Augmenting a training dataset of the generative diffusion model for molecular docking with artificial binding pockets-Reference-Cited by-同舟云学术

Augmenting a training dataset of the generative diffusion model for molecular docking with artificial binding pockets

Published:2024 Issue:2 Volume:14 Page:1341-1353
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Voitsitskyi Taras¹²^ORCID,Bdzhola Volodymyr³^ORCID,Stratiichuk Roman¹⁴,Koleiev Ihor¹²,Ostrovsky Zakhar¹,Vozniak Volodymyr¹,Khropachov Ivan¹,Henitsoi Pavlo¹,Popryho Leonid¹,Zhytar Roman¹,Yesylevskyy Semen¹⁵²⁶^ORCID,Nafiiev Alan¹,Starosyla Serhii¹^ORCID

Affiliation:

1. Receptor.AI Inc., 20-22 Wenlock Road, London N1 7GU, UK

2. Department of Physics of Biological Systems, Institute of Physics of The National Academy of Sciences of Ukraine, 46 Nauky Ave., Kyiv 03038, Ukraine

3. Institute of Molecular Biology and Genetics of The National Academy of Sciences of Ukraine, 150 Zabolotnogo Str., Kyiv 03143, Ukraine

4. Department of Biophysics and Medical Informatics, Educational and Scientific Centre “Institute of Biology and Medicine”, Taras Shevchenko Kyiv National University, 64 Volodymyrska Str., Kyiv 01601, Ukraine

5. Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, Prague 6 CZ-166 10, Czech Republic

6. Department of Physical Chemistry, Faculty of Science, Palacký University Olomouc, 17 listopadu 12, Olomouc 771 46, Czech Republic

Abstract

We introduce introduces the PocketCFDM generative diffusion model, aimed at improving the prediction of small molecule poses in the protein binding pockets.

Funder

European Commission

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2024/RA/D3RA08147H

Reference36 articles.

1. Evaluation of the performance of four molecular docking programs on a diverse set of protein-ligand complexes

2. J. B.Ghasemi , A.Abdolmaleki and F.Shiri , in Pharmaceutical Sciences: Breakthroughs in Research and Practice , IGI Global , 2017 , pp. 770–794

3. W.Lu , Q.Wu , J.Zhang , J.Rao , C.Li and S.Zheng , bioRxiv , 2022 , preprint, 10.1101/2022.06.06.495043

4. H.Stärk , O.-E.Ganea , L.Pattanaik , R.Barzilay and T.Jaakkola , arXiv , 2022 , preprint, arXiv:2202.05146, 10.48550/arXiv.2202.05146

5. B.Jing , S.Eismann , P.Suriana , R. J. L.Townshend and R.Dror , arXiv , 2021 , preprint, arXiv:2009.01411, 10.48550/arXiv.2009.01411