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

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 - 1 Jan 2024

Keywords

cheminformatics
fragment screening
hit-to-lead optimization

ASJC Scopus subject areas

Analytical Chemistry
Chemistry (miscellaneous)
Molecular Medicine
Pharmaceutical Science
Drug Discovery
Physical and Theoretical Chemistry
Organic Chemistry

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10.3390/molecules29010276

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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.",

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AU - Alon, Shahar

AU - Rozenberg, Nofar

AU - Peled, Amit

AU - Piro, Daniel

AU - Vilenchik, Dan

AU - Akabayov, Barak

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AB - 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.

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KW - hit-to-lead optimization

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

Abstract

Keywords

ASJC Scopus subject areas

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