This seminar was the second of ten in our AI3SD Winter Seminar Series. This seminar was hosted online via a zoom webinar. The event was themed around Enhancing Experiments through…
This seminar forms part of the AI3SD Online Seminar Series that will run across the winter (from November 2020 to April 2021). This seminar will be run via zoom, when you register on Eventbrite you will receive a zoom registration email alongside your standard Eventbrite registration email. Where speakers have given permission to be recorded, their talks will be made available on our AI3SD YouTube Channel. The theme for this seminar is Robots, AI and NLP in Drug Discovery.
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 āThe Fourth Paradigm: Data Intensive Scientific Discoveryā. 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, āDeep Learningā neural networks have already transformed several areas of computer science and research scientists are now exploring their use in analyzing their āBig Scientific Dataā. The talk concludes with a vision of how this āAI for Scienceā agenda can be truly transformative for experimental scientific discovery.
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 authorās 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.
This year theĀ AI3SD Network+Ā (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) ran a workshop at theĀ WebSci20Ā Conference that took place online due to COVID-19 Restrictions. A full report…
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.
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…
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.
AI3SD offers funding for ECR members of AI3SD looking to attend conferences that are relevant to the Network+. On the 6th May, Dr James Cumby from the University of Edinburgh…
On the 19th March AI3SD hosted an AI for Materials Discovery Workshop at the University of Southampton. This was a very well attended workshop, with over 60 participants attending across…