Site-specific template generative approach for retrosynthetic planning
Shee Y, Li H, Zhang P, Nikolic A, Lu W, Kelly H, Manee V, Sreekumar S, Buono F, Song J, Newhouse T, Batista V. Site-specific template generative approach for retrosynthetic planning. Nature Communications 2024, 15: 7818. PMID: 39251606, PMCID: PMC11385523, DOI: 10.1038/s41467-024-52048-4.Peer-Reviewed Original ResearchComplexity of chemical spaceRetrosynthetic planningGenerative machine learning methodsChemical spaceTarget compoundsChemical transformationsChemical synthesisReaction templatesSynthetic pathwaySmall moleculesGenerative machine learningMoleculesReactionMachine learning methodsSynthesisUser selectionSynthonsLearning methodsMachine learningGeneration approachReactantsRetrosynthesisInterconversionCompoundsLigand-Based Principal Component Analysis Followed by Ridge Regression: Application to an Asymmetric Negishi Reaction
Kelly H, Sreekumar S, Manee V, Cuomo A, Newhouse T, Batista V, Buono F. Ligand-Based Principal Component Analysis Followed by Ridge Regression: Application to an Asymmetric Negishi Reaction. ACS Catalysis 2024, 14: 5027-5038. DOI: 10.1021/acscatal.3c06230.Peer-Reviewed Original ResearchPd-catalyzed Negishi cross-coupling reactionsC-C bond-forming reactionsNegishi cross-coupling reactionsP-chiral monophosphorus ligandsCross-coupling reactionsP-stacking interactionsBond-forming reactionsElectronic descriptorsNegishi reactionMonophosphorus ligandsCatalytic systemChemical spaceEnantioselectivityChemical understandingLigandReactionSelective inversionDescriptorsRidge regressionStericallyChemicalPrincipal component analysisMechanistic knowledgeRidge regression modelElectron