{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T16:03:23Z","timestamp":1790093003909,"version":"4.0.1"},"reference-count":64,"publisher":"American Chemical Society (ACS)","issue":"37","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2238147"],"award-info":[{"award-number":["2238147"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Predicting monomer reactivity ratios is crucial for controlling monomer sequence distribution in copolymers and their properties. Traditional experimental methods of determining reactivity ratios are time-consuming and resource-intensive, while existing computational methods often struggle with accuracy or scalability. Here, we present a method that combines unsupervised learning with artificial neural networks to predict reactivity ratios in radical copolymerization. By applying spectral clustering to physicochemical features of monomers, we identified three distinct monomer groups with characteristic reactivity patterns. This computationally efficient clustering approach revealed specific monomer group interactions leading to different sequence arrangements including alternating, random, block, and gradient copolymers\u2500providing chemical insights for initial exploration. Building upon these insights, we trained artificial neural networks to achieve quantitative reactivity ratio predictions. We explored two integration strategies: direct feature concatenation and cluster-specific training, which demonstrated performance enhancements for targeted chemical domains compared to general training with equivalent sample sizes. However, models utilizing complete data sets outperformed specialized models trained on focused subsets, revealing a fundamental trade-off between chemical specificity and data availability. This work demonstrates that unsupervised learning offers rapid chemical insight for exploratory analysis, while supervised learning provides the accuracy necessary for final design predictions, with optimal strategies depending on data availability and application requirements.<\/jats:p>","DOI":"10.1021\/acsomega.6c00795","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T10:01:37Z","timestamp":1789120897000},"page":"56104-56118","source":"Crossref","is-referenced-by-count":0,"title":["Chemically Informed Machine Learning Approach for\nPrediction of Reactivity Ratios in Radical Copolymerization"],"prefix":"10.1021","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1382-9582","authenticated-orcid":true,"given":"Habibollah","family":"Safari","sequence":"first","affiliation":[{"name":"University of Nebraska-Lincoln , , , ,","place":["Lincoln, Nebraska, United States, 68588"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7689-773X","authenticated-orcid":true,"given":"Mona","family":"Bavarian","sequence":"additional","affiliation":[{"name":"University of Nebraska-Lincoln , , , ,","place":["Lincoln, Nebraska, United States, 68588"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"issue":"1","key":"2026092210570204800_cit1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1039\/D3LP00213F","article-title":"Radical\nPolymers in Optoelectronic and Spintronic Applications","volume":"2","author":"Yeo","year":"2024","journal-title":"RSC Appl. Polym."},{"issue":"7","key":"2026092210570204800_cit2","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.1021\/acs.jpclett.1c00007","article-title":"Programmable\nSoft-Matter\nElectronics","volume":"12","author":"Ivanov","year":"2021","journal-title":"J. Phys. Chem. Lett."