2024
Kernel-elastic autoencoder for molecular design
Li H, Shee Y, Allen B, Maschietto F, Morgunov A, Batista V. Kernel-elastic autoencoder for molecular design. PNAS Nexus 2024, 3: pgae168. PMID: 38689710, PMCID: PMC11059255, DOI: 10.1093/pnasnexus/pgae168.Peer-Reviewed Original ResearchMaximum mean discrepancyMean discrepancyTransformer architectureCondition generatorWeighted reconstructionTraining datasetGenerative modelGeneration approachDocking applicationsMolecular designAutoencoderAccurate reconstructionVAESpectrum of applicationsAutoDock VinaEnhanced performanceDesignDatasetArchitectureGeneration performanceBenchmarksApplicationsGlide scoreReconstructionGeneration behavior
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