MolOptimizer: A Molecular Optimization Toolkit for Fragment-Based Drug Design

Adam Soffer; Samuel Joshua Viswas; Shahar Alon; Nofar Rozenberg; Amit Peled; Daniel Piro; Dan Vilenchik; Barak Akabayov

doi:10.3390/molecules29010276

MolOptimizer: A Molecular Optimization Toolkit for Fragment-Based Drug Design

Molecules. 2024 Jan 4;29(1):276. doi: 10.3390/molecules29010276.

Authors

Adam Soffer^{1

2}, Samuel Joshua Viswas^{1

2}, Shahar Alon³, Nofar Rozenberg³, Amit Peled³, Daniel Piro³, Dan Vilenchik⁴, Barak Akabayov^{1

2}

Affiliations

¹ Department of Chemistry, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel.
² Data Science Research Centre, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel.
³ Department of Software Engineering, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel.
⁴ School of Computer and Electrical Engineering, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel.

Abstract

MolOptimizer is a user-friendly computational toolkit designed to streamline the hit-to-lead optimization process in drug discovery. MolOptimizer extracts features and trains machine learning models using a user-provided, labeled, and small-molecule dataset to accurately predict the binding values of new small molecules that share similar scaffolds with the target in focus. Hosted on the Azure web-based server, MolOptimizer emerges as a vital resource, accelerating the discovery and development of novel drug candidates with improved binding properties.

Keywords: cheminformatics; fragment screening; hit-to-lead optimization.

MeSH terms

Drug Design*
Drug Discovery*
Machine Learning

Grants and funding

This research was supported by grant no. 2016142 from the United States-Israel Binational Science Foundation (BSF), grant no. 59081 from the IMTI (TAMAT)/Israel Ministry of Industry–KAMIN Program, and grant no. 4441137465 from the Israel Ministry of Defense.