},{"issue":"3","key":"2026092210570204800_cit3","doi-asserted-by":"publisher","first-page":"317","DOI":"10.3390\/pharmaceutics16030317","article-title":"Excipients in Pharmaceutical Additive\nManufacturing: A Comprehensive Exploration of Polymeric Material Selection\nfor Enhanced 3D Printing","volume":"16","author":"Muehlenfeld","year":"2024","journal-title":"Pharmaceutics"},{"key":"2026092210570204800_cit4","doi-asserted-by":"publisher","first-page":"4439","DOI":"10.1021\/acs.iecr.4c03280","article-title":"Design of\nSupported Ionic Liquid\nMembranes for CO2 Capture Using a Generative AI-Based Approach","volume":"64","author":"Ismail","year":"2025","journal-title":"Ind. Eng. Chem. Res."},{"issue":"2","key":"2026092210570204800_cit5","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1038\/s44287-024-00017-w","article-title":"Intelligent Block\nCopolymer Self-Assembly towards IoT Hardware Components","volume":"1","author":"Yang","year":"2024","journal-title":"Nat. Rev. Electr. Eng."},{"issue":"25","key":"2026092210570204800_cit6","doi-asserted-by":"publisher","first-page":"2314337","DOI":"10.1002\/adma.202314337","article-title":"Rational Design\nof New Conjugated Polymers with Main Chain Chirality for Efficient\nOptoelectronic Devices: Carbo[6]Helicene and Indacenodithiophene Copolymers\nas Model Compounds","volume":"36","author":"Gedeon","year":"2024","journal-title":"Adv. Mater."},{"key":"2026092210570204800_cit7","doi-asserted-by":"publisher","first-page":"123119","DOI":"10.1016\/j.polymer.2020.123119","article-title":"Nickel(II)-Pyridinedicarboxamide-Co-Polydimethylsiloxane Complexes as Elastic Self-Healing\nSilicone Materials with Reversible Coordination","volume":"212","author":"Deriabin","year":"2021","journal-title":"Polymer"},{"issue":"15","key":"2026092210570204800_cit8","doi-asserted-by":"publisher","first-page":"2750","DOI":"10.1021\/acs.organomet.1c00392","article-title":"Structural Features of Polymer Ligand\nEnvironments Dramatically Affect the Mechanical and Room-Temperature\nSelf-Healing Properties of Cobalt(II)-Incorporating Polysiloxanes","volume":"40","author":"Deriabin","year":"2021","journal-title":"Organometallics"},{"issue":"12","key":"2026092210570204800_cit9","doi-asserted-by":"publisher","first-page":"5902","DOI":"10.1021\/acs.macromol.4c00613","article-title":"Designer Conjugation-Break\nSpacer That Boosts Charge Transport in Semiconducting Terpolymers","volume":"57","author":"Qiang","year":"2024","journal-title":"Macromolecules"},{"issue":"26","key":"2026092210570204800_cit10","doi-asserted-by":"publisher","first-page":"2314705","DOI":"10.1002\/adfm.202314705","article-title":"Operando Investigation\nof the Molecular Origins of Dipole Switching\nin P(VDF-TrFE-CFE) Terpolymer for Large Adiabatic Temperature Change","volume":"34","author":"Zhu","year":"2024","journal-title":"Adv. Funct. Mater."},{"issue":"10","key":"2026092210570204800_cit11","doi-asserted-by":"publisher","first-page":"1321","DOI":"10.3390\/polym16101321","article-title":"Four-Component Statistical Copolymers by RAFT Polymerization","volume":"16","author":"Vagenas","year":"2024","journal-title":"Polymers"},{"issue":"3","key":"2026092210570204800_cit12","doi-asserted-by":"publisher","first-page":"645","DOI":"10.3390\/polym12030645","article-title":"Novel Polyester Amide Membranes Containing Biquinoline\nUnits and Complex with Cu(I): Synthesis, Characterization, and Approbation\nfor n-Heptane Isolation from Organic Mixtures","volume":"12","author":"Pulyalina","year":"2020","journal-title":"Polymers"},{"issue":"5","key":"2026092210570204800_cit13","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.3390\/polym15051139","article-title":"Non-Covalent\nInteractions in Polymers","volume":"15","author":"Novikov","year":"2023","journal-title":"Polymers"},{"issue":"23","key":"2026092210570204800_cit14","doi-asserted-by":"publisher","first-page":"12847","DOI":"10.1002\/anie.202013189","article-title":"Structural\nAnalysis of an Antigen Chemically Coupled\non Virus-Like Particles in Vaccine Formulation","volume":"60","author":"Jaudzems","year":"2021","journal-title":"Angew. Chem., Int. Ed."