2024
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 approachReactantsRetrosynthesisInterconversionCompoundsQuantum-to-Classical Neural Network Transfer Learning Applied to Drug Toxicity Prediction
Smaldone A, Batista V. Quantum-to-Classical Neural Network Transfer Learning Applied to Drug Toxicity Prediction. Journal Of Chemical Theory And Computation 2024, 20: 4901-4908. PMID: 38795030, DOI: 10.1021/acs.jctc.4c00432.Peer-Reviewed Original ResearchScalable machine learning modelVastness of chemical spaceInner product estimationDrug toxicity predictionMachine learning modelsLearnable weightsLearning AppliedDeep learningQuantum phase estimationTox21 dataHadamard testLearning modelsMatrix multiplicationReduced complexityNeural behaviorQuantum circuit designLife-saving applicationsQuantum advantagePrediction accuracyQuantumPhase estimationSwap testChemical spaceToxicity predictionCircuit designLigand-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 modelElectronChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation
Kyro G, Morgunov A, Brent R, Batista V. ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation. Journal Of Chemical Information And Modeling 2024, 64: 653-665. PMID: 38287889, DOI: 10.1021/acs.jcim.3c01456.Peer-Reviewed Original ResearchConceptsVastness of chemical spaceMolecular generationDomain of drug discoveryArtificial intelligence modelsChemical spaceIntelligence modelsLearning methodologyPython packageDrug discoverySmall molecule inhibitorsActive learning methodologiesFDA-approved small molecule inhibitorsMoleculesEfficient methodDomainSoftwareC-Abl kinase
2023
ChemSpaceAL: An efficient active learning methodology applied to protein-specific molecular generation
Kyro G, Morgunov A, Brent R, Batista V. ChemSpaceAL: An efficient active learning methodology applied to protein-specific molecular generation. Biophysical Journal 2023, 123: 283a. PMID: 37744464, PMCID: PMC10516108, DOI: 10.1016/j.bpj.2023.11.1763.Peer-Reviewed Original ResearchMolecular generationVastness of chemical spaceLearning methodologyActive learning methodologiesDomain of drug discoveryArtificial intelligence modelsChemical spaceGenerative modelIntelligence modelsPython packageDrug discoverySample spaceSmall molecule inhibitorsFDA-approved small molecule inhibitorsMoleculesEfficient methodDomainSoftwareApplicationsMethodologyC-Abl kinaseImplementationSpaceMethod