2024
Expanding PFAS Identification with Transformation Product Libraries: Nontargeted Analysis Reveals Biotransformation Products in Mice
Liu S, Dukes D, Koelmel J, Stelben P, Finch J, Okeme J, Lowe C, Williams A, Godri D, Rennie E, Parry E, McDonough C, Pollitt K. Expanding PFAS Identification with Transformation Product Libraries: Nontargeted Analysis Reveals Biotransformation Products in Mice. Environmental Science And Technology 2024, 59: 119-131. PMID: 39704186, PMCID: PMC12097807, DOI: 10.1021/acs.est.4c07750.Peer-Reviewed Original ResearchConceptsMass spectral libraryTransformation productsLiquid chromatography-high resolution mass spectrometryProduct libraryPolyfluoroalkyl substancesBiological transformation productsSpectral libraryFragmentation rulesBehavior of PFASPotential transformation productsMass spectrometryToxicity predictionChemical subclassesReaction productsLiver S9 fractionBiotransformation productsEnzymatic reactionsMutagenic toxicityReactionMouse liver S9 fractionBiological systemsS9 fractionDealkylationChemicalSpectrometryQuantum-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 design
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
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