Identification of novel hit molecules targeting M. tuberculosis polyketide synthase 13 by combining generative AI and physics-based methods

被引：0

作者：

Chikhale R.V. ^{[1
]}

Choudhary R. ^{[2
,4
]}

Malhotra J. ^{[2
,4
]}

Eldesoky G.E. ^{[3
]}

Mangal P. ^{[2
,4
]}

Patil P.C. ^{[4
]}

机构：

[1] Department of Pharmaceutical and Biological Chemistry, School of Pharmacy, University College London, London

[2] SilicoScientia Private Limited, Nagananda Commercial Complex, No. 07/3, 15/1, 18th Main Road, Jayanagar 9th Block, Bengaluru

[3] Chemistry Department, College of Science, King Saud University, Riyadh

[4] Department of Bioinformatics, Rajiv Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth Deemed to Be University, Pune-Satara Road, Pune

来源：

Computers in Biology and Medicine | 2024年 / 176卷

关键词：

antiTb drug discovery; Deep neural network; FEL; Generative AI; Graph learning; Physics-based methods; Pks13; Reinforcement learning; Reinvent; 4;

D O I：

10.1016/j.compbiomed.2024.108573

中图分类号：

学科分类号：

摘要：

In this work we investigated the Pks13-TE domain, which plays a critical role in the viability of the mycobacteria. In this report, we have used a series of AI and Physics-based tools to identify Pks13-TE inhibitors. The Reinvent 4, pKCSM, KDeep, and SwissADME are AI-ML-based tools. AutoDock Vina, PLANTS, MDS, and MM-GBSA are physics-based methods. A combination of these methods yields powerful support in the drug discovery cycle. Known inhibitors of Pks13-TE were collected, curated, and used as input for the AI-based tools, and Mol2Mol molecular optimisation methods generated novel inhibitors. These ligands were filtered based on physics-based methods like molecular docking and molecular dynamics using multiple tools for consensus generation. Rigorous analysis was performed on the selected compounds to reduce the chemical space while retaining the most promising compounds. The molecule interactions, stability of the protein-ligand complexes and the comparable binding energies with the native ligand were essential factors for narrowing the ligands set. The filtered ligands from docking, MDS, and binding energy colocations were further tested for their ADMET properties since they are among the essential criteria for this series of molecules. It was found that ligands Mt1 to Mt6 have excellent predicted pharmacokinetic, pharmacodynamic and toxicity profiles and good synthesisability. © 2024 Elsevier Ltd

引用

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