3SD & IoFT AI Technologies for Allergen Detection and Smart Cleaning within Food Production - SCI, London

posted 14 Feb 2019, 03:16 by Samantha Kanza   [ updated 16 Sep 2019, 15:27 ]

Eventbrite Link: https://www.eventbrite.co.uk/e/ai3sd-ioft-ai-for-allergen-detection-workshop-tickets-69275899079 

This event is brought to you by the AI3SD (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) and the IoFT (Internet of Food things) Networks. As food allergies and intolerances are on the rise, allergen detection and awareness is becoming more critical than ever at all stages of the food production pipeline; from cleaning the factories and kitchens the food is produced in, to detecting allergens in food, right through to creating allergen free food in the future. Unsurprisingly research has turned to technological solutions to combat this issue. This workshop is centered around the usage of Artificial Intelligence in Allergen Detection and Smart Cleaning within Food Production; research areas that co-align between both AI3SD & IoFT. The workshop will begin with some thought provoking talks to report on the current state of affairs, and consider where we need to be going in the future. There are six main working group topics identified for this workshop, and talks will be given on the different aspects that need to be considered with respect to allergen detection and smart cleaning before we break into the working groups for more formal discussions. There are multiple sessions for the working group discussions, and so there will be opportunities to take part in as many group discussions as you wish. The workshop will be formally recorded and the suggestions for going forward will be captured in a position paper. Lunch will be provided and the workshop will end with networking drinks. 


The programme for the day is as follows:
  • 10:00-10:30: Registration & Coffee
  • 10:30-10:45: Welcome from Professor Jeremy Frey & Professor Simon Pearson
  • 10:45-11:15: Speaker TBC
  • 11:15-11:45: Speaker TBC
  • 11:45-12:15: Speaker TBC
  • 12:15-1300: Lunch
  • 13:00-13:15: Smart Cleaning & Robots in Factories - Dr Nicholas Watson
  • 13:15-13:30: AI in Allergen Detection - Steve Brewer
  • 13:30-14:30: Working Group Discussions
  • 14:30-14:45: Coffee Break
  • 14:45-15:30: Working Group Discussions
  • 15:30-16:00: Working Groups Report Back, Decide on Next Steps
  • 16:00-17:00: Networking Drinks

FAQs
1. Who should attend? 
Anyone with an interest in aspects of the Food Chain & Food Production, Internet of Things, Artificial Intelligence, Machine Learning, Statistics & Probability, or Data Science. We welcome members from academia, industry and government. We are always looking to grow both our Networks (AI3SD & IoFT) and bring in people with a wealth of experience in the many different subject areas that are needed so that we can form interdisciplinary partnerships and work together to further the field of these technologies in Food and Scientific Discovery. 

2. What will I get out of it? 
You will be able to network with like-minded people who have research interests that complement yours. There will be some keynotes to detail the state of play for the Food Industry with respect to Allergen Detection, and flash talks from each of the working group heads to spark discussion and ideas. There are five main working groups identified for this workshop, so you will have the opportunity to enter whichever discussions group(s) most align with your interests and there will also be opportunities for general networking. Members of the Network Executive Group from both AI3SD & IoFT will also be in attendance so you will be able to find out more about our Networks and the opportunities we have available including funding opportunities and the types of events we will be running (e.g. workshops, conferences and hackathons).

3. What are the aims of the workshop? 
This workshop is aiming to help the two EPSRC-funded Networks to drive progress in this area and facilitate collaboration by introducing people to make new interdisciplinary partnerships, and to potentially generate new grant applications. We have identified the main topics that require investigation in this area, and are planning to produce a position paper as an output of this workshop, and this will include a list of recommendations of best practices and approaches. 

4. What are the themes of the workshop? 
There are six main working groups for this workshop:
  • Data Collection / Data Storage / Data Sharing / Data Privacy
  • Data Decision Making / Data Analytics / Data Visualization
  • Data Responsibility / Approaches / Ethics
  • Hardware Approaches to using IoT to monitor Allergens
  • Cleaning Robots & The Factory
  • Future Production of Allergen Free Foods

12-13/09/2019 - AI3SD Machine Learning for Chemistry Training Workshop & Hackathon - Wide Lane Southampton

posted 14 Feb 2019, 03:14 by Samantha Kanza   [ updated 11 Sep 2019, 09:13 ]

Eventbrite Link: https://www.eventbrite.co.uk/e/ai3sd-machine-learning-for-chemistry-training-workshop-hackathon-tickets-65426429211

Dataset and Challenges Link: https://docs.google.com/document/d/1Luz0t9QCWHyF9qdycbWvk6ULd9RD_7t8/edit 

This training workshop and hackathon is intended to help upskill scientists in AI and Machine Learning techniques for Chemistry and provide some challenges to test out their new skills. This event will run over two days with lunch and dinner provided on day 1 and lunch provided on day 2. Day 1 will begin with some training courses to add to or refresh your knowledge, followed by creating your teams and choosing which challenge you wish to address! There will be mentors on hand to provide advice during the hacking, and there will be pizza provided and the use of the wide lane facilities until 8pm. On day 2, the morning will be spent hacking and after lunch the groups will come together to present their work!

Training Sessions & Talks
  • Data Science Awareness Session - Steve Brewer. This training session will provide a general overview of what a data scientist is, from the perspective of working on the EDISON Project.
  • Progressing from basic to advanced Machine Learning - Professor Mahesan Niranjan. This training session will introduce some more advanced machine learning methods and demonstrate how to implement them on top of or instead of your basic ML techniques.
  • Dataset Training - The different datasets will be introduced including details on what they contain and the apis required to access them.
Challenges & Datasets

1. Solubility Challenge - Dr Nicola Knight
2. Physical Science Data Science Service and ChEMBL Mashup Challenges - Professor Simon Coles & Dr Tim Rozday
3. Chemical Safety Challenge - Dr Nick Lynch
4. Or Bring your own Challenge! If you have an idea for the next funding application and want to try it out in a hackathon style environment then feel free to bring your own challenge.

