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 approachReactantsRetrosynthesisInterconversionCompoundsKernel-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