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

posted 6 Jul 2020, 02:20 by Samantha Kanza   [ updated 6 Jul 2020, 02:20 ]

Interview: Dr Wendy Warr interviewed John prior to this seminar. This interview can be found here:

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. 

9-11/03/2020: AIReact2020

posted 22 Jun 2020, 04:06 by Samantha Kanza   [ updated 22 Jun 2020, 09:46 ]

In March 2020, right before our world got turned upside down and we went into lockdown, we managed to hold our big joint Network Conference: AIReact2020 in the beautiful setting of the DeVere Tortworth Court Hotel in Gloucester. This was a joint meeting between the AI3SD, Dial-a-Molecule, and Directed Assembly Networks. 

A full report on this event has been written by the wonderful Dr Wendy Warr! This report can be downloaded from here. [LINK COMING SOON]

A majority of the presentations given at this event, and all of the posters can be found online on our Paperless Content Page.

The meeting was designed to 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 were illustrated including expert systems, statistical methods, mechanism based and Machine Learning. It was a wonderful event, and despite the chaos going on it was a very well attended meeting! Several of our speakers were unable to attend due to travel restrictions, but were fortunately able to present to us virtually, for which we were very grateful. 

As part of the event, we had a wonderful drinks reception sponsored by CAS SciFinder, and a poster session, for which the prizes were sponsored by IKTOS. The full list of posters can be found here. The winners of the Poster Prizes were:

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

posted 3 Feb 2020, 06:46 by Samantha Kanza   [ updated 17 Mar 2020, 03:59 ]

Back in January 2019 AI3SD announced their first funding call. We funded 3 pilot projects, and one of the successful applicants of this funding call was Professor Mat Todd from UCL, with the project entitled "Predicting the Activity of Drug Candidates where there is No Target". More information can be found in the Interim Report of this project. The final report is coming soon. 

A full report on this event was written by Dr Chris Swain and can be downloaded from here

Project Dates: 01/07/2019 - 28/02/2020. 
Industrial Partners: Dr. Mykola Galushka (Auromind), Dr Willem Van Hoorn (ExScientia) and Dr Tom Whitehead (Intellegens)

The discovery of new antimalarial medicines with novel mechanisms of action is key to combating the increasing reports of resistance to our frontline treatments. The Open Source Malaria (OSM) consortium have been developing compounds (”Series 4”) which possess potent activity against Plasmodium falciparum in vitro and in vivo and have been suggested to act through the inhibition of PfATP4, an essential ion pump in the parasite membrane that regulates intracellular Na+ and H+ concentrations. This pump has not yet been crystallised, so in the absence of structural information about this target, a public competition was created to develop a model that would allow us to predict when compounds in Series 4 are likely to be active. In the first round in 2016, six participants used the open data collated by OSM to develop moderately predictive models using diverse methods. Notably all submitted models were available to all other participants in real time. Since then further bioactivity data have been acquired and machine learning methods have rapidly developed, so a second round of the competition was performed, with 10 models submitted. The best-performing models from this second round are being used to predict novel analogs in Series 4 that will be synthesised and evaluated against the parasite. As such the project will openly demonstrate the abilities of new machine learning algorithms in the prediction of active compounds where there is no confirmed target, frequently the central problem in phenotypic drug discovery.

This meeting was to conclude and present the results of this competition. 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 were 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. Presentations were given by some of the eleven competition entrants about how their models were constructed, and other presentations were given on related developments. 
The presentations given are listed below:

06/11/2019 - Quantum Computers: a guide for the perplexed

posted 15 Jan 2020, 07:34 by Samantha Kanza

On the 6th November Professor Andy Stanford-Clark gave a fascinating seminar on Quantum Computers. 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. He is also a vital member of the AI3SD Advisory Board!

Andy's talk introduced the mind-bending principles of quantum computing, give some history of the technology, and described potential application areas for quantum computers (notably including those relevant to AI3SD). 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. Whilst he was unable to leave his full slide deck with us, he has provided some useful links for anyone interested in leaning more about quantum computing. These links can be found here.

17/10/2019 - AI3SD & IoFT: AI for Allergen Detection and Smart Cleaning within Food Production

posted 15 Jan 2020, 05:29 by Samantha Kanza   [ updated 10 Jun 2020, 02:39 ]

On the 17th October 2019 AI3SD, IoFT (The Internet of Food Things Network+) and members of the Food Water and Waste Research Group at the University of Nottingham teamed up to host an event on AI for Allergen Detection and Smart Cleaning within Food Production. Here is a blog post written by Michelle Pauli about the event. You can find full coverage of the presentations and working group discussions in the main report, also written by Michelle Pauli which can be found here

Another blog post written about this event by Dr Nicholas Watson from the University of Nottingham can be found here, and Nicholas's report on the event and other related activities as part of their cleaning projects can be found here.

