2023
Chemical representation learning for toxicity prediction
Born J, Markert G, Janakarajan N, Kimber T, Volkamer A, Martínez M, Manica M. Chemical representation learning for toxicity prediction. Digital Discovery 2023, 2: 674-691. DOI: 10.1039/d2dd00099g.Peer-Reviewed Original ResearchChemical language modelsLanguage modelMolecular property prediction tasksMolecular property prediction modelProperty prediction tasksMolecular property predictionExplicit supervisionAttention weightsMultiscale convolutionData augmentationPrediction taskToxicity datasetMolecular representationsProperty prediction modelsImproved accuracyModel reliabilityDatasetProperty predictionChemical representationsToxicity predictionPrediction uncertaintyUncertainty estimationDrug discoveryRepresentationPrediction model
2019
Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders
Manica M, Oskooei A, Born J, Subramanian V, Sáez-Rodríguez J, Martínez M. Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders. Molecular Pharmaceutics 2019, 16: 4797-4806. PMID: 31618586, DOI: 10.1021/acs.molpharmaceut.9b00520.Peer-Reviewed Original ResearchConceptsConvolutional encoderReceptor tyrosine kinasesProtein-protein interaction networkAttention-based encoderStructural similarity indexSelection of encodingDrug designDrug sensitivity predictionGene expression profilesIn silico predictionSensitivity predictionAttention weightsLeukemia cell linesSMILES sequencesInformative genesGene expression profiles of tumorsApoptotic processInteraction networkExpression profiles of tumorsBaseline modelIntracellular interactionsEncodingTyrosine kinaseDevelopment of personalized therapiesGenes