{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["29(1)"],"submitter":["Soffer A"],"pubmed_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."],"journal":["Molecules (Basel, Switzerland)"],"pagination":["276"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10780997"],"repository":["biostudies-literature"],"pubmed_title":["MolOptimizer: A Molecular Optimization Toolkit for Fragment-Based Drug Design."],"pmcid":["PMC10780997"],"pubmed_authors":["Akabayov B","Rozenberg N","Soffer A","Peled A","Piro D","Alon S","Vilenchik D","Viswas SJ"],"additional_accession":[]},"is_claimable":false,"name":"MolOptimizer: A Molecular Optimization Toolkit for Fragment-Based Drug Design.","description":"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.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jan","modification":"2025-04-04T13:59:55.639Z","creation":"2025-04-04T13:59:55.639Z"},"accession":"S-EPMC10780997","cross_references":{"pubmed":["38202859"],"doi":["10.3390/molecules29010276"]}}