AI3SD Video: DeepDock: a deep learning approach to predict ligand binding conformations
AI3SD Video: DeepDock: a deep learning approach to predict ligand binding conformations
Understanding the interactions formed between a ligand and its molecular target is key to guide the optimization of molecules. Different experimental and computational methods have been key to understand better these intermolecular interactions. In this talk I will describe DeepDock, a method based on deep learning that is capable of predicting the binding conformations of ligands to protein targets. Overall, this method performs similar or better than well-established scoring functions for docking and screening tasks. Result presented in this talk are an example of how artificial intelligence can be used to improve structure-based drug design.
AI, AI3SD Event, Artificial Intelligence, Machine Intelligence, Machine Learning, ML, Proteins
Méndez-Lucio, Oscar
feedcfca-c22c-4021-b116-1c4de2f4f359
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
17 June 2021
Méndez-Lucio, Oscar
feedcfca-c22c-4021-b116-1c4de2f4f359
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Méndez-Lucio, Oscar
(2021)
AI3SD Video: DeepDock: a deep learning approach to predict ligand binding conformations.
Frey, Jeremy G., Kanza, Samantha and Niranjan, Mahesan
(eds.)
AI 4 Proteins Seminar Series 2021.
14 Apr - 17 Jun 2021.
(doi:10.5258/SOTON/P0102).
Record type:
Conference or Workshop Item
(Other)
Abstract
Understanding the interactions formed between a ligand and its molecular target is key to guide the optimization of molecules. Different experimental and computational methods have been key to understand better these intermolecular interactions. In this talk I will describe DeepDock, a method based on deep learning that is capable of predicting the binding conformations of ligands to protein targets. Overall, this method performs similar or better than well-established scoring functions for docking and screening tasks. Result presented in this talk are an example of how artificial intelligence can be used to improve structure-based drug design.
Video
AI4Proteins-Seminar-Series-OscarMendezLucio-170621
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Published date: 17 June 2021
Additional Information:
Oscar got his PhD in Chemistry (cheminformatics) from the University of Cambridge in 2016. In 2017, he joined Bayer to apply deep learning to their research pipeline involved in the toxicity prediction. Currently, he is a scientist at Janssen Pharmaceuticals using artifical intlligence to automatically design molecules with improved potency and safety profiles. Oscar has published more than 40 scientific papers including some featured in Nature and Nature Communications.
Venue - Dates:
AI 4 Proteins Seminar Series 2021, 2021-04-14 - 2021-06-17
Keywords:
AI, AI3SD Event, Artificial Intelligence, Machine Intelligence, Machine Learning, ML, Proteins
Identifiers
Local EPrints ID: 450162
URI: http://eprints.soton.ac.uk/id/eprint/450162
PURE UUID: 6cbbdd00-8c39-4262-bff7-7d6f51ed9a74
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Date deposited: 14 Jul 2021 16:38
Last modified: 17 Mar 2024 03:51
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Author:
Oscar Méndez-Lucio
Editor:
Mahesan Niranjan
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