22/07/2020 - AI3SD Online Seminar Series: Design Fiction as a method and why we might use it to consider AI - Dr Naomi Jacobs

posted 29 Jun 2020, 01:58 by Samantha Kanza   [ updated 29 Jun 2020, 04:04 ]

This seminar forms part of the AI3SD Online Seminar Series that will run across the summer. Please sign up to register for this event, and the weblink for the seminar will be sent to you the day before the event. A recording of this seminar will be made available afterwards to AI3SD members. 

AI is a fast moving field that is rapidly advancing and becoming embedded in a multitude of sectors and applications. With such a fast pace, and excitement over the possibilities it allows, there is often a rush to get things going. This being the case, sometimes not enough time is spent considering the implications and unforeseen outcomes that might come from the introduction of new technologies, processes and practices. Ideas that seem plausible and useful can turn out to be problematic when actually implemented, by which time it is often too late. By using the methodology of speculative design, we can more closely examine these implications and outcomes before the technologies become a reality. This talk will introduce speculative design and give some examples of design fiction, a method wherein objects from fictional futures or alternate presents are created to provoke discussion and explore possibilities.

Naomi is Lecturer in Design Policy and Futures Thinking at Imagination Lancaster, the design-led interdisciplinary research group at Lancaster University. Her work crosses various disciplines including design, computer science, and social science. Naomi’s previous work has focused primarily on interaction; between individuals, communities, disciplines or sectors, and between people and technology and the media they consume. Naomi is particularly interested in the intersection between the digital and the physical, and how this impacts society on many axes. She has been part of a number of research projects looking at how technologies such as IoT and AI are being implemented and governed, and is interested in how design can be used to shape policy for new technologies. In particular, her current work focuses on issues such as trust, transparency, privacy and bias, using speculative design methods to explore implications of technology currently in development or proposed for the future.  

15/07/2020 - AI3SD Online Seminar Series: InChI: measuring the molecules – Professor Jonathan Goodman

posted 22 Jun 2020, 07:23 by Samantha Kanza   [ updated 22 Jun 2020, 08:14 ]

This seminar forms part of the AI3SD Online Seminar Series that will run across the summer. Please sign up to register for this event, and the weblink for the seminar will be sent to you the day before the event. A recording of this seminar will be made available afterwards to AI3SD members. 

The IUPAC international chemical identifier, InChI, provides a way to name molecules. It is defined by an open algorithm that transforms molecular structures into unique strings of text. Each molecule should have exactly one InChI, and each InChI should correspond to exactly one molecule. This property makes it a useful tool in the management of chemical information, and it is widely used. The InChI Trust and IUPAC are continuing to work on developing the standard and on creating new tools which are built on the InChI. This talk will outline how the InChI is used now, and how this may develop in the future. 

Jonathan is a Professor of Chemistry at the University of Cambridge, and Director of Studies of Chemistry at Clare College, where he also serves as the Academic Dean. His research focuses on experimental and computational chemistry, analysing organic reaction mechanisms, interpreting analytical data and investigating computational chemical toxicology. He is also secretary of the Subcommittee on the IUPAC International Chemical Identifier, and has developed the Reaction-InChI (RInChI): an InChI-based identifier for chemical reactions.

07/07/2020 - AI4Good @ WebSci20

posted 18 Jun 2020, 03:17 by Samantha Kanza   [ updated 22 Jun 2020, 03:57 ]

This year the AI3SD Network+ (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) will be running a workshop at the WebSci20 Conference in Southampton, UK. Artificial and Augmented Intelligence systems have the potential to make a real difference in the scientific discovery domain however this brings a new wealth of ethical and societal implications to consider with regards to this research (e.g. human enhancement, algorithmic biases, risk of detriment). This workshop looks to explore the ethical and societal issues centered around using intelligent technologies (Artificial Intelligence, Augmented Intelligence, Machine Learning, and in general Semantic Web Knowledge Technologies) to further scientific discovery, with a strong consideration of data ethics and algorithmic accountability. Advances in technology and software are rarely inherently bad in themselves, however that unfortunately does not preclude them from being subverted to ill intent by others; furthermore, as demonstrated by the examples above, even an unintentional lack of care towards ethical codes and algorithmic accountability can lead to societal and ethical implications of scientific discovery. It is our responsibility as researchers to consider these issues in our research; are we conducting studies ethically? What ethical codes can we put in place for scientific discovery research to mitigate against ethical and societal issues. These are really important issues, and they require an interdisciplinary focus between scientists, social scientists and technical experts in order to be comprehensively addressed. 

Key Dates:
All deadlines are at Midnight UK Time. 

  • This workshop will take place at the WebSci20 Conference at the University of Southampton. The main conference sessions will take place in the prestigious new Centenary Building (Building 100) which was recently completed to celebrate 100 years of teaching at Highfield Campus.  
  • Due to COVID-19, this workshop will now take place virtually. Further details will be available soon. 

