Dec

AI3SD Winter Seminar Series: Robots, AI and NLP in Drug Discovery

by AI3 Science Discovery Network (AI3SD)

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Event description

Dr Sia Togia, Dr Martin-Immanuel Bittner and Professor Matt Todd will speak about Robots, AI and NLP in Drug Discovery

About this Event

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.

Agenda

14:00-14:45: Natural Language Processing in AI-driven Drug Discovery: What it is, why it matters and how (not) to do it - Dr Sia Togia (Benevolent AI)
14:45-15:00: Coffee Break
15:00-15:45: New Trends in Drug Discovery - Robotics & AI - Dr Martin-Immanuel Bittner (Arctoris)
15:45-16:00: Coffee Break
16:00-16:45: An Open Competition of People and Machines to Develop Predictive Models for Antimalarial Drug Discovery - Professor Matthew Todd (University College London)

Abstracts & Speaker Bios

Natural Language Processing in AI-driven Drug Discovery: What it is, why it matters and how (not) to do it - Dr Sia Togia

Abstract: Natural Language Processing (NLP) has been used in drug discovery for decades. However, the emergence of AI-driven drug discovery coupled with recent advances in NLP have redefined the relationship between the two fields. This talk will focus on major classes of NLP techniques used in drug discovery as well as challenges arising when creating real-world biomedical NLP systems. The talk will cover topics such as dataset selection and construction, evaluation, models, methodology, research process and structure of NLP systems in AI-driven drug discovery.

Bio: I am a Lead AI Scientist at Benevolent AI with a focus on NLP. I have a PhD in NLP from the University of Cambridge and have spent the last six years building NLP systems in the AI industry, mostly in the biomedical domain. I specialise in Information Extraction, Information Retrieval and Knowledge Representation and have a strong interest in creating NLP systems that solve real-world problems.

New Trends in Drug Discovery - Robotics & AI - Dr Martin-Immanuel Bittner

Abstract: The drug discovery ecosystem is undergoing transformational change. New technologies have an increasing impact on how drugs are being discovered and developed, including in particular Artificial Intelligence and robotics. A critical precondition for successful development of AI applications is having access to large amounts of structured, annotated, machine-learnable data. The talk will cover how recent advances in laboratory automation and robotics enable the generation of biomedical data at scale, providing the most critical input for AI systems. The talk will present real-world case studies, showing how leading AI drug discovery firms benefit from full automation of their discovery processes, delegating experiment execution to advanced robotics, and allowing for projects to move to the next stage faster and more efficiently.

Bio: Martin-Immanuel Bittner MD DPhil is the Chief Executive Officer of Arctoris, the world's first fully automated drug discovery platform that he cofounded in 2016. He graduated as a medical doctor from the University of Freiburg in Germany, followed by his DPhil in Oncology as a Rhodes scholar at the University of Oxford. Martin has extensive research experience covering both clinical trials and preclinical drug discovery and is an active member of several leading cancer research organisations, including EACR, AACR, and ESTRO. In recognition of his research achievements, he was elected a member of the Young Academy of the German National Academy of Sciences in 2018.

An Open Competition of People and Machines to Develop Predictive Models for Antimalarial Drug Discovery - Professor Matthew Todd

Abstract: One of the most promising series within the Open Source Malaria (OSM) consortium involves compounds that are active in the in vivo model of the disease. A molecular mechanism of action is strongly implicated, and is a mechanism shared with several leading antimalarials in the drug development pipeline, but no crystal structure has been obtained for the protein target. This OSM project is in the lead optimisation phase, with small changes being made to the structures synthesised. Yet even now many compounds designed by the human chemists are proving to be inactive, which can be wasteful of project resources. Over the last several years the consortium has run open competitions to see if the broader community can derive more predictive models for which molecules to synthesise. The most recent, funded by AI3SD, elicited high quality, open submissions from academia and several new companies specialising in artificial intelligence and machine learning. To close the loop, and examine the utility of these predictions, several of the novel structures proposed were synthesised and evaluated in a blood stage antimalarial assay. Were the machine-assisted predictions better than those derived from human intuition? An answer will be provided.

Bio: Mat Todd was born in Manchester, England. He obtained his PhD in organic chemistry from Cambridge University in 1999, was a Wellcome Trust postdoc at The University of California, Berkeley, a college fellow back at Cambridge University, a lecturer at Queen Mary, University of London and between 2005 and 2018 was at the School of Chemistry, The University of Sydney. He is now Chair of Drug Discovery at University College London. He lives in Greenwich, London, with his wife and two children. Mat’s research interests include the development of new ways to make molecules, particularly how to make chiral molecules with new catalysts. He is also interested in making metal complexes that do unusual things when they meet biological molecules or metal ions. His lab motto is ”To make the right molecule in the right place at the right time”, and his students are currently trying to work out what this means. He has a significant interest in open science, and how it may be used to accelerate research, with particular emphasis on open source discovery of new medicines. He founded and currently leads the Open Source Malaria (OSM) and Open Source Mycetoma (MycetOS) consortia, and is a founder of a broader Open Source Pharma movement. He is on the Editorial Boards of PLoS One, ChemistryOpen and Nature Scientific Reports.

About AI3SD

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.