Imperial College London
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DrAntonioDel Rio Chanona

Faculty of Engineering, Department of Chemical Engineering

Senior Lecturer
 
 
 
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Contact

 

a.del-rio-chanona Website

 
 
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Location

 

ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Méndez-Lucio:2021:10.26434/chemrxiv.14453106.v1,
author = {Méndez-Lucio, O and Ahmad, M and del, Rio-Chanona EA and Wegner, JK},
doi = {10.26434/chemrxiv.14453106.v1},
title = {A Geometric Deep Learning Approach to Predict Binding Conformations of Bioactive Molecules},
url = {http://dx.doi.org/10.26434/chemrxiv.14453106.v1},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:p>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. Herein, we report a method based on geometric deep learning that is capable of predicting the binding conformations of ligands to protein targets. Concretely, the model learns a statistical potential based on distance likelihood which is tailor-made for each ligand-target pair. This potential can be coupled with global optimization algorithms to reproduce experimental binding conformations of ligands. We show that the potential based on distance likelihood described in this paper performs similar or better than well-established scoring functions for docking and screening tasks. Overall, this method represents an example of how artificial intelligence can be used to improve structure-based drug design.</jats:p>
AU  - Méndez-Lucio,O
AU  - Ahmad,M
AU  - del,Rio-Chanona EA
AU  - Wegner,JK
DO  - 10.26434/chemrxiv.14453106.v1
PY  - 2021///
TI  - A Geometric Deep Learning Approach to Predict Binding Conformations of Bioactive Molecules
UR  - http://dx.doi.org/10.26434/chemrxiv.14453106.v1
ER  -
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