Programme Committee:
We are part of AI3SD: The Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery Network, and as such we are keenly interested in research that applies AI technologies to science. However, we are aware that this process has many ethical and societal implications, and want to ensure that these areas are fully investigated and considered so that research in the AI for Scientific Discovery Research can be conducted in an ethical and responsible fashion. 

01/07/2020 - AI3SD Online Seminar Series: Drug Repositioning for COVID-19 - Professor John Overington

posted 27 Mar 2020, 03:34 by Samantha Kanza   [ updated 30 Jun 2020, 01:58 ]

This seminar forms part of the AI3SD Online Seminar Series that will run across the summer. Please sign up to register for this event, and the weblink for the seminar will be sent to you the day before the event. A recording of this seminar will be made available afterwards to AI3SD members. 

Pandemics, such as Covid-19. are by definition essentially unanticipatable and rapid onset. Features unfortunately incompatible with current industry capabilities in drug discovery. This has led to a large number of studies, both theoretical and experimental to reposition, or reuse an existing drug for Covid-19 therapy. There are some general patterns of success in historical repositioning that point to the most likely strategies for drug repositioning, and also, following some specific data gathering and curation, to point towards specific actionable activities for Covid-19. The presentation will briefly overview drug repositioning as a general strategy, and then the focussed application of core concepts towards the treatment of Covid-19. 

John has had extensive experience in technology driven drug discovery. In his work as CIO at the Medicine Discovery Catapult he leads research projects for developing and applying informatics-based approaches for drug discovery. Prior to this he worked for Benevolent AI where he was involved in the development of novel data extraction and integration strategies, integrating deep learning and other Artificial Intelligence approaches to drug target validation and drug optimisation. 

Interview: Dr Wendy Warr interviewed John prior to this seminar. This interview can be found here: https://eprints.soton.ac.uk/441804/

***Postponed*** 06/05/2020 - AI3SD Seminar: First Principles and Machine Learning Calculations of Aqueous Solubility - University of Southampton

posted 19 Feb 2020, 10:14 by Samantha Kanza   [ updated 23 Apr 2020, 08:13 ]

When: Wednesday 6th May

Where: Building 29/1101, University of Southampton 

Dr John Mitchell, EaStCHEM School of Chemistry, University of St Andrews

Bio: John Mitchell obtained his PhD in Theoretical Chemistry from Cambridge, studying the energetics of hydrogen bonding with Prof. Sally Price. He then worked with Prof. Janet Thornton at University College London, applying computational chemistry to the growing field of structural bioinformatics. He returned to Cambridge in 2000, taking up a lectureship in Chemistry. He was appointed to a readership at St Andrews in 2009. His research uses theoretical and machine learning techniques in pharmaceutical chemistry, condensed phase modelling, and structural bioinformatics. His group have worked extensively on prediction of bioactivity, solubility, melting point and hydrophobicity from chemical structure, using both informatics and theoretical chemistry methodologies. Recently they have developed novel applications of machine learning in computational biochemistry, such as drug side effect prediction, identifying athletic performance enhancers, and competing against a panel of human experts to predict solubility accurately.

However simple it may appear, predicting how much of a substance will dissolve in water or other solvents turns out to be both an important and a difficult problem. Medicines need to be soluble in environments throughout the body, including the stomach and the bloodstream, in order to get to the sites where they are designed to act. The difficulty of designing medicines that are both effective and soluble continues to deny or delay treatments that patients need, as well as costing hundreds of millions of pounds in wasted research effort. Beyond pharmaceuticals, developing safer pesticides depends on understanding their solubility in wet soil or rivers, in order to know their environmental impact. Furthermore, design of new compounds for high-tech uses and separation of mirror-image molecules would be greatly facilitated by the ability to calculate and predict solubility accurately.  
There are two philosophically very different approaches to computing solubility, which will be discussed and compared throughout this seminar. One is to look towards first principles theoretical chemistry. A major advantage of using theoretically rigorous quantitative modelling of both crystals and solutions, typically derived from quantum mechanics, is that the methods can be rigorously adapted to give the solubility for other solvents, to account for the presence of other molecules, and to vary the predictions with changes of temperature. Using the best available computational chemistry models, often borrowed from crystal structure prediction or condensed phase physics, also allows us to benchmark, adapt, and systematically improve the methodology to approach the chemical accuracy that will be required if first principles models are to acquire mainstream utility in solubility science.
The alternative is to use an informatics-based approach, a field once described as QSPR but now more likely to be badged Machine Learning or Artificial Intelligence. Informatics methods are designed with the sole objective of accurate numerical prediction. We seek simply to link our inputs – representations of molecular structures, to the outputs – accurate predictions of experimental solubility, with no requirement for the model to utilise real-world chemistry or physics. Any interpretability or mechanistic insight from the model is a secondary consideration. Currently, Machine Learning techniques such as Support Vector Machine or Random Forest offer solubility predictions more numerically accurate and orders of magnitude faster than first principles. 

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

posted 28 Jan 2020, 08:14 by Samantha Kanza   [ updated 24 Feb 2020, 02:30 ]

Paperless Content can be found here: Paperless Content Page 

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.

Abstracts & Speaker Bios:

Please see our Agenda Page

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.

