23/09/2020 – AI3SD Online Seminar Series: AI for Science: Transforming Scientific Research – Professor Tony Hey

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There is now broad recognition within the scientific community that the ongoing deluge of scientific data is fundamentally transforming academic research. Turing Award winner Jim Gray referred to this revolution as “The Fourth Paradigm: Data Intensive Scientific Discovery’. Researchers now need tools and technologies to manipulate, analyze, visualize, and manage vast amounts of research data. This talk will begin by reviewing the challenges posed by the explosive growth of experimental and observational data generated by large-scale facilities such as the Diamond Synchrotron and the CryoEM Facilities at the Rutherford Appleton Laboratory. Increasingly, scientists are beginning to use sophisticated machine learning and other AI technologies both to automate parts of the data pipeline and also to find new scientific discoveries in the deluge of experimental data. In particular, ‘Deep Learning’ neural networks have already transformed several areas of computer science and research scientists are now exploring their use in analyzing their ‘Big Scientific Data’. The talk concludes with a vision of how this ‘AI for Science’ agenda can be truly transformative for experimental scientific discovery.

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16/09/2020 – AI3SD Online Seminar Series: Supramolecular Antimicrobials – the next target for AI/Machine Learning? – Dr Jennifer Hiscock

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Since the 1980’s the development of novel antibiotics has dramatically reduced. This, combined with the ever-increasing prevalence of antibiotic resistance in bacteria, means that some bacterial strains have now been identified that are resistant to treatment with all known classes of antibiotic currently available. Supramolecular Self-associating Amphiphiles (SSAs) are a novel class of amphiphilic salts that contain an uneven number of covalently linked hydrogen bond donating and accepting groups, meaning that they are ‘frustrated’ in nature. The hydrogen-bonded, self-associative properties for members of this class of over 70 compounds synthesised to date have been extensively studied in the gas phase, solution state, solid state and in silico. Through these studies we have shown correlations between certain physicochemical properties that maybe predicted by simple, low-level, high-throughput, easily accessible computational modelling. In addition, members from this class of compound have been shown to kill a variety of different bacteria, including those with known antibiotic resistance (e.g. Methicillin Resistant Staphylococcus aureus (MRSA)). These initial studies have highlighted within the supramolecular chemistry community a vast amount of experimental data, not yet accessed by AI/machine learning. Could data sets such as these be the next targets of interest for this community? Is there room for a consortium or community led approach to solving predictive modelling within this branch of chemistry.

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09/09/2020 – AI3SD Online Seminar Series: Using Artificial Intelligence to Optimise Small-Molecule Drug Design – Dr Nathan Brown

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he concept of in silico molecular design goes back decades and has a long history of published approaches using many different algorithms and models. Major challenges involved in de novo molecular design are manifold, including identifying appropriate molecular representations for optimisation, scoring designed molecules against multiple modelled endpoints, and objectively quantifying synthetic feasibility of the designed structures. Recently, multiobjective de novo design, more recently referred to as generative chemistry, has had a resurgence of interest. This renaissance has highlighted a step-change in successful applications of such methods. This presentation will review the development of de novo design methods over the years including the author’s original work in this area from the early 2000s, to recent approaches that show great promise. Through this review, improvements in important components of de novo design, including machine learning model predictions and automated synthesis planning, will also be presented.

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

https://www.youtube.com/watch?v=gcgeTVfiVl4&ab_channel=AI4ScientificDiscovery Interview: Dr Wendy Warr interviewed John prior to this seminar. This interview can be found here: https://eprints.soton.ac.uk/441804/ Abstract: Pandemics, such as Covid-19. are by definition essentially unanticipatable and rapid onset. Features…

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

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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.

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

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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.

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

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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. 

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26/10/2018 – AI3SD Attends Centre of Machine Intelligence Showcase

Co-Investigator Niranjan and Network+ Coordinator Samantha attended the Centre of Machine Intelligence Showcase at the University of Southampton, where Niranjan our Co-Investigator gave an insightful talk on learning from outliers…

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