Can AI solve the food allergen challenge? - Michelle Pauli

Ever wondered how you quickly measure the fieriness of Tabasco, the ‘gingerness’ of ginger or the pungency of garlic? It might seem like little more than a fun party trick but what if you could apply the same low-cost techniques to detect e-coli in food, with the goal of doing so in minutes rather than the current need to send food off to labs for three days of testing? How might such breakthroughs be applied to detecting allergens in food during production – a life and death matter for those affected? This may be a relatively straightforward with liquid foods – drinks and sauces – and foods that can be put in aqueous solutions for testing but how do you test for the presence of a single sesame seed in a whole batch of bread loaves?

Detecting, responding to and alerting consumers to allergens in food production is a huge challenge for the food industry. According to the Food Standards Agency in 2018, the number of food safety events relating to allergens more than doubled between 2014 and 2017, despite the increasing awareness of allergies and their triggers. And, as allergies increase, so too do the difficulties of managing the food production process for allergens. Cleaning production lines – both the visible parts and those hidden away in the engineering – is more essential than ever but is also increasingly time-consuming and expensive, not least with our ever-burgeoning demand for different varieties of food: where once a production line might be dedicated to a long run of a single type of bread, it now has to accommodate low-gluten bread, low-salt bread and more, with vital cleaning needed between each batch. Meanwhile, there is also the requirement to label foods ever more accurately, to inform consumers in fine detail what is in their food. If that single sesame seed might be there, it should be on the label.

Which is where industry 4.0 technology comes in – particularly the combining of artificial intelligence (AI) and the Internet of Things (IoT). Towards the end of last year, a workshop was held in London to bring these technologies together in pursuit of better allergen detection and awareness. Held jointly by Internet of Food Things Network+ (IoFT+) and AI3 Science Discovery Network+ (AI3SD+), the event brought together researchers, industry representatives, regulators and policymakers to consider the entire food chain, from producer to consumer, and all the processes that ensure we know what is in our food.

Discussions began by looking at pioneering work in three areas – the use of robots for factory cleaning, sensors for e-coli detection and the potential of AI in the field, including machine learning and the concept of ‘augmented intelligence’, which brings together the strengths of both artificial and human intelligence. Can AI-driven robots improve cleaning? Reductions not only in downtime but in other costs are highly desirable – 30% of energy in dairy processing and 35% of water in beer production are spent on cleaning. Food safety might also be improved. A combination of AI robotics and low-cost ‘cleanliness’ sensors might reduce the amount of time taken to declare each area clean, while intelligent robots might interpret allergen sensors to recognise something that should not be there and raise an alert. The workshop heard from a pioneering multi-disciplinary project, RoboClean at the University of Nottingham, that is making significant progress towards such goals.

The traditional, gold standard analytical lab test to confirm the presence of e-coli in food takes three days. At the workshop, Zimmer and Peacock, the company behind a product that measures the hotness of Tabasco sauce – using, essentially, a low-cost sensor, a meter and a smartphone app – described how they are applying it to e-coli detection, with the goal of bringing down the speed of detection to just 30 minutes. Applying this to allergens is more complex. Liquids are homogenous: they are the same throughout and so a sample can be extracted for testing. But what of that sesame seed that might or might not be somewhere in those many loaves? You can’t just sample some of the bread.

Many questions around allergen detection and response go beyond sensors and robotics into practice and ethics, which became a focus for a large part of the event. Robot cleaners need to work with people on factory lines, who cannot be expected to have expertise in robotics. They may also do work that people are presently doing, which raises its own questions. Meanwhile, the event heard, there are considerable practical and ethical questions yet to be resolved around the collection and use of data, built-in biases of allergen data collection and the impacts of allergen control. Allergen research is focused towards western countries, their diets and ethnic groups. There is also bias towards studying mild food allergies because of the risks of things going wrong with severe, life-threatening allergies.

And a highly restrictive diet that excludes allergens may increase intolerances, so the cleaner we make our environments, the more we are effectively introducing more intolerance. It was noted that children who grow up on dairy farms have been found to have fewer allergies than other rural children. So we need safe exposure, but also safe foods. Is there a risk we reach a point of cutting out all allergen-based foods and producing a negative impact? The workshop concluded that such industry 4.0 technologies as AI and IoT-enabled sensors offer much potential to help with allergen detection, awareness and exposure in the food chain. Some applications are already in production and others show great promise. It was felt that the discussion between people from academia, industry and policy was an excellent starting point for what is a highly complex issue deserving continuing attention.   

IoFT+ and AI3SD+ will be publishing a position paper in 2020 as the next step in addressing the challenges. Meanwhile, the more complete workshop report can be downloaded here and anyone with a continuing interest should join the IoFT+ and AI3SD+ mailing lists, which confers membership of the networks.

The presentations given at the beginning of the event were:
The main themes of the workshop were:
  • 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
The original event page can be found here

17/09/2019 - AI3SD Presents at Internet of Food Things Network+ Conference

posted 15 Jan 2020, 05:00 by Samantha Kanza

On the 17th September 2019 Dr Samantha Kanza and Professor Jeremy Frey attended the Internet of Food things Network Conference.

The Internet of Food Things Network+ (IoFT) is funded by EPSRC, and is centred around nurturing and growing the UK's food and manufacturing digital economy. It is based out of the University of Lincoln, and our PI Professor Jeremy Frey is a Co-I on this Network+. It was launched on the 21st September 2018 and this event marked the end of their first year. AI3SD and IoFT overlap in several areas of interest and subsequently we were invited to give a talk to the conference attendees about AI3SD to let them know what we do and highlight areas they may wish to get involved with. 