*We are now operating a waiting list. Please get in touch with dialamol@soton.ac.uk

Exhibitors & Sponsors:

Our drinks reception, and Professor Peter Johnson's talk is exclusively sponsored by: 

Our poster prizes are sponsored by:

This event is sponsored by:
   .      .

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

*** Postponed *** 04/03/2019 - AI3SD Seminar: Closed-loop materials research for solid-state batteries - University of Southampton

posted 10 Jan 2020, 06:50 by Samantha Kanza   [ updated 23 Apr 2020, 08:13 ]

NB: This event has been postponed until further notice. 

When: Wednesday 4th March, 10:00 - 11:00 

Where: Building 29/1101, University of Southampton 

Taro Hitosugi (Professor, Tokyo Institute of Technology)

Education: 1999 Ph.D. Graduate School of Engineering, The University of Tokyo 
Professional Career: 2015 - present 2007 - 2015 2003 - 2007 1999 - 2003
Professor, Tokyo Institute of Technology Associate Professor, Tohoku University Assistant Professor, The University of Tokyo Sony Corporation
Interests: electrochemistry, solid-state ionics, surface/interface, thin films, heterointerfaces, oxides, hydrides, scanning probe microscopy, materials informatics, artificial intelligence (AI), Bayes optimization, robot

Solid-state lithium batteries are promising candidates for the next-generation rechargeable batteries with high energy and power density. Such high-performance batteries require the discovery of new solid-electrode and solid-electrolyte materials. Thus, a high-throughput methodology for rapid selection and development of new inorganic materials becomes crucial.
To this end, the integration of knowledge, experience, and intuition of researchers using robotics and artificial intelligence (AI) can accelerate progress in materials research [1]. Strategies combining high-throughput synthesis with machine learning have already produced new small organic molecules and biomaterials at ever faster rates [2, 3, 4]. However, the application of these techniques to inorganic materials research is still at its infancy. Therefore, to drastically accelerate its development, the inclusion of AI and robotics into inorganic materials research is essential.
In this study, we demonstrate the autonomous synthesis of inorganic compounds using robotics and Bayesian optimization. This system fully automates sample transfer, thin film deposition, physical-property characterization, and growth condition optimization. Our apparatus is also equipped with a robot arm that can access each satellite chamber for growth and characterization. Based on the data obtained from the physical-property characterization, the Bayesian-optimization algorithm is used to predict the next growth condition (closed-loop process). In this talk, we showcase this closed-loop process in reducing the resistivity of Nb-doped TiO2, a negative electrode material [5]. We also show that this autonomous synthesis can be applied to a wide variety of functional inorganic materials including magnetic, optical, electronic, and ionic properties.

[1] R. F. Service, Science 366, 1295 (2019). [2] M. Peplow, Nature 512, 20 (2014).
[3] R. D. King et al., Science 324, 85 (2009). [4] J. M. Granda et al., Nature 559, 377 (2018). [5] T. Hitosugi et al., Phys. Status Solidi A 207, 1529 (2010).

31/01/2020 - AI3SD, OSM & RSC-CICAG: AI and ML in Drug Discovery: Predicting Bioactive Molecules when there is No Target

posted 8 Oct 2019, 03:57 by Samantha Kanza   [ updated 3 Feb 2020, 06:28 ]

This one-day meeting concerns the application of machine learning/artificial intelligence (ML/AI) approaches to the discovery of new drug leads. Specifically the meeting is about cases where the biological target is not clearly established - so-called phenotypic drug discovery.

The meeting centers on a real example - a competition run by Open Source Malaria (OSM), funded by a grant from the EPSRC/AI3SD+ Network. Data on active and inactive compounds in one OSM antimalarial series were published online, and anyone was able to submit a model able to predict the actives. The models were judged against a dataset that was kept private, and the winners were asked to use their models to predict novel molecules. These are currently being made in the lab and biologically evaluated, and the results will be reported at the meeting, providing a real-world test, and a complete case study, of the capabilities of ML/AI approaches to accelerate modern drug discovery.

We will hear from some of the eleven competition entrants about how their models were constructed, and will have other presentations on related developments. We hope during this meeting to establish which approaches worked well, which did not, and why. All those interested in the application of ML/AI methods to drug discovery are encouraged to attend.

The meeting is free, but there will be a cap on numbers, meaning first come first served, meaning registration is essential. Lunch will be provided as part of this event.

The agenda for the day is as follows:
1. Who should attend? 
All academic and industrial research scientists with an interest in the application of AI/ML approaches to drug discovery.

2. What will I get out of it? 
We will discuss a real case study in which new AI/ML methods have been applied to a currently-active drug discovery project, and where the predictions made have been synthesised in the laboratory and biologically evaluated.

3. What are the aims of the workshop? 
The meeting is a post-mortem of a real research competition carried out as part of a public domain drug discovery project, but we will also discuss the potential of AI/ML methods more generally. It is hoped that new collaborations may emerge between meeting attendees.

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 

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:

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. 

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. 

  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 ]

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.

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. 

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.

1-10 of 20