2025
Filling the Gaps in PFAS Detection: Integrating GC-MS Non-Targeted Analysis for Comprehensive Environmental Monitoring and Exposure Assessment
Newton S, Bowden J, Charest N, Jackson S, Koelmel J, Liberatore H, Lin A, Lowe C, Nieto S, Pollitt K, Robuck A, Rostkowski P, Townsend T, Wallace M, Williams A. Filling the Gaps in PFAS Detection: Integrating GC-MS Non-Targeted Analysis for Comprehensive Environmental Monitoring and Exposure Assessment. Environmental Science & Technology Letters 2025, 12: 104-112. PMID: 40206203, PMCID: PMC11977685, DOI: 10.1021/acs.estlett.4c00930.Peer-Reviewed Original ResearchNon-target analysisLC-ESI-MSLC-MSNovel PFASsChromatography-mass spectrometryNTA studiesGC-MSTransformation productsPFAS structuresPFAS chemistryLiquid chromatography-mass spectrometryPolyfluoroalkyl substancesGC-MS methodPFAS researchGas chromatography-mass spectrometryLC-MS analysisChemical spaceParent compoundAqueous filmSpectrometryPFAS detectionsChemicalSources of PFASsCompoundsChemistry
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 approachReactantsRetrosynthesisInterconversionCompoundsHigh-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage
Lai Y, Koelmel J, Walker D, Price E, Papazian S, Manz K, Castilla-Fernández D, Bowden J, Nikiforov V, David A, Bessonneau V, Amer B, Seethapathy S, Hu X, Lin E, Jbebli A, McNeil B, Barupal D, Cerasa M, Xie H, Kalia V, Nandakumar R, Singh R, Tian Z, Gao P, Zhao Y, Froment J, Rostkowski P, Dubey S, Coufalíková K, Seličová H, Hecht H, Liu S, Udhani H, Restituito S, Tchou-Wong K, Lu K, Martin J, Warth B, Pollitt K, Klánová J, Fiehn O, Metz T, Pennell K, Jones D, Miller G. High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage. Environmental Science And Technology 2024, 58: 12784-12822. PMID: 38984754, PMCID: PMC11271014, DOI: 10.1021/acs.est.4c01156.Peer-Reviewed Original ResearchHigh-resolution mass spectrometryChemical spaceCoverage of chemical spaceMass spectrometryAccurate mass measurementsChemical space coverageNon-target approachHuman exposomeAnalytical workflowMass measurementsDisease outcomeGenetic driversSpectrometryHarmonized workflowMulti-omics integrationRetrospective validationCare providersChemistsMetabolomicsSpace coverageBiomoleculesHypothesis-drivenDiseaseQuantum-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 modelElectronChemically Recyclable Unnatural (1→6)-Polysaccharides from Cellulose-Derived Levoglucosenone and Dihydrolevoglucosenone
Mizukami Y, Kakehi Y, Li F, Yamamoto T, Tajima K, Isono T, Satoh T. Chemically Recyclable Unnatural (1→6)-Polysaccharides from Cellulose-Derived Levoglucosenone and Dihydrolevoglucosenone. ACS Macro Letters 2024, 13: 252-259. PMID: 38334272, DOI: 10.1021/acsmacrolett.3c00720.Peer-Reviewed Original ResearchClosed-loop chemical recyclingTransparent self-standing filmsCationic ring-opening polymerizationRing-opening polymerizationSelf-standing filmsUnnatural polysaccharidesSubstituent patternChemical spaceAcid catalystMonomer synthesisSynthetic complexesChemical synthesisPolymer materialsLevoglucosenoneThermal stabilityPolymerization kineticsChemical recyclingAmbient conditionsCellulose-derivativesAmorphous solidsDihydrolevoglucosenonePolymerizationMonomerPolymerMaterial propertiesChemSpaceAL: 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 kinaseImplementationSpaceMethodData Science-Enabled Palladium-Catalyzed Enantioselective Aryl-Carbonylation of Sulfonimidamides
