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

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

Ben-Gurion University of the Negev

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

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.

Original language	English
Article number	276
Journal	Molecules
Volume	29
Issue number	1
DOIs	https://doi.org/10.3390/molecules29010276
State	Published - 4 Jan 2024
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2024 by the authors.

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.

Funders	Funder number
IMTI
Israel Ministry of Industry	4441137465
United States-Israel Binational Science Foundation	59081
Ministry of Defense

Keywords

cheminformatics
fragment screening
hit-to-lead optimization

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10.3390/molecules29010276

Cite this

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

Abstract

Bibliographical note

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Keywords

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