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

posted 8 Oct 2019, 03:57 by Samantha Kanza   [ updated 19 Nov 2019, 08:24 ]





Description: 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.



Speakers:
Plenary Lectures include:
Invited Lectures include:



Oral and poster contributions are invited.
  • Oral Contribution Submission Form. Deadline: 31/10/2019. Notification of Acceptance: 02/12/2019.
  • Poster Abstract Submission Form. Deadline: 15/01/2020. Notification of Acceptance: 31/01/2020. 
The poster session has been generously sponsored by IKTOS!
  • There will be a £300 poster prize for best poster
  • The top five posters will be rewarded with expert accounts on Spaya, IKTOS's fully data-driven retrosynthetic tool which will soon be launched online.



Event Details:
  • Location: DeVere, Tortworth Hotel, Gloucestershire 
  • Time: This 3-day residential event runs from 10:30 on Monday 9th March through to 4pm on Wednesday 11th March. Registration includes 2 nights accommodation and all meals at the lovely De Vere Tortworth Court Hotel.



Registration fees:
  • Early Bird tickets (to 31st October)  Academia and SMEs £100; Industry Delegates £150
  • General Admission (from 1st Nov)  Academia and SMEs £200; Industry Delegates £300
To secure your place, register now! More information on the event will be available here.



Exhibitors & Sponsors:

This event is sponsored by:

              .       . 

If you are interested in exhibiting/sponsoring this event please contact us.

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

posted 13 Aug 2019, 02:39 by Samantha Kanza   [ updated 14 Nov 2019, 07:10 ]

Paperless Content can be found here: Paperless Content Page 


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 or a talk please fill in our AI3SD 2019 Poster Submission Form or our AI3SD 2019 Abstract Submission Form. The deadline for oral submissions is the 6th October at Midnight, and for posters the 1st November at midnight and we will send out notification of the accepted talks by the 7th October, and the accepted posters by the 2nd November.  

Abstracts & Speaker Bios:

Programme: 
Please see our Agenda Page

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 even if this is after the 7th October. 

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.

06/11/2019 - AI3SD Seminar: Quantum Computers: a guide for the perplexed - University of Southampton

posted 26 Feb 2019, 08:31 by Samantha Kanza   [ updated 8 Oct 2019, 04:11 ]


Description: 
Andy will introduce the mind-bending principles of quantum computing, give some history of the technology, and describe potential application areas for quantum computers. He will take us on tour inside a real quantum computer, and explain how you can get free hands-on experience of IBM's quantum computer, and start to learn how to program these exciting new machines.

Abstract: 
We experience the benefits of classical computing every day. However, there are challenges that today’s systems will never be able to solve. For problems above a certain size and complexity, we don’t have enough computational power on Earth to tackle them. To stand a chance at solving some of these problems, we need a new kind of computing.  Quantum computers could spur the development of new breakthroughs in science: Medications to save lives, machine learning methods to diagnose illnesses sooner, materials to make more efficient devices and structures, financial strategies to live well in retirement, and algorithms to quickly direct resources such as ambulances. IBM Q is the world's most advanced quantum computing initiative, focused on propelling the science and pioneering commercial applications for quantum advantage. An industry first initiative to build universal quantum computers for business, engineering and science. This effort includes advancing the entire quantum computing technology stack and exploring applications to make quantum broadly usable and accessible. 

Biography:
Andy Stanford-Clark is the Chief Technology Officer for IBM in UK and Ireland. He is an IBM Distinguished Engineer and Master Inventor with more than 40 patents. Andy is based at IBM's Hursley Park laboratories in the UK, and has a long background in Internet of Things technologies. He has a BSc in Computing and Mathematics, and a PhD in Computer Science. He is a Visiting Professor at the University of Newcastle, an Honorary Professor at the University of East Anglia, an Adjunct Professor at the University of Southampton, and a Fellow of the British Computer Society.

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 Oct 2019, 03:45 ]

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 five 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 
  • 10:45-11:15: Smart Cleaning & Robots in Factories - Dr Nicholas Watson
  • 11:15-11:45: Food Sensor Technology - Dr Martin Peacock
  • 11:45-12:15: What do we mean by Artificial Intelligence? And how does it apply to Allergen Detection? - Professor Jeremy Frey 
  • 12:15-13:15: Lunch
  • 13:15-14:30: 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 five 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

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.

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