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 journalArticlepeer-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 languageEnglish
Article number276
JournalMolecules
Volume29
Issue number1
DOIs
StatePublished - 1 Jan 2024

Keywords

  • cheminformatics
  • fragment screening
  • hit-to-lead optimization

ASJC Scopus subject areas

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

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