The conference was hosted in the stunning Riseholme Park Campus of the University of Lincoln. It was spread over three days, and was a combination of presentations from a range of different experts in Industry and Academia, interspersed with networking opportunities and a chance to hear from the funding applicants for the IoFT Funding Awards. 

Samantha presented on AI3SD, outlining where our main research themes overlap with IoFT, and noted where there are relevant events and funding opportunities for IoFT members to get involved with. The full presentation can be downloaded from here

The full event information for this meeting can be found here

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

posted 19 Sep 2019, 02:32 by Samantha Kanza   [ updated 19 Sep 2019, 02:49 ]

On the 12th and 13th September 2019 AI3SD hosted a training workshop and hackathon, intended to help upskill scientists in Data Science, AI and Machine Learning techniques for Chemistry and provide some challenges to test out their new skills. 

Day 1 began with some informative presentations: 
The Challenges presented to the teams are detailed below. Full details of each challenge and the datasets provided can be found here
  • Solubility Challenge: This was a model building challenge to predict intrinsic aqueous solubilities using the available solubility datasets enhanced by other datasets
  • Data Mashup Challenge: A challenge to combine data from multiple different sources 
  • Chemical Safety Library Challenge: A challenge to work with the Pistoia Alliance's Chemical Safety Library Dataset and enhance it with other data sources. 
After this teams were formed and challenges were chosen! We had four teams, three of whom chose to do the solubility challenge and one who chose to do the data mashup challenge.

For the solubility challenge the different teams used a variety of methods to address this challenge. One team focused on using dimensionality reduction to select features that are good predictors of solubility. They used NCA to construct a pipeline and plan to evaluate a variety of methods from sklearn. The two others both used PCA, one initially using decision tree analysis, then variance analysis and PCA for feature extraction, followed by using AdaBoost Regression pipelined with anova analysis and PCA feature analysis; the other used scikit-learn and rdkit to work out fingerprints to do some initial machine learning, random forest and PCA. For the data mashup challenge the fourth team worked on a Jupyter notebook to pull spectra and physical data from a variety of different data sources to describe a list of common impurities (like solvents), with the look to facilitating a user importing their own dataset and viewing it alongside the provided spectra. 

Each of the teams were very energetic and enthusiastic and put together some very interesting work in a short space of time, and we were very impressed with all of their work. There had to be some winners however! And two teams did stand out for their innovative work, and the results were: 
  • 1st Place: Team Underachievers who undertook the data mashup challenge! 
  • 2nd Place: The Insolubles who undertook the solubility challenge! 

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

posted 11 Sep 2019, 04:44 by Samantha Kanza   [ updated 15 Jan 2020, 05:16 ]

On the 11th September 2019 AI3SD Hosted a Network+ Town Meeting and Funding Workshop at the University of Southampton Wide Lane Sports Ground in Eastleigh. This purpose of this meeting was to provide some useful talks around the different aspects of creating research projects including developing sustainable research software and impact and Intellectual Property in research projects, and also to give prospective applicants the opportunity to ask questions about our second funding call, and also to find other organisations to collaborate with. There was a presentation on tips for writing your funding application, and time set aside for specific networking to find collaborators. All questions and answers were written up and added to our Funding FAQ Page

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

posted 11 Sep 2019, 04:38 by Samantha Kanza   [ updated 23 Sep 2019, 02:20 ]

On the 18th and 19th July 2019 AI3SD teamed up with the Dial-a-Molecule Network, Directed Assembly Network and the University of Leeds to run a meeting for AI/Machine Learning for Chemical Discovery & Development. 

This was a two day residential event aiming to bring together stakeholders with different backgrounds including academic/industry, researchers/data owners, and chemists/engineers/computer scientists, to discuss applications of AI and Machine Learning in Chemical Discovery and Development. Over the two days a series of structured discussion sessions were held 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.  

These discussions have been captured in the Event Summary written by Dr Bao Nguyen which can be found here

Additionally, Professor Jonathan Goodman, who is a member of our advisory board also presented on AI3SD. His presentation can be found here

11/07/2019 - AI3SD Funds ECR to attend ISTCP: 10th Triennial Congress of the International Society for Theoretical Chemical Physics

posted 5 Sep 2019, 08:18 by Samantha Kanza   [ updated 18 Sep 2019, 03:48 ]

AI3SD offers funding for ECR members of AI3SD looking to attend conferences that are relevant to the Network+.

On the 11th July, Dr Grant Hill from the University of Sheffield attended the  ISTCP: 10th Triennial Congress of the International Society for Theoretical Chemical Physics. This was a seven day conference held in Norway with the aim of showcasing the achievement and advances of all areas of theoretical chemical physics. The conference had several areas that are of high interest to AI3SD including the sessions on Machine Learning and some of the talks in the Physical Organic Chemistry and Catalysis Sessions.

The full report written by Dr Grant Hill can be found here

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