Eventbrite Link: https://www.eventbrite.co.uk/e/ai-in-drug-discovery-and-drug-safety-workshop-tickets-51705581787

Description:
This is event is brought to you by AI3SD and Medicines Discovery Catapult. Drug discovery is a complex and long-term scientific investigation involving interdisciplinary research methods coupled with large heterogeneous datasets. The research and data space in this area is vast, and we believe that the use of AI and machine learning technologies can help spur on advances in this domain. This workshop has been designed to draw together those with a keen interest in using AI and machine learning technologies in the domain of drug discovery, both to aid future drug discovery, and to help improve drug safety. At AI3SD we firmly believe that interdisciplinary collaboration is the key to many of these advances, and so welcome anyone working in the technical or scientific ends of this domain, as well as those already working in an interdisciplinary fashion. There will be keynote talks interspersed with general group discussions, and working groups around the key topics that arise. There will also be an opportunity to tour the labs at Medicines Discovery Catapult. Lunch will be provided and there will be plenty of time for networking, and the day will conclude with a prosecco reception.

Keynote Speakers:
Alongside the discussions and lab tours there will some keynote talks about different aspects of drug discovery:

• Professor John Overington, Chief Informatics Officer, Medicines Discovery Catapult. John leads the development and application of informatics approaches to promote and support innovative, fast-to-patient drug discovery in the UK through collaborative projects across the applied R&D community.
• Professor Val Gillet – Val is a Professor of Chemoinformatics at the University of Sheffield where she heads the Chemoinformatics Research Group. Her research focuses on the development and validation of chemoinformatics methods, especially for drug discovery. She has expertise in machine learning, evolutionary algorithms and the development of novel methods for molecular representation and applying these to applications such as de novo design and virtual screening. She has collaborated with many of the major pharmaceutical companies and specialist chemcoinformatics software companies.
• Dr Willem van Hoorn, Chief Decision Scientist, ExScientia. Willem gained a PhD in computational chemistry in the group of David Reinhoudt at the University of Twente followed by a postdoc at Yale. He subsequently spent a decade at Pfizer focusing on computational techniques for HTS triage and combinatorial library design. This was followed by a position as senior solutions consultant at Accelrys assisting a range of clients from small biotech to big pharma.
• Dr Nicola Richmond – Nicola is the Director of Artificial Intelligence and Machine Learning at GlaxoSmithKline. Her research focuses on discovering innovative ways of deriving insights from data to advance drug discovery and development.

Keynote Abstracts:

• Using Machine Learning to Drive Reaction Based De Novo Design – Professor Val Gillet: The de novo design of novel drug candidate has been a topic of considerable interest since the 1990s. The main challenges in de novo design arise from the astronomical number of drug-like molecules that could exist and the difficulties associated with designing scoring functions to navigate this space. The recent resurgence of interest in de novo design can be attributed to the application of deep learning methods that typically operate on SMILES strings. While these approaches have been shown to be effective in generating valid SMILES, they are limited in the extent to which they can account for synthetically accessibility. We have been working on reaction-based de novo design for a number of years. Our approach takes explicit account of synthetic accessibility since the transformations that are applied to molecules are based on rules derived from real reactions. The rules are encoded as reaction vectors and are derived automatically from reaction databases. The availability of large public datasets of reactions provides a rich source of reactions for synthetically accessible de novo design. Here we will describe how we are using machine learning to select the most promising reactions for reaction-based de novo design.
• Re-energising Small Molecule Drug Discovery – Dr Willem van Hoorn: The optimisation trajectory of hit to lead to candidate is the most expensive part of drug discovery. Exscientia’s drug discovery platform brings that cost down significantly by combining the strengths of AI compound design and human strategic thinking into the Centaur ChemistTM. A high level overview of the technology is presented and results are shown from successful collaborations that resulted in the delivery of clinical candidates in less than a year.
• Understanding the holes in the metabolome – Dr Nicola Richmond: The metabolome refers to the complete set of both endogenous and exogenous small molecule metabolites that are either produced naturally as a bi-product of a biological process or as a result of the external environment. Quantifying changes in the metabolome can help diagnose disease, understand disease mechanisms, identify novel drug targets and understand drug safety and efficacy. As such, metabolomics is now widely used in the pharmaceutical industry throughout the drug discovery and development process. The annotated human metabolome now stands at over 350K metabolites and 25K pathways. It is therefore unsurprising that analysing metabolomics data presents a major challenge. The current gold standard approach is highly subjective and does not account for pathway-level, structural information. Hypotheses tend to be established a priori and validated through manual navigation of data rather than letting the data speak. At GSK, we have established a fully automated, data-driven approach to analysing metabolomics data using concepts from topological data analyses. Our analysis pipeline provides bench scientists with an automated approach for validating their hypotheses, allows data scientist, with no understanding of biology, to generate meaningful hypotheses and potentially fills gaps in our understanding of the metabolite.

Programme:

• 10:00 – 10:30: Coffee & Registration
• 10:30 – 10:45: Introduction with Professor John Overington
• 10:45 – 11:15: Using Machine Learning to Drive Reaction Based De Novo Design – Professor Val Gillet
• 11:15 – 12:30: Initial Discussions
• 12:30 – 13:00: Lunch
• 13:00 – 13:30: Re-energising Small Molecule Drug Discovery – Dr Willem van Hoorn
• 13:30 – 15:00: Working Group Discussions and Lab Tours
• 15:00 – 15:15: Coffee
• 15:15 – 15:45: Report and Action Plan
• 15:45 – 16:15: Understanding the holes in the metabolome – Dr Nicola Richmond
• 16:15 – 17:30: Prosecco reception

FAQS:

1. Who should attend?
Anyone with an interest in Drug Discovery and Drug Safety, and Artificial Intelligence, Machine Learning or Data Science and how these methods can be applied to Drug Discovery/Safety. We welcome members from academia, industry and government. We are always looking to grow our Network+ and bring in people with a wealth of experience in the many different subject areas that are needed so that we can form interdisciplinary partnerships and work together to further the field of Scientific Discovery.

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 hear a range of thought-provoking talks about different aspects of using AI and Machine Learning in Drug Discovery and Drug Safety. There will also be plenty of time to network and discuss this subject area with other members of the workshop, in addition to being able to take a tour of the Medicines Discovery Catapult labs. Members of the Network Executive Group and Advisory Board will also be in attendance so you will be able to find out more about our Network and the opportunities we have available including funding opportunities and the types of events we will be running (e.g. workshops, conferences and hackathons).

3. What are the aims of the workshop?
This workshop is aiming to help AI3SD and MDC drive progress in this area 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.