Target2DeNovoDrug: a novel programmatic tool for in silico-deep learning based de novo drug design for any target of interest. Issue 16 (2nd November 2022)

• Record Type: Journal Article
• Authors: Madaj, Rafal; Geoffrey, Ben; Sanker, Akhil; Valluri, Pavan Preetham
• Is Part Of: Journal of biomolecular structure & dynamics. Volume 40:Issue 16(2022)
• Page Start: 7511
• Page End: 7516
• Publication Date: 2022-11-02
• Subjects: Drug design -- automated drug discovery -- in silico drug discovery; Biomolecules -- Periodicals; Molecular structure -- Periodicals; Molecular Biology -- Periodicals; Biomechanics -- Periodicals; 572
• DOI: 10.1080/07391102.2021.1898474
• Languages: English
• ISSNs: 0739-1102

Abstract

Abstract: The on-going data-science and Artificial Intelligence (AI) revolution offer researchers a fresh set of tools to approach structure-based drug design problems in the computer-aided drug design space. A novel programmatic tool that incorporates in silico and deep learning based approaches for de novo drug design for any target of interest has been reported. Once the user specifies the target of interest in the form of a representative amino acid sequence or corresponding nucleotide sequence, the programmatic workflow of the tool generates compounds from the PubChem ligand library and novel SMILES of compounds not present in any ligand library but are likely to be active against the target. Following this, the tool performs a computationally efficient In-Silico modeling of the target and the newly generated compounds and stores the results of the protein-ligand interaction in the working folder of the user. Further, for the protein-ligand complex associated with the best protein-ligand interaction, the tool performs an automated Molecular Dynamics (MD) protocol and generates plots such as RMSD (Root Mean Square Deviation) which reveal the stability of the complex. A demonstrated use of the tool has been shown with the target signatures of Tumor Necrosis Factor-Alpha, an important therapeutic target in the case of anti-inflammatory treatment. The future scope of the tool involves, running the tool on a High-Performance Cluster for all known target signatures to generate data that will be useful to drive AI and Big data driven drug discovery. The code is hosted, maintained, and supported at the GitHub repository given in the link below https://github.com/bengeof/Target2DeNovoDrug Communicated by Ramaswamy H. Sarma