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

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 o...

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Bibliographic Details
Published in:Molecules (Basel, Switzerland) Switzerland), 2024-01, Vol.29 (1), p.276
Main Authors: Soffer, Adam, Viswas, Samuel Joshua, Alon, Shahar, Rozenberg, Nofar, Peled, Amit, Piro, Daniel, Vilenchik, Dan, Akabayov, Barak
Format: Article
Language:English
Subjects:
Algorithms
cheminformatics
Datasets
Decision trees
Design
Drug discovery
fragment screening
hit-to-lead optimization
Libraries
Machine learning
Optimization
Tutorial
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Summary: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.
ISSN:1420-3049
1420-3049
DOI:10.3390/molecules29010276