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AI3SD Video: Machines Learning Chemistry

AI3SD Video: Machines Learning Chemistry
AI3SD Video: Machines Learning Chemistry
Reinvigorated by algorithmic developments, faster hardware and large data sets, machine learning is pervading many aspects of chemistry. We present two examples from our recent studies, one in the area of drug discovery, the other focused on protein spectroscopy. In the first, we consider pharmaceutical lead discovery as active search in a space of labelled graphs [1]. We extend a recent data-driven adaptive Markov chain approach, and evaluate it on a focused drug design problem, where we search for an antagonist of an 훂v integrin, the target protein that belongs to a group of Arg-Gly-Asp integrin receptors. In the second example, we present a novel machine learning protocol that uses a few key structural descriptors to predict amide I IR spectra of proteins and agrees well with experiment [2]. Its transferability enabled us to distinguish protein secondary structures, probe atomic structure variations with temperature, and monitor protein folding.[1] Oglic, D., Oatley, S.A., Macdonald, S.J.F., McInally, T., Garnett, R., Hirst, J.D. & Gärtner, T. Active search for computer-aided drug design. Mol. Inf., 2018, 37, 1700130. https://doi.org/10.1002/minf.201700130[2] Ye, S., Zhong, K., Zhang, J., Hu, W., Hirst, J.D., Zhang, G., Mukamel, S., Jiang, J. A transferable machine learning protocol for predicting protein amide-I infrared spectra. J. Am. Chem. Soc., 2020, 142, 19071. https://doi.org/10.1021/jacs.0c06530
AI, AI3SD Event, Artificial Intelligence, Chemistry, Graphs, Machine Intelligence, Machine Learning, ML, Molecules Discovery, Networks
Hirst, Jonathan D.
650bceb6-d71e-4c32-98fb-9550c449caa2
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
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Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84
Hirst, Jonathan D.
650bceb6-d71e-4c32-98fb-9550c449caa2
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

Hirst, Jonathan D. (2021) AI3SD Video: Machines Learning Chemistry. 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/P0092).

Record type: Conference or Workshop Item (Other)

Abstract

Reinvigorated by algorithmic developments, faster hardware and large data sets, machine learning is pervading many aspects of chemistry. We present two examples from our recent studies, one in the area of drug discovery, the other focused on protein spectroscopy. In the first, we consider pharmaceutical lead discovery as active search in a space of labelled graphs [1]. We extend a recent data-driven adaptive Markov chain approach, and evaluate it on a focused drug design problem, where we search for an antagonist of an 훂v integrin, the target protein that belongs to a group of Arg-Gly-Asp integrin receptors. In the second example, we present a novel machine learning protocol that uses a few key structural descriptors to predict amide I IR spectra of proteins and agrees well with experiment [2]. Its transferability enabled us to distinguish protein secondary structures, probe atomic structure variations with temperature, and monitor protein folding.[1] Oglic, D., Oatley, S.A., Macdonald, S.J.F., McInally, T., Garnett, R., Hirst, J.D. & Gärtner, T. Active search for computer-aided drug design. Mol. Inf., 2018, 37, 1700130. https://doi.org/10.1002/minf.201700130[2] Ye, S., Zhong, K., Zhang, J., Hu, W., Hirst, J.D., Zhang, G., Mukamel, S., Jiang, J. A transferable machine learning protocol for predicting protein amide-I infrared spectra. J. Am. Chem. Soc., 2020, 142, 19071. https://doi.org/10.1021/jacs.0c06530

Video
AI3SD-Winter-Seminar-Series-GNM-JonathanHirst - Version of Record
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More information

Published date: 3 February 2021
Additional Information: Professor Jonathan Hirst obtained his B.A. in chemistry from Oxford University in 1990. He received his Ph. D. from London University in 1993, under the supervision of Dr. Michael Sternberg at the Imperial Cancer Research Fund. He spent the following three years as a postdoctoral research associate in the United States with Professor Charles Brooks III, first at Carnegie Mellon University, Pittsburgh, and subsequently at The Scripps Research Institute, La Jolla, as a recipient of a Human Frontiers Long-term Fellowship. In 1996, he was promoted to Assistant Professor. In 1999, he was appointed as a Lecturer in Computational and Theoretical Chemistry at the University of Nottingham. In 2002, he was promoted to Reader and in 2004 to Professor in Computational Chemistry. In 2012, he became the Head of the Department of Physical and Theoretical Chemistry. In Aug 2013, he relinquished this role to become the Head of the School of Chemistry, concluding a four-year term in 2017. In 2020, he was awarded a Royal Academy of Engineering Chair in Emerging Technologies.
Venue - Dates: AI3SD Winter Seminar Series, , Online, 2020-11-18 - 2021-04-21
Keywords: AI, AI3SD Event, Artificial Intelligence, Chemistry, Graphs, Machine Intelligence, Machine Learning, ML, Molecules Discovery, Networks

Identifiers

Local EPrints ID: 448982
URI: http://eprints.soton.ac.uk/id/eprint/448982
PURE UUID: b2163e9f-d4e7-4fe4-aa16-9934f32b0a37
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: 12 May 2021 16:44
Last modified: 13 May 2021 01:59

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Contributors

Author: Jonathan D. Hirst
Editor: Samantha Kanza ORCID iD
Editor: Jeremy G. Frey ORCID iD
Editor: Mahesan Niranjan
Editor: Victoria Hooper

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