Additional Potential useful Datasets

Programme

Day 1
  • 09:30-10:00: Coffee & Registration
  • 10:00-10:15: Welcome Introduction – Professor Jeremy Frey
  • 10:15-11:00: Data Science Awareness Session - Steve Brewer
  • 11:00-11:45: Progressing from Basic to Advanced Machine Learning – Professor Mahesan Niranjan
  • 11:45-12:30: Access to Datasets + Introduction of Challenges
  • 12:30-13:30: Lunch & Team Formation
  • 13:30-15:00: Hacking
  • 15:00-15:15: Coffee Break
  • 15:15-16:30: Hacking
  • 16:30-17:00: Coffee Break
  • 17:00-20:00: Hacking (Pizza and refreshments will be provided at 18:30)
Day 2
  • 09:00-09:30: Coffee
  • 09:30-11:00: Hacking
  • 11:00-11:15: Coffee
  • 11:15-12:30: Hacking
  • 12:30-13:30: Lunch
  • 13:30-14:15: Hacking
  • 14:15-15:15: Finalise Work & Prepare Presentations
  • 15:15-15:30: Coffee
  • 15:30-16:30: Presentations & Feedback
  • 16:30-17:00: Wrap Up / Summary Feedback & Prizes
Travelling to the Event
  • By Bus: The U1A Bus will take you directly to Southampton Airport Parkway which is just across the road from the Wide Lane Sports Grounds
  • By Train: Southampton Airport Parkway is just across the road from the Wide Lane Sports Ground, or if your train can only arrive at Southampton Central or St Denys then you can get the U1A bus from either of these stations to Airport Parkway
  • By Car: The postcode for the Wide Lane Sports Ground is SO50 5PE and there will be parking available for delegates. Upon arrival please inform the staff manning the barriers that you are part of this event and they will let you through.
  • By Plane: Southampton Airport is just across the road from the Wide Lane Sports Ground.
Accommodation
If you do not live locally and need somewhere to stay we recommend the Premier Inn by Southampton Airport.

FAQs
  1. Who should attend? Anyone with an interest in any of the training, challenges or interested in tackling their own challenge in a hackathon style environment. We welcome members of academia or industry working in or with an interest in any of the science, mathematics or machine learning techniques required for these challenges. Feel free to come as a team, or come by yourself and we will find you a team to work with.
  2. What will I get out of it? You will gain useful skills from the training sources, hear about a range of interesting useful data sources and challenges, and have the opportunity to take part in a hackathon in a supportive team environment with mentors and helpers. You will have the opportunity to work with other likeminded people from different academic and industrial backgrounds. There will be lunch and dinner provided on day 1, and lunch on day 2, with multiple coffee breaks throughout the day. 
  3. What are the aims of the training session / hackathon? The aims of this session are to skill you up on data science and machine learning techniques and given you the opportunity to try these new skills out during the hackathon. 

11/09/2019 - AI3SD Network+ TownMeeting & Funding Workshop - Wide Lane, Southampton

posted 14 Feb 2019, 03:13 by Samantha Kanza   [ updated 4 Sep 2019, 09:03 ]


This meeting has been organised to provide useful information about both the AI3SD-FundingCall2 and funding call applications in general. There will be talks from experts in the areas of IP for AI, there will be a top tips for writing your funding application session where we will provide advice on strengthening your applications based on previous experience of reviewing funding applications. There will also be an opportunity to ask questions about our second funding call and to find other people / institutions to collaborate with.  We strongly encourage all interested parties to come along, as we hope that this event will not only answer any questions you have, but offer an opportunity to match up companies with academic institutes for collaboration on projects proposals. All questions and answers will be written up and added to our Funding Call Page as an FAQ. 
A draft Agenda for the day is as follows:
  • 10:00-10:30: Coffee & Registration
  • 10:30-11:00: Welcome from Professor Jeremy Frey & Introduction of Funding Call
  • 11:00-11:45: Building Sustainable Research Software - James Graham
  • 11:45-12:30: Aspects of Intellectual Property for AI Research Software - David Woolley
  • 12:30-13:30: Lunch followed by Coffee
  • 13:30-14:15: Top tips for writing your funding application (based on lessons learned from last time) & Further Discussions - Dr Samantha Kanza
  • 14:15-15:00: Questions & Discussions about the funding (which will be written up into an FAQ)
  • 15:00-15:30: Flash talk introductions from attendees
  • 15:30-16:30: Networking session with coffee to match up academic institutes and companies looking for collaborators
  • 16:30-17:00: Wrap up and conclusions
Travelling to the Event
  • By Bus: The U1A Bus will take you directly to Southampton Airport Parkway which is just across the road from the Wide Lane Sports Grounds
  • By Train: Southampton Airport Parkway is just across the road from the Wide Lane Sports Ground, or if your train can only arrive at Southampton Central or St Denys then you can get the U1A bus from either of these stations to Airport Parkway
  • By Car: The postcode for the Wide Lane Sports Ground is SO50 5PE and there will be parking available for delegates. Upon arrival please inform the staff manning the barriers that you are part of this event and they will let you through.
  • By Plane: Southampton Airport is just across the road from the Wide Lane Sports Ground.
FAQ
1. Who should attend? 
Anyone who is considering applying for our second funding call. Even if you do not currently have an established project or team it would still be very worthwhile to attend as you will gain further understanding of the funding process, and potentially meet people to collaborate with at the meeting!

2. What will I get out of it? 
You will have the opportunity to learn about our funding call process. There will be opportunities to ask specific questions about our funding process, and there will be sessions to provide advice on how best to structure your funding application. There will also be useful talks about IP for AI and plenty of opportunities for networking and potentially finding new collaborators.

18-19/07/2019 - AI3SD, Dial-a-Molecule, Directed Assembly & University of Leeds AI and ML in Chemical Discovery and Development - Weetwood Hall, Leeds

posted 14 Feb 2019, 03:12 by Samantha Kanza   [ updated 18 Sep 2019, 07:06 ]


Description
This is a joint networks event between AI3SD, Dial-a-Molecule, Directed Assembly Network and the University of Leeds, and it will be held at Weetwood Hall in Leeds on 18-19th July 2019.

