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AI3SD Video: An Open Competition of People and Machines to Develop Predictive Models for Antimalarial Drug Discovery

AI3SD Video: An Open Competition of People and Machines to Develop Predictive Models for Antimalarial Drug Discovery
AI3SD Video: An Open Competition of People and Machines to Develop Predictive Models for Antimalarial Drug Discovery
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.
AI, AI3SD Event, Artificial Intelligence, Chemistry, Drug Discovery, Machine Intelligence, Machine Learning, ML, Scientific Discovery
Todd, Matthew
1084a7bc-bd6f-4014-8acd-091512a93a3f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84
Todd, Matthew
1084a7bc-bd6f-4014-8acd-091512a93a3f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84

Todd, Matthew (2020) AI3SD Video: An Open Competition of People and Machines to Develop Predictive Models for Antimalarial Drug Discovery. Kanza, Samantha, Frey, Jeremy G., Niranjan, Mahesan and Hooper, Victoria (eds.) AI3SD Winter Seminar Series, , Online. 18 Nov 2020 - 21 Apr 2021 . (doi:10.5258/SOTON/P0085).

Record type: Conference or Workshop Item (Other)

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.

Video
AI3SD-Winter-Seminar-Series-DrugDiscovery-MatTodd-AI3SD - Version of Record
Available under License Creative Commons Attribution.
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More information

Published date: 2 December 2020
Additional Information: 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.
Venue - Dates: AI3SD Winter Seminar Series, , Online, 2020-11-18 - 2021-04-21
Keywords: AI, AI3SD Event, Artificial Intelligence, Chemistry, Drug Discovery, Machine Intelligence, Machine Learning, ML, Scientific Discovery

Identifiers

Local EPrints ID: 448781
URI: http://eprints.soton.ac.uk/id/eprint/448781
PURE UUID: 7c586e17-6526-4a83-86d2-76ab29c1ecc2
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302

Catalogue record

Date deposited: 05 May 2021 16:48
Last modified: 06 May 2021 01:59

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Contributors

Author: Matthew Todd
Editor: Samantha Kanza ORCID iD
Editor: Jeremy G. Frey ORCID iD
Editor: Mahesan Niranjan
Editor: Victoria Hooper

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