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AI3SD Video: Using icospherical input data in machine learning on the protein-binding problem

AI3SD Video: Using icospherical input data in machine learning on the protein-binding problem
AI3SD Video: Using icospherical input data in machine learning on the protein-binding problem
Determining the binding coefficients of ligands to proteins is an essential step in targeted drug development. The 3-dimensional structure of both the protein binding pocket and the ligand is crucial in solving this problem. I will present ICOSPHERER (Icospherical Chemical Objects Surpassing Traditional A.I. Restrictions Through Replacing Existing Representations) a new methodology and software and demonstrate it’s use on the protein binding problem.
AI, AI3SD Event, Artificial Intelligence, Machine Learning, Machine Intelligence, ML, Proteins
Gale, Ella
24d979c7-f91f-4ccd-979c-56dcbf99acf1
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Gale, Ella
24d979c7-f91f-4ccd-979c-56dcbf99acf1
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Gale, Ella (2021) AI3SD Video: Using icospherical input data in machine learning on the protein-binding problem. Frey, Jeremy G., Kanza, Samantha and Niranjan, Mahesan (eds.) AI 4 Proteins Seminar Series 2021. 14 Apr - 17 Jun 2021. (doi:10.5258/SOTON/P0100).

Record type: Conference or Workshop Item (Other)

Abstract

Determining the binding coefficients of ligands to proteins is an essential step in targeted drug development. The 3-dimensional structure of both the protein binding pocket and the ligand is crucial in solving this problem. I will present ICOSPHERER (Icospherical Chemical Objects Surpassing Traditional A.I. Restrictions Through Replacing Existing Representations) a new methodology and software and demonstrate it’s use on the protein binding problem.

Video
AI4Proteins-Seminar-Series-EllaGale-160621 - Version of Record
Available under License Creative Commons Attribution.
Download (336MB)

More information

Published date: 16 June 2021
Additional Information: Ella Gale is currently the Machine Learning subject specialist attached to the Technology Enhanced Chemical Synthesis CDT in the School of Chemistry at the University of Bristol. Her current responsibilities include training the CDT students in machine learning, data science, statistics and design of experiments, providing data science and machine learning support to the chemistry department generally and researching machine learning techniques for retrosynthesis and de novo drug design. Dr Gale has has over ten years of experience working across artificial intelligence, computer science and chemistry.
Venue - Dates: AI 4 Proteins Seminar Series 2021, 2021-04-14 - 2021-06-17
Keywords: AI, AI3SD Event, Artificial Intelligence, Machine Learning, Machine Intelligence, ML, Proteins

Identifiers

Local EPrints ID: 450160
URI: http://eprints.soton.ac.uk/id/eprint/450160
PURE UUID: 07fa2750-ad4f-47cf-b31f-2d15f6db8cb4
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489

Catalogue record

Date deposited: 14 Jul 2021 16:33
Last modified: 28 Jul 2021 01:54

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