This residential event aims to bring together stakeholders with different backgrounds, e.g.academic/industry, researchers/data owners, and chemists/engineers/computer scientists, to discuss applications of AI and Machine Learning in Chemical Discovery and Development. A series of structured discussion sessions over the two days will be carried out to form a general consensus on some key objectives and milestones to deliver the promised impacts of these important tools within the remit of the three networks.  The discussions are also expected to lead to new and unusual collaborative project proposals which may address the more immediate objectives.

This event is free to attend and registration will include all refreshments, lunches on both days, a networking dinner on the 18th and, if required, one nights’ accommodation at Weetwood Hall. Numbers are strictly limited. We expect demand to be high and we want to make sure we have a good balance of interests amongst our attendees. Therefore we ask all those interested to submit a short application on the Leeds AI_ML event application form by the closing date of 17th May. Applications will be assessed and applicants will be notified of the outcome within 4 weeks of submission.

The following priming talks will be given:  

The final agenda is as follows:

Day 1 (18th July 2019)
  • 10.00  10.30: Reception and tea/coffee
  • 10.30  10.45: Welcome and introduction - Bao Nguyen
  • 10.45  11.00: AI Technologies - David Hogg, University of Leeds
  • 11.00  11.30: Learning Chemistry with Machines - Professor Jonathan Goodman
  • 11.30  12.15: Discussion on potential applications of AI/ML in chemical discovery and development
    • Targeted Areas
    • Key Challenges
  • 12.15  13.30: Lunch and interests identification
  • 13.30  13.45: Martin Elliott, Directed Assembly network
  • 13.45  14.45: Discussion on AI/ML methodology
    • Method/problem suitability/benchmarking standards
    • Confidence and uncertainty
    • How to deal with non-perfect data? Chemical bias/intervention/causal relationship and ML as an analytical tool?
  • 14.45  15.00: Coffee/tea break
  • 15.00  16.00: Discussion on data
    • What is ‘good’ data in your area of interest?
    • What to do about old data?
    • Can industrial data be shared?
  • 16.00  16.15: Discussion/vote on Research Areas for Day 2
  • 18.00  19.00: Pre-dinner drink at the Stables pub, Weetwood Hall
  • 19.00: Dinner at Weetwood Hall
Day 2 (19th July 2019)
  • 9.15  9.30: Humanity v The Machines: An AI Challenge - Dr John Mitchell
  • 9.30  10.30: Discussion on milestones for research area 1-3 (rotation 1)
  • 10.30  11.00: Coffee/tea break
  • 11.00  12.00: Discussion on milestones for research area 1-3 (rotation 2)
  • 12.00  13.00: Lunch
  • 13.00  14.00: Discussion on milestones for research area 1-3 (rotation 3)
  • 14.00  15.00: Discussion on wider engagement
    • Missing expertise
    • Training pipeline
    • Wider perspectives on application of AI/ML to chemical research and development
  • 15.00  15.15: Closing remarks/summary

01/05/2019 - AI3SD Semantics and Knowledge Learning for Chemical Design Workshop - Solent Conference Centre, Southampton

posted 3 Jan 2019, 08:42 by Samantha Kanza   [ updated 30 Apr 2019, 06:40 ]

Eventbrite Link: https://www.eventbrite.co.uk/e/ai3sd-semantics-and-knowledge-learning-for-chemical-design-workshop-tickets-55073920579 

Description 
Designing chemicals, discovering new drugs, discovering materials and indeed all aspects of scientific discovery are all tasks that are highly data driven, and semantic web technologies are key to enabling researchers to deal with high levels of data in a useful and meaningful way. Semantic technologies facilitate representing data in a formal, structured, and interoperable way, and enable data to be reasoned over to infer potential relationships. In this workshop we seek to explore the ways in which semantic web technologies can be used to drive predictions in chemical design, including using Machine Learning and other AI techniques to exploit semantic links in knowledge graphs and linked datasets. There will be several keynote talks, an expert panel chaired by Professor Jeremy Frey, and a chance for breakout discussions. Lunch will be provided and the day will end with networking drinks.

Keynote Speakers
  • Dr Colin Bachelor - Colin Batchelor is a theoretical chemist by training, and now works at the Royal Society of Chemistry. He started as a technical editor before moving over to work on Project Prospect and ChemSpider before joining the Data Science team. He has published on natural language processing for chemistry, ontologies and cheminformatics.

  • Dr Age Chapman - Age is an Associate Professor of Computer Science at the University of Southampton, Co-Director of the ECS Centre for Health Technologies, and is part of the Web and Internet Science(WAIS) Research Group. Her research is in the area of database systems, focusing on using data appropriately and effectively. This involves solving problems that span the areas of databases, information discovery and retrieval, provenance and algorithmic accountability.

  • Dr Nicholas Gibbins - Nick is an Associate Professor in Computer Science at the University of Southampton and is part of the Web and Internet Science (WAIS) Research Group. His primary research interests are in the Semantic Web, Hypertext and Distributed Information Systems.

  • Professor Jonathan Goodman - Jonathan is a Professor of Chemistry and Director of Studies of Chemistry at the University of Cambridge, where he also serves as the Academic Dean. His research focuses on experimental and computational chemistry.

  • Dr Alexandra Simperler - Alexandra is a freelance consultant who works with Dr Gerhard Goldbeck of Goldbeck Consulting on the H2020 EU project European Materials Modelling Council (EMMC). She is interested in finding holistic solutions to ingrate materials modelling deeper into industrial workflows.

Keynote Abstracts
  • You did WHAT? - Dr Age Chapman: During scientific research and experimentation, information is discovered, generated, processed, analysed and disseminated. Rinse. Repeat. At each point, a user must understand what happened previously to the data and what impact that may have on their current work. During this talk, I will describe the notion of provenance, the history of creation and modification of data. This talk will provide an overview of how provenance can be used to support trust, the tools available for its capture and manipulation in various different scientific settings. I will then move on to describe how provenance provides a backbone for reasoning over choices made and their impact on results.