van Dijk L, Haas B, Lim N, Clagg K, Dotson J, Treacy S, Piechowicz K, Roytman V, Zhang H, Toste F, Miller S, Gosselin F, Sigman M. Data Science-Enabled Palladium-Catalyzed Enantioselective Aryl-Carbonylation of Sulfonimidamides. Journal Of The American Chemical Society 2023, 145: 20959-20967. PMID: 37656964, DOI: 10.1021/jacs.3c06674.Peer-Reviewed Original ResearchCross-coupling methodsHeteroaryl iodidesLigand descriptorsExcellent yieldsCoupling partnersChemical spaceMedicinal chemistrySulfonimidamidesAgrochemical discoveryVirtual libraryReaction optimizationOptimal conditionsEfficient strategyHeteroarylScience techniquesEnantioselectivityArylCatalystIodideReactionChemistryData science techniquesYieldDescriptorsDiverse setNon-targeted analysis (NTA) and suspect screening analysis (SSA): a review of examining the chemical exposome
Manz K, Feerick A, Braun J, Feng Y, Hall A, Koelmel J, Manzano C, Newton S, Pennell K, Place B, Godri Pollitt K, Prasse C, Young J. Non-targeted analysis (NTA) and suspect screening analysis (SSA): a review of examining the chemical exposome. Journal Of Exposure Science & Environmental Epidemiology 2023, 33: 524-536. PMID: 37380877, PMCID: PMC10403360, DOI: 10.1038/s41370-023-00574-6.Peer-Reviewed Original ResearchConceptsHigh-resolution mass spectrometryNon-targeted analysisExposure mediaChemical spaceSoils/sedimentsMass spectrometryPolyfluoroalkyl substancesSemi-volatile organic compoundsChemical exposomePolyaromatic hydrocarbonsLiquid chromatographyEnvironmental mediaOrganic compoundsGC-HRMSHuman exposureLC-HRMSAnalytical platformConsumer productsChemical exposureSedimentsPesticidesEnvironmental chemicalsChemicalsExposure sourcesSpectrometry
2022
Bespoke library docking for 5-HT2A receptor agonists with antidepressant activity
Kaplan AL, Confair DN, Kim K, Barros-Álvarez X, Rodriguiz RM, Yang Y, Kweon OS, Che T, McCorvy JD, Kamber DN, Phelan JP, Martins LC, Pogorelov VM, DiBerto JF, Slocum ST, Huang XP, Kumar JM, Robertson MJ, Panova O, Seven AB, Wetsel AQ, Wetsel WC, Irwin JJ, Skiniotis G, Shoichet BK, Roth BL, Ellman JA. Bespoke library docking for 5-HT2A receptor agonists with antidepressant activity. Nature 2022, 610: 582-591. PMID: 36171289, PMCID: PMC9996387, DOI: 10.1038/s41586-022-05258-z.Peer-Reviewed Original ResearchConceptsRelevant chemical spaceVirtual libraryStructure-based dockingStructure-based optimizationLow micromolar activityChemical spaceInitial moleculesLigand discoveryAminergic G protein-coupled receptorsPot CChemical librariesAvailable derivativesSynthesizable moleculesFavorable physical propertiesH alkenylationMoleculesHalf-maximal effective concentration (EC50) valuesStructural analysisPsychedelic activityPhysical propertiesDockingHigh brain permeabilityEffective concentration valuesBrain permeabilityConsiderable interest
2016
Assessment of predictive models for estimating the acute aquatic toxicity of organic chemicals
Melnikov F, Kostal J, Voutchkova-Kostal A, Zimmerman J, Anastas P. Assessment of predictive models for estimating the acute aquatic toxicity of organic chemicals. Green Chemistry 2016, 18: 4432-4445. DOI: 10.1039/c6gc00720a.Peer-Reviewed Original ResearchAcute aquatic toxicityAquatic toxicityToxicity data gapsAppropriate model selectionChemical classesModel selectionSpecific chemical classesData gapsStatistical algorithmsToxicity modelChemical spaceModel applicability domainApplicability domainOrganic chemicalsMost modelsData setsChemical propertiesStructural descriptors
2011
Small-molecule hydrophobic tagging–induced degradation of HaloTag fusion proteins
Neklesa TK, Tae HS, Schneekloth AR, Stulberg MJ, Corson TW, Sundberg TB, Raina K, Holley SA, Crews CM. Small-molecule hydrophobic tagging–induced degradation of HaloTag fusion proteins. Nature Chemical Biology 2011, 7: 538-543. PMID: 21725302, PMCID: PMC3139752, DOI: 10.1038/nchembio.597.Peer-Reviewed Original Research
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