},{"issue":"14","key":"2026092210570204800_cit15","doi-asserted-by":"publisher","first-page":"6727","DOI":"10.1021\/acs.macromol.4c00835","article-title":"Accurate\nDetermination of Reactivity Ratios for Copolymerization\nReactions with Reversible Propagation Mechanisms","volume":"57","author":"Lundberg","year":"2024","journal-title":"Macromolecules"},{"key":"2026092210570204800_cit16","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2312.01228","article-title":"Mixed-Integer\nOptimisation of Graph Neural Networks for Computer-Aided Molecular\nDesign","author":"McDonald","year":"2023","journal-title":"arXiv"},{"issue":"3","key":"2026092210570204800_cit17","doi-asserted-by":"publisher","first-page":"459","DOI":"10.3390\/compounds3030034","article-title":"Computer\nModeling and Machine Learning in Chemistry and Materials Science:\nFrom Properties and Reactions of Small Organic and Inorganic Molecules\nto the Smart Design of Polymers and Composites","volume":"3","author":"Novikov","year":"2023","journal-title":"Compounds"},{"issue":"11","key":"2026092210570204800_cit18","doi-asserted-by":"publisher","first-page":"5477","DOI":"10.1021\/acs.macromol.5c00427","article-title":"De Novo Design\nof Polymers with Specified Properties Using Reinforcement Learning","volume":"58","author":"Zhou","year":"2025","journal-title":"Macromolecules"},{"key":"2026092210570204800_cit19","doi-asserted-by":"publisher","first-page":"2559","DOI":"10.1039\/D5DD00239G","article-title":"Toward Sustainable\nPolymer Design: A Molecular Dynamics-Informed Machine Learning Approach\nfor Vitrimers","volume":"4","author":"Zheng","year":"2025","journal-title":"Digital Discovery"},{"issue":"1","key":"2026092210570204800_cit20","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1021\/acs.macromol.4c01822","article-title":"Designing Multicomponent Thermosetting\nResins through Machine Learning and High-Throughput Screening","volume":"58","author":"Zhao","year":"2025","journal-title":"Macromolecules"},{"issue":"2","key":"2026092210570204800_cit21","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1021\/cr60144a001","article-title":"Copolymerization","volume":"46","author":"Mayo","year":"1950","journal-title":"Chem. Rev."},{"issue":"5","key":"2026092210570204800_cit22","doi-asserted-by":"publisher","first-page":"1752","DOI":"10.1002\/aic.14439","article-title":"Demystifying the Estimation of Reactivity\nRatios for Terpolymerization Systems","volume":"60","author":"Kazemi","year":"2014","journal-title":"AIChE\nJ."},{"key":"2026092210570204800_cit23","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1016\/j.eurpolymj.2018.06.021","article-title":"Binary vs. Ternary Reactivity Ratios: Appropriate Estimation\nProcedures with Terpolymerization Data","volume":"105","author":"Scott","year":"2018","journal-title":"Eur.\nPolym. J."},{"key":"2026092210570204800_cit24","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.polymer.2017.05.001","article-title":"Co and Terpolymer Reactivity Ratios\nof Chemically Amplified Resists","volume":"118","author":"Pujari","year":"2017","journal-title":"Polymer"},{"issue":"1","key":"2026092210570204800_cit25","doi-asserted-by":"publisher","first-page":"9","DOI":"10.3390\/pr5010009","article-title":"AMPS\/AAm\/AAc\nTerpolymerization: Experimental\nVerification of the EVM Framework for Ternary Reactivity Ratio Estimation","volume":"5","author":"Scott","year":"2017","journal-title":"Processes"},{"key":"2026092210570204800_cit26","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/0032-3861(62)90089-7","article-title":"The Reactivity\nRatio in Styrene Maleic Anhydride Copolymerization","volume":"3","author":"Huglin","year":"1962","journal-title":"Polymer"},{"issue":"11","key":"2026092210570204800_cit27","doi-asserted-by":"publisher","first-page":"2044","DOI":"10.1021\/ja01227a502","article-title":"The Copolymerization of Styrene and\nMaleic Anhydride","volume":"67","author":"Alfrey","year":"1945","journal-title":"J. Am. Chem. Soc."