  • The Semantic Web at 20: Lessons from two decades of developing linked data applications - Dr Nicholas Gibbins: The Resource Description Framework, the first of the technologies that underpin the Semantic Web, became a W3C recommendation in February 1999. Since that date, a large and vibrant research community has grown up around the Semantic Web, yet widespread visibility of Semantic Web technologies has been slower than the early hype would have had us believe. In this talk, I examine the growth of the Semantic Web, assess its technological maturity, and examine likely future developments.

  • Challenging Chemistry: Solving Molecular Problems - Professor Jonathan Goodman: The power of AI is changing the way that chemistry is done, but chemistry is far from being solved. What is holding us back? Creating a new molecule or a necessary, but unknown, transformation is a demanding problem. Successes and surprises in predicting how molecules should behave can be used to try to discover the limits of our knowledge and the best ways to solve molecular problems.

  • Semantics vs. Statistics in Chemistry – Dr Colin Bachelor: Since the late 1990s, natural language processing (NLP) has seen a massive shift from high-precision, low-recall systems based on small sets of hand-written rules, to methods based on the statistical analysis of large corpora. The field of chemoinformatics, likewise, is dominated by statistical and machine-learning approaches. More recently deep learning methods have had surprising success in aspects of natural language processing, image processing and board games. Conversely, pharmaceutical companies have been engaging more and more with Semantic Web technologies, which are largely built around the sorts of hand-written systems that NLP has moved away from this century. In this talk I cover how we have applied both sorts of systems at the Royal Society of Chemistry and their strengths and weaknesses.

  • EMMO (European Materials & Modelling Ontology): semantic knowledge organisation for applied sciences - Dr Alexandra Simperler: Semantic knowledge organisation refers to the organisation of information about a given domain providing not just data but some level of detail regarding meaning and logic. The European Materials & Modelling Ontology (EMMO), developed within the EMMC is a multidisciplinary effort aimed at providing a standard representational framework for materials and their modelling. An introduction to semantic knowledge organisation and the EMMO will be provided. In the advent of digitalisation, the aim is to come up with tool to build a ‘digital twin’ of a material, representing its key traits at different levels of granularity and its changes over time. Likewise, potential materials models (electronic, atomistic, mesoscopic and continuum) relate to granularity perspectives of the material. EMMO hence provides the basis for progress beyond syntactically based scripted workflows and reach true (semantic) interoperability between models. Therefore, solutions should be found that are based on semantic approaches with metadata backed up by an ontology framework. It will be discussed, how EMMC supports efforts to achieve interoperability of materials models and by establishing open standards for the integration of different codes (e.g. academic and commercial, open and close source), referred to as the Open Simulation Platform (OSP). The work presented is based on the efforts of Gerhard Goldbeck (Goldbeck Consulting, Emanuele Ghedini (University of Bologna), Jesper Friis (SINTEF), Adham Hashibon (Fraunhofer IWM), and Georg J. Schmitz (ACCESS).

Programme
The programme for the day is as follows:
  • 10:00-10:30: Coffee & Registration
  • 10:30-11:00: Welcome Introduction – Professor Jeremy Frey
  • 11:00-11:30: You did WHAT? – Dr Age Chapman
  • 11:30-12:00: Keynote - Dr Nick Gibbins
  • 12:00-12:30: Challenging Chemistry: Solving Molecular Problems – Professor Jonathan Goodman
  • 12:30-13:30: Lunch
  • 13:30-14:00: Semantics vs. Statistics in Chemistry - Dr Colin Bachelor
  • 14:00-15:00: Breakout Discussions
  • 15:00-15:30: Coffee & Report Back
  • 15:30-16:00: EMMO (European Materials & Modelling Ontology): semantic knowledge organisation for applied sciences - Dr Alexandra Simperler
  • 16:00-17:00: Panel Discussions chaired by Professor Jeremy Frey
  • 17:00-18:00: Drinks Reception
FAQs 

1. Who should attend? 
Anyone with an interest in Semantic Web Technologies (either in their own right or as a technology to be used in conjunction with Artificial Intelligence or Machine Learning), Scientific Discovery, Chemical Design, Knowledge learning for Scientific Discovery. We welcome members from academia, industry and government. We are always looking to grow our Network+ and bring in people with a wealth of experience in the many different subject areas that are needed so that we can form interdisciplinary partnerships and work together to further the field of Scientific Discovery.

2. What will I get out of it? 
You will be able to network with likeminded people who have research interests that complement yours. You will hear a range of thought-provoking talks about different aspects of semantics and knowledge learning for chemical design, and have the opportunity to both discuss this subject area with other members of the workshop, and address questions and raise areas of discussion to our expert panel. Members of the Network Executive Group will also be in attendance so you will be able to find out more about our Network and the opportunities we have available including funding opportunities and the types of events we will be running (e.g. workshops, conferences and hackathons). 

3. What are the aims of the workshop? 
This workshop is aiming to help the Network+ to drive progress in this area and facilitate collaboration by introducing people to make new interdisciplinary teams, and to produce new grant applications. To achieve this we may commission literature reviews, papers, or small scale investigations to test out new ideas. We welcome ideas and suggestions about how to go forward in this area and how best to achieve our aims.

4. What are the themes of the workshop? 
This workshop will begin by covering the topics of semantic web technologies, from their conception to the state of the available semantic web tools of today, alongside best practices of research data management with a strong focus of provenance. The workshop will then segway into the application of these technologies to chemistry in the form of creating chemistry ontologies, and using semantic web and AI technologies to make advances in chemical discovery. 

19/03/2019 - AI3SD AI for Materials Discovery Workshop - University of Southampton

posted 3 Jan 2019, 08:27 by Samantha Kanza   [ updated 15 Apr 2019, 04:04 ]


Description 
Materials play a key role in modern society and the growing demands for functionality, selectivity, re-usability, efficiency and environmentally sustainable which all place huge demands on the chemistry.  The use of Computational Chemistry and more generally Artificial and Augmented Intelligence (AI) to predict structure and function provides new possibilities in using theory to drive innovation and discovery. 

Agenda 
  • 13:00-13:30 - Registration & Coffee 
  • 13:30-13:45 - Welcome from Professor Jeremy Frey 
  • 13:45-14:30 – Theoretical Studies of CO and CO2 Hydrogenation to Methanol and Conversion of Methanol to Olefins – Professor Felix Studt  
  • 14:30-15:15 - LAISER: Putting the AI in Laser - Dr Ben Mills 
  • 15:15-15:30 - Coffee Break 
  • 15:30-16:15 - Machine learning opportunities in prediction-led discovery of molecular materials – Professor Grame Day 
  • 16:15-17:00 - Potential Solutions to Mathematical Challenges for Solid Crystalline Materials – Dr Vitaliy Kurlin 
  • 17:00-17:45 – One million crystal structures: what can we learn? – Dr Angeles Pulido 
  • 17:45-18:00 - Wrap up & Conclusions
Keynote Speakers 
  • Professor Felix Studt - Felix is a Professor at the Institute of Chemical Technology and Polymer Chemistry (ITCP) and the Institute of Catalysis Research and Technology (IKFT) at the Karlsruhe Institute of Technology (KIT). His research interests are in theory guided materials discovery, electrochemical processes, synthesis gas conversion, CO2 reduction, and routes from biomass to chemicals. He has published over 70 papers which have had over 2900 citations, and has been invited to speak at many international conferences. He has also published a textbook on the  Fundamental Concepts in Heterogeneous Catalysis.  
  • Dr Ben Mills - Ben is a Senior Research Fellow and EPSRC Early Career Fellow at the Optoelectronics Research Centre at the University of Southampton, where he leads a research group focussed at the interface of laser machining and machine learning. Ben completed his PhD in high harmonic generation via ultrafast lasers at the University of Southampton in 2009, and then became the manager of the “FAST lab”, a femtosecond laser facility at the University. His background covers 10 years in ultrafast lasers and their application for high-precision materials processing. Current research interests also include laboratory automation and machine learning, in particular convolutional neural networks. 
  • Professor Graeme Day - Graeme is a Professor of Chemical Modelling at the University of Southampton. His research interests centre on the development and application of computational methods for understanding and predicting the structures and properties of molecular materials. An area of particular interest is crystal structure prediction, and its applications in structure determination, polymorph discovery and the design of materials with targeted properties. These research areas all stem from a fundamental interest in understanding and modelling intermolecular interactions. Graeme is the author or co-author of over 115 publications, including 5 book chapters. He serves on the advisory board for the Royal Society of Chemistry’s journal Molecular Systems Design and Engineering, is on the steering committee of the UK Materials Chemistry High End Computing Consortium and is a member of the EPSRC peer review college.
  • Dr Vitaliy Kurlin - Vitaliy is a Computer Scientist at the Materials Innovation Factory in Liverpool, where he facilitates the collaboration between Chemists and Computer Scientists. He was awarded the Marie Curie International Incoming Fellowship (2005-2007) and the EPSRC grant “Persistent Topological Structures in Noisy Images" (2011-2013). In 2014-2016 he has gained industrial experience through Knowledge Transfer Secondments in the Computer Vision group at Microsoft Research, Cambridge, UK. From 2018 he leads the Liverpool team on a £2.8M EPSRC 5-year grant “Application-Driven Topological Data Analysis” (with Oxford and Swansea). His research group includes one postdoc and five PhD students working on applications of topology and geometry to Materials Science, Computer Vision and Climate. 
  • Dr Angeles Pulido - Angeles is a Research and Application Scientist at the  Crystallographic Data Centre (CCDC) the Cambridge CDC, she is  part of the Pfizer Design Centre within the Materials Science team who apply computational techniques to study organic molecular crystals relevant to pharmaceutical industry, with especial interest in crystal structure prediction, materials stability and polymorphism. Angeles’ main research interest is in silico modelling of solids and the use of computational techniques to provide an atomistic view and a better understanding of thermodynamic, kinetic and spectroscopic features of crystalline organic and inorganic materials.
Keynote Abstracts 
  • Theoretical Studies of CO and CO2 Hydrogenation to Methanol and Conversion of Methanol to Olefins – Professor Felix Studt: The catalytic conversion of CO2 to fuels and chemicals is experiencing renewed interest and growth as it is seen as one of the cornerstone reactions in a future sustainable energy scenario. Methanol, which can also be produced from CO2, is also an important chemical building block as it can be converted to olefins, hydrocarbon and gasoline. Theoretical studies of the processes at the catalytic surfaces help to understand how these catalyst function on the atomic-scale. Here insight gained on the active site of methanol synthesis[1] as well as the selectivity for CO and CO2 hydrogenation[2] is used for the computational screening of new CO2 hydrogenation catalysts.[3] We also investigated the conversion of methanol to olefins in zeolite catalysts using a combination of ab initio/density functional theory and microkinetic/reactor modeling.[4,5] In addition we will show how theory can help establishing trends across different acid sites and various frameworks,[6-8] a finding that might serve as a guidance for the discovery of improved catalysts for the production of fuels and chemicals from methanol. 
  • LAISER: Putting the AI in Laser – Dr Ben Mills: Advances in lasers now allow the laser-based processing of almost any material. Innovation in this field is now becoming heavily focussed on making existing processing techniques more precise and efficient. A research area of particular current importance is therefore the development of real-time monitoring and feedback systems for laser machining, via visual inspection of the sample during machining. Convolutional neural networks (CNNs) offer the capability for image processing without the need for understanding the underlying physical processes, and hence offer an ideal solution for the monitoring of laser machining, which itself is not fully understood. In this talk, the application of CNNs for real-time monitoring and process control for laser machining will be discussed, along with the capability of CNNs for predicting the outcome of laser machining before the experiment occurs. In addition, an application of combining laser light with CNNs for real-time sensing of pollution particulates will be demonstrated. 
  • Machine learning opportunities in prediction-led discovery of molecular materials - Professor Graeme Day: Predictive computational approaches have developed rapidly as tools to accelerate the discovery of molecular materials with targeted properties. A challenge in developing the use of these approaches is the expense of both crystal structure prediction and property prediction, and the difficulty of interpreting the resulting energy-structure-function landscapes, which normally contain huge numbers of possible structures. The talk will discuss opportunities for developing machine learning approaches to improve the speed and reliability of computational predictions. 
  • Potential Solutions to Mathematical Challenges for Solid Crystalline Materials – Dr Vitaliy Kurlin: Abstract. Solid crystalline materials (briefly, crystals) can be modelled as periodic structures based on a geometric pattern that represents any chemical composition. One of the challenges in crystal structure prediction is to encode any crystal in a unique numerical form that is convenient to compare crystals and to search for new crystals with better properties. The talk will discuss continuous geometric invariants that will enable a more efficient search in the huge configuration space of all possible crystals. 
  • One million crystal structures: what can we learn? – Dr Angeles PulidoThe Cambridge Structural Database (CSD) is fast approaching the astonishing milestone of 1 million crystal structures. The CSD captures not just crystallographic structural data, but it intrinsically also contains an enormous amount of experimental information on molecular conformations and interactions, as well as physico-chemical properties. This chemistry and property information is key to underpinning the challenge of computer-led materials design and development. This talk will focus on how AI strategies have been used to transform the vast amount of scientific information in the CSD into actionable knowledge: from approaches to improve data curation and quality; to the development of methodologies to assist in drug development. Some of the challenges faced by AI approaches will be discussed, as well as the potential for empowering and further enriching the information in the CSD.
FAQ 

1. Who should attend? 
Anyone with an interest in Materials discovery, Artificial Intelligence, Machine Learning, Deep Learning, and particularly those looking to apply AI technologies to materials discovery. We welcome members from academia, industry and government. We are always looking to grow our Network+ and bring in people with a wealth of experience in the many different subject areas that are needed so that we can form interdisciplinary partnerships and work together to further the field of Scientific Discovery. 

2. What will I get out of it? 
You will be able to network with likeminded people who have research interests that complement yours. There will be several keynotes around the topics of AI for Materials Discovery to spark discussion and ideas. You will hear a range of thought-provoking talks about different aspects of using AI technologies in the area of materials discovery, and have the opportunity to both discuss this subject area with other members of the workshop and address questions to the speakers. Members of the Network Executive Group will also be in attendance so you will be able to find out more about our Network and the opportunities we have available including funding opportunities and the types of events we will be running (e.g. workshops, conferences and hackathons). 

3. What are the aims of the workshop? 
This workshop is aiming to help the Network+ to drive progress in this area and facilitate collaboration by introducing people to make new interdisciplinary teams, and to produce new grant applications. To achieve this we may commission literature reviews, papers, or small scale investigations to test out new ideas. We welcome ideas and suggestions about how to go forward in this area and how best to achieve our aims. 

4. What are the main themes of the workshop? 
Materials, Computational chemistry, Novel Mathematics, AI.

06/03/2019 - AI3SD & MDC AI in Drug Discovery and Drug Safety Workshop - Medicines Discovery Catapult, Cheshire

posted 3 Jan 2019, 07:58 by Samantha Kanza   [ updated 5 Jul 2019, 05:43 ]

Eventbrite Link: https://www.eventbrite.co.uk/e/ai-in-drug-discovery-and-drug-safety-workshop-tickets-51705581787

Description: 
This is event is brought to you by AI3SD and Medicines Discovery CatapultDrug discovery is a complex and long-term scientific investigation involving interdisciplinary research methods coupled with large heterogeneous datasets. The research and data space in this area is vast, and we believe that the use of AI and machine learning technologies can help spur on advances in this domain. This workshop has been designed to draw together those with a keen interest in using AI and machine learning technologies in the domain of drug discovery, both to aid future drug discovery, and to help improve drug safety. At AI3SD we firmly believe that interdisciplinary collaboration is the key to many of these advances, and so welcome anyone working in the technical or scientific ends of this domain, as well as those already working in an interdisciplinary fashion. There will be keynote talks interspersed with general group discussions, and working groups around the key topics that arise. There will also be an opportunity to tour the labs at Medicines Discovery Catapult. Lunch will be provided and there will be plenty of time for networking, and the day will conclude with a prosecco reception.

Keynote Speakers:
Alongside the discussions and lab tours there will some keynote talks about different aspects of drug discovery: 
  • Professor John Overington, Chief Informatics Officer, Medicines Discovery Catapult. John leads the development and application of informatics approaches to promote and support innovative, fast-to-patient drug discovery in the UK through collaborative projects across the applied R&D community.
  • Professor Val Gillet - Val is a Professor of Chemoinformatics at the University of Sheffield where she heads the Chemoinformatics Research Group. Her research focuses on the development and validation of chemoinformatics methods, especially for drug discovery. She has expertise in machine learning, evolutionary algorithms and the development of novel methods for molecular representation and applying these to applications such as de novo design and virtual screening. She has collaborated with many of the major pharmaceutical companies and specialist chemcoinformatics software companies.
  • Dr Willem van Hoorn, Chief Decision Scientist, ExScientia. Willem gained a PhD in computational chemistry in the group of David Reinhoudt at the University of Twente followed by a postdoc at Yale. He subsequently spent a decade at Pfizer focusing on computational techniques for HTS triage and combinatorial library design. This was followed by a position as senior solutions consultant at Accelrys assisting a range of clients from small biotech to big pharma.
  • Dr Nicola Richmond – Nicola is the Director of Artificial Intelligence and Machine Learning at GlaxoSmithKline. Her research focuses on discovering innovative ways of deriving insights from data to advance drug discovery and development.
Keynote Abstracts:
  • Using Machine Learning to Drive Reaction Based De Novo Design - Professor Val Gillet: The de novo design of novel drug candidate has been a topic of considerable interest since the 1990s. The main challenges in de novo design arise from the astronomical number of drug-like molecules that could exist and the difficulties associated with designing scoring functions to navigate this space. The recent resurgence of interest in de novo design can be attributed to the application of deep learning methods that typically operate on SMILES strings. While these approaches have been shown to be effective in generating valid SMILES, they are limited in the extent to which they can account for synthetically accessibility. We have been working on reaction-based de novo design for a number of years. Our approach takes explicit account of synthetic accessibility since the transformations that are applied to molecules are based on rules derived from real reactions. The rules are encoded as reaction vectors and are derived automatically from reaction databases. The availability of large public datasets of reactions provides a rich source of reactions for synthetically accessible de novo design. Here we will describe how we are using machine learning to select the most promising reactions for reaction-based de novo design. 
  • Re-energising Small Molecule Drug Discovery – Dr Willem van HoornThe optimisation trajectory of hit to lead to candidate is the most expensive part of drug discovery. Exscientia’s drug discovery platform brings that cost down significantly by combining the strengths of AI compound design and human strategic thinking into the Centaur ChemistTM. A high level overview of the technology is presented and results are shown from successful collaborations that resulted in the delivery of clinical candidates in less than a year.
  • Understanding the holes in the metabolome - Dr Nicola Richmond: The metabolome refers to the complete set of both endogenous and exogenous small molecule metabolites that are either produced naturally as a bi-product of a biological process or as a result of the external environment. Quantifying changes in the metabolome can help diagnose disease, understand disease mechanisms, identify novel drug targets and understand drug safety and efficacy. As such, metabolomics is now widely used in the pharmaceutical industry throughout the drug discovery and development process. The annotated human metabolome now stands at over 350K metabolites and 25K pathways. It is therefore unsurprising that analysing metabolomics data presents a major challenge. The current gold standard approach is highly subjective and does not account for pathway-level, structural information. Hypotheses tend to be established a priori and validated through manual navigation of data rather than letting the data speak. At GSK, we have established a fully automated, data-driven approach to analysing metabolomics data using concepts from topological data analyses. Our analysis pipeline provides bench scientists with an automated approach for validating their hypotheses, allows data scientist, with no understanding of biology, to generate meaningful hypotheses and potentially fills gaps in our understanding of the metabolite.
Programme: 
  • 10:00 - 10:30: Coffee & Registration
  • 10:30 - 10:45: Introduction with Professor John Overington
  • 10:45 - 11:15: Using Machine Learning to Drive Reaction Based De Novo Design - Professor Val Gillet
  • 11:15 - 12:30: Initial Discussions
  • 12:30 - 13:00: Lunch
  • 13:00 - 13:30: Re-energising Small Molecule Drug Discovery – Dr Willem van Hoorn
  • 13:30 - 15:00: Working Group Discussions and Lab Tours
  • 15:00 - 15:15: Coffee
  • 15:15 - 15:45: Report and Action Plan
  • 15:45 - 16:15: Understanding the holes in the metabolome - Dr Nicola Richmond
  • 16:15 - 17:30: Prosecco reception
FAQS:

1. Who should attend?
Anyone with an interest in Drug Discovery and Drug Safety, and Artificial Intelligence, Machine Learning or Data Science and how these methods can be applied to Drug Discovery/Safety. We welcome members from academia, industry and government. We are always looking to grow our Network+ and bring in people with a wealth of experience in the many different subject areas that are needed so that we can form interdisciplinary partnerships and work together to further the field of Scientific Discovery.

2. What will I get out of it?
You will be able to network with likeminded people who have research interests that complement yours. You will hear a range of thought-provoking talks about different aspects of using AI and Machine Learning in Drug Discovery and Drug Safety. There will also be plenty of time to network and discuss this subject area with other members of the workshop, in addition to being able to take a tour of the Medicines Discovery Catapult labs. Members of the Network Executive Group and Advisory Board will also be in attendance so you will be able to find out more about our Network and the opportunities we have available including funding opportunities and the types of events we will be running (e.g. workshops, conferences and hackathons).

3. What are the aims of the workshop?
This workshop is aiming to help AI3SD and MDC drive progress in this area and facilitate collaboration by introducing people to make new interdisciplinary teams, and to produce new grant applications. To achieve this we may commission literature reviews, papers, or small scale investigations to test out new ideas. We welcome ideas and suggestions about how to go forward in this area and how best to achieve our aims.

06/02/2019 - AI3SD Molecules Graphs & AI Workshop - Ageas Bowl, Southampton

posted 3 Jan 2019, 07:56 by Samantha Kanza   [ updated 5 Jul 2019, 05:35 ]

Eventbrite Link: https://www.eventbrite.co.uk/e/molecules-graphs-ai-workshop-tickets-55016184890 

Description: 
The representation of molecules as connected graphs and the application of graph theory has been very useful in defining aspects of molecular structure, molecular energy levels and identifying unique topology features. In the workshop we seek to explore the ways in which molecular graphs can be used to drive property and other predictions using Machine Learning and other AI techniques. All ideas welcome – come and discuss and debate and come up with new plans! There will be two keynote talks, and a chance for participants to present their areas of research and ideas. Lunch will be provided, and the day will end with networking drinks.

Keynote Speakers
There will be three keynote talks from experts in this field.
  • Professor Mahesan Niranjan - Professor of Electronics and Computer Science at the University of Southampton & AI3SD Co-Investigator. Nirajnan works in the area of machine learning, and his research interests are in the algorithmic and applied aspects of the subject. He has worked on a range of applications of machine learning and neural networks including speech and language processing, computer vision and computational finance. Currently, the major focus of his research is in computational biology. Some of his work (e.g. the SARSA algorithm in Reinforcement Learning) have been fairly influential in the field. He has held several research grants from the Research Councils in the UK, and the European Union. Currently, his main focus is on architectures and algorithms for Deep Learning and inference problems that arise in computational biology.
  • Professor Sophia Yaliraki - Sophia is a Professor of Theorerical Chemistry at Imperial College London. Sophia is a Professor of Theorerical Chemistry at Imperial College London. Her research interests are in the theory of self-assembly in biology and molecular electronic devices and coarse-graining and model reduction techniques in dynamics.
  • Professor Patrick Fowler - Patrick has been a Professor of Theoretical Chemistry at the University of Sheffield since 2005. Prior to that he had worked at both the University of Durham as a Senior Demonstrator, the University of Cambridge as a Postdoctoral Research Fellow and the University of Exeter where he became a professor. His research focuses on molecular properties, ring currents, aromaticity, fullerenes, molecular electronic devices, symmetry and discrete mathematics in chemistry, and he has published extensively in these areas. He was also elected a Fellow of the Royal Society in 2012.
Keynote Abstracts
  • Inference from Outliers – Professor Mahesan Niranjan: Classic machine learning is largely about classification and regression problems. However, many practical problems of interest in genomics, condition monitoring, medical diagnostics and security are better posed as problems of detecting novelty. In this talk, I will describe two applications of extracting useful information from novel data, in problems relating to modelling cellular protein concentrations and the solubility of synthetic chemical molecules. The algorithmic framework poses a robust support vector regression problem and the resulting non-convex optimisation problem is solved using a difference-of-convex formalism. (Part of this work is supported by grant EP/N014189/1, "Joining the Dots: From Data to Insight" from the EPSRC).
  • Unsupervised, multiscale learning through atomistic graphs: From molecules to systems - Professor Sophia Yaliraki: We have derived an all-scale graph partitioning approach that preserves atomistic physico-chemical detail and by using diffusive processes on the graph (both on the node and the edge space), we have shown that we can obtain the behaviour of biomolecules and biomolecular assemblies at different timescales without the need of any reparametrisation or a priori selection of relevant timescales. The approach is computationally efficient and general and can be applied to molecules, molecular assemblies as well as data. We will showcase the theory with examples from predictions and experimental verification of mutations that control protein dynamics at different scales (AdK), prediction of allosteric sites for drug design and communication and signalling in multimers and assemblies (ATCase, Rubisco). Finally, the application of this unsupervised learning approach to trajectories and free text will be briefly discussed.
  • Source-and-sink models for molecular conduction - Professor Patrick Fowler: This talk describes recent progress in Sheffield in describing ballistic molecular conduction with the Ernzerhof source-and-sink-potential (SSP) model. SSP gives a broad classification of conduction behaviour at the graph theoretical level. We have been able to derive selection rules, classifications and intuitive descriptions that remain useful at higher levels of theory. This talk is based on joint work with Barry Pickup (Sheffield), Irene Sciriha (Malta) and Martha Borg (Sheffield).
Programme: 
  • 10:00-10:30: Coffee & Registration 
  • 10:30-10:45: Welcome Introduction – Professor Jeremy Frey 
  • 10:45-11:15: Unsupervised, multiscale learning through atomistic graphs: From molecules to systems - Professor Sophia Yaliraki
  • 11:15-12:00: Inference from Outliers - Professor Mahesan Niranjan  Presentations from Participants to initiate discussions
  • 12:00-12:45: Presentations from Participants to initiate discussions
  • 12:45-13:45: Lunch 
  • 13:45-14:15: Source-and-sink models for molecular conduction - Professor Patrick Fowler
  • 14:15-14:45: Initial Discussions to form Working Group Topics 
  • 14:45-15:15: Coffee 
  • 15:15-16:30: Working Group Discussions 
  • 16:30-17:00: Report back and form Action Plan 
  • 17:00-18:00: Drinks reception
FAQs:

1. Who should attend? 
Anyone with an interest in Molecules, Graphs, Artificial Intelligence, Machine Learning, Deep Learning. We welcome members from academia, industry and government. We are always looking to grow our Network+ and bring in people with a wealth of experience in the many different subject areas that are needed so that we can form interdisciplinary partnerships and work together to further the field of Scientific Discovery.  

2. What will I get out of it? 
You will be able to network with likeminded people who have research interests that complement yours. There will be two keynotes around the topics of Molecules, Graphs and AI to spark discussion and ideas. There will be an opportunity to present your own research interests/areas of expertise briefly, with plenty of opportunity to have general discussions and some specific topic based discussions in smaller groups. Members of the Network Executive Group will also be in attendance so you will be able to find out more about our Network and the opportunities we have available including funding opportunities and the types of events we will be running (e.g. workshops, conferences and hackathons).  

3. What are the aims of the workshop? 
This workshop is aiming to help the Network+ to drive progress in this area and facilitate collaboration by introducing people to make new interdisciplinary teams, and to produce new grant applications. To achieve this we may commission literature reviews, papers, or small scale investigations to test out new ideas. We welcome ideas and suggestions about how to go forward in this area and how best to achieve our aims.  

4. What are the main themes of the workshop? 
Molecules are often represented by connected graphs showing in some sense the bonding between the atoms.  These graphs can serve as the input in the quantum chemistry packages to determine the 3D molecular geometry and with high level and time-consuming calculations obtain the electron density and electric fields surrounding the molecules and with even greater difficulty the interactions between molecules obtained and used as the basis for simulation for collections of molecules.  In this workshop we wish to explore how the graphs themselves can be used to generate molecular properties, predict drug activity, suggest crystal structures, without going through the QM route.  Can the graphs be the input to Machine Learning models?  What fundamental properties of the graphs directly relate to molecular behaviour? How do these relate to the topology of the systems?  Can we predict drug solubility, drug binding or the nature of molecular assemblies and crystals structures?  For example graph theory predictions of Huckel Molecular orbital energies give very fundamental predictions about the nature of some molecules. The graph approach is very useful when considering similarity between molecules and the transformations between molecules  and we seek to explore how AAI can play a role in these areas too.

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