},{"issue":"1","key":"2026092210570204800_cit28","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1002\/macp.1961.020440145","article-title":"Reactive Fiber. Part I. Copolymerization\nand Copolymer of Acrylonitrile with Glycidyl Methacrylate and with\nGlycidyl Acrylate","volume":"44","author":"Iwakura","year":"1961","journal-title":"Makromol. Chem."},{"issue":"22","key":"2026092210570204800_cit29","doi-asserted-by":"publisher","first-page":"6534","DOI":"10.1039\/C4PY00852A","article-title":"Double Thermoresponsive\nDi- and Triblock Copolymers Based on N-Vinylcaprolactam and N-Vinylpyrrolidone:\nSynthesis and Comparative Study of Solution Behaviour","volume":"5","author":"Kermagoret","year":"2014","journal-title":"Polym. Chem."},{"issue":"11","key":"2026092210570204800_cit30","doi-asserted-by":"publisher","first-page":"3155","DOI":"10.1002\/pola.1990.080281121","article-title":"Radical Copolymerization\nof 2-Ethylacrylic Acid and\nMethacrylic Acid","volume":"28","author":"You","year":"1990","journal-title":"J. Polym. Sci., Part A: Polym.\nChem."},{"issue":"7","key":"2026092210570204800_cit31","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1007\/BF01419313","article-title":"Configuration of Methyl\nMethacrylate-2-Naphthyl\nMethacrylate Copolymers","volume":"259","author":"Simionescu","year":"1981","journal-title":"Colloid Polym. Sci."},{"issue":"44","key":"2026092210570204800_cit32","doi-asserted-by":"publisher","first-page":"52010","DOI":"10.1021\/acsami.3c12050","article-title":"Electrochemical Sensor to Detect Antibiotics in Milk\nBased on Machine Learning Algorithms","volume":"15","author":"Aliev","year":"2023","journal-title":"ACS Appl.\nMater. Interfaces"},{"issue":"43","key":"2026092210570204800_cit33","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.abc6216","article-title":"Targeted Sequence\nDesign within the Coarse-Grained Polymer Genome","volume":"6","author":"Webb","year":"2020","journal-title":"Sci. Adv."},{"issue":"8","key":"2026092210570204800_cit34","doi-asserted-by":"publisher","first-page":"2251","DOI":"10.3390\/biomedicines11082251","article-title":"Machine\nLearning-Based Virtual Screening and Molecular Simulation Approaches\nIdentified Novel Potential Inhibitors for Cancer Therapy","volume":"11","author":"Shahab","year":"2023","journal-title":"Biomedicines"},{"key":"2026092210570204800_cit35","doi-asserted-by":"publisher","first-page":"125866","DOI":"10.1016\/j.polymer.2023.125866","article-title":"Machine Learning Approach to Polymer\nReaction Engineering:\nDetermining Monomers Reactivity Ratios","volume":"275","author":"Nguyen","year":"2023","journal-title":"Polymer"},{"issue":"2\u20133","key":"2026092210570204800_cit36","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1002\/mren.201100065","article-title":"A QM Approach\nto the Calculation of Reactivity Ratios\nin Free-Radical Copolymerization","volume":"6","author":"Dossi","year":"2012","journal-title":"Macromol.\nReact. Eng."},{"issue":"6","key":"2026092210570204800_cit37","doi-asserted-by":"publisher","first-page":"5919","DOI":"10.1021\/acsomega.4c09861","article-title":"Search for Correlations Between the\nResults of the Density Functional Theory and Hartree\u2013Fock Calculations\nUsing Neural Networks and Classical Machine Learning Algorithms","volume":"10","author":"Normatov","year":"2025","journal-title":"ACS Omega"},{"issue":"23","key":"2026092210570204800_cit38","doi-asserted-by":"publisher","first-page":"2779","DOI":"10.1039\/D3PY00246B","article-title":"An Artificial Neural\nNetwork to Predict Reactivity Ratios in Radical Copolymerization","volume":"14","author":"Farajzadehahary","year":"2023","journal-title":"Polym. Chem."},{"key":"2026092210570204800_cit39","doi-asserted-by":"publisher","first-page":"157792","DOI":"10.69997\/sct.116002","article-title":"Enhancing Polymer Reaction\nEngineering Through the\nPower of Machine Learning","volume":"3","author":"Safari","year":"2024","journal-title":"Syst. Control Trans."},{"issue":"10","key":"2026092210570204800_cit40","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1039\/D3PY01372C","article-title":"CoPolDB: A\nCopolymerization Database for Radical Polymerization","volume":"15","author":"Takahashi","year":"2024","journal-title":"Polym. Chem."},{"issue":"4","key":"2026092210570204800_cit41","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1021\/ja01184a066","article-title":"Copolymerization\nIV. Effects of Temperature and Solvents on Monomer Reactivity Ratios","volume":"70","author":"Lewis","year":"1948","journal-title":"J. Am. Chem. Soc."},{"issue":"1","key":"2026092210570204800_cit42","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1002\/(SICI)1099-0518(20000101)38:1&lt;60::AID-POLA8&gt;3.0.CO;2-F","article-title":"Solvent\nEffects on the Free-Radical Copolymerization of Styrene with Butyl\nAcrylate. I. Monomer Reactivity Ratios","volume":"38","author":"Ferna\u0301ndez-Garci\u0301a","year":"2000","journal-title":"J. Polym.\nSci., Part A: polym. Chem."},{"issue":"1","key":"2026092210570204800_cit43","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1002\/(SICI)1099-0518(20000101)38:1&lt;220::AID-POLA27&gt;3.0.CO;2-P","article-title":"Free-Radical\nCopolymerization of\nVinyl Esters Using Fluoroalcohols as Solvents: The Solvent Effect\non the Monomer Reactivity Ratio","volume":"38","author":"Yamada","year":"2000","journal-title":"J. Polym. Sci.,\nPart A: Polym. Chem."},{"issue":"4","key":"2026092210570204800_cit44","doi-asserted-by":"publisher","first-page":"4045","DOI":"10.1002\/app.24328","article-title":"Monomer Apparent\nReactivity Ratios for Acrylonitrile\/Methyl Vinyl Ketone Copolymerization\nSystem","volume":"102","author":"Hou","year":"2006","journal-title":"J. Appl. Polym. Sci"},{"issue":"25","key":"2026092210570204800_cit45","doi-asserted-by":"publisher","first-page":"2598","DOI":"10.1039\/D4PY00009A","article-title":"Quantitative Comparison\nof the Copolymerisation Kinetics\nin Catalyst-Transfer Copolymerisation to Synthesise Polythiophenes","volume":"15","author":"He","year":"2024","journal-title":"Polym. Chem."},{"key":"2026092210570204800_cit46","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.eurpolymj.2017.11.022","article-title":"Effect of\nthe Charge Interactions on the Composition Behavior of Acrylamide\/Acrylic\nAcid Copolymerization in Aqueous Medium","volume":"98","author":"Fischer","year":"2018","journal-title":"Eur.\nPolym. J."},{"key":"2026092210570204800_cit47","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.jcis.2024.10.040","article-title":"Alkyne-Rich Patchy\nPolymer Colloids Prepared by Surfactant-Free Emulsion Polymerization","volume":"679","author":"Morgenthaler","year":"2025","journal-title":"J. Colloid Interface Sci."},{"issue":"140","key":"2026092210570204800_cit48","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1002\/pol.1960.1204214016","article-title":"Tracer Techniques\nfor the Determination of Monomer\nReactivity Ratios. II. Monomer Reactivity Ratios in Copolymerizations\nwith Divinyl Monomers","volume":"42","author":"Wiley","year":"1960","journal-title":"J. Polym. Sci."},{"issue":"1","key":"2026092210570204800_cit49","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1002\/pol.1965.100030128","article-title":"Copolymerization\nof Styrene and P-Divinylbenzene. Initial Rates and Gel Points","volume":"3","author":"Storey","year":"1965","journal-title":"J. Polym. Sci., Part A"},{"key":"2026092210570204800_cit50","doi-asserted-by":"publisher","first-page":"107523","DOI":"10.1016\/j.csda.2022.107523","article-title":"Clustering,\nMulticollinearity, and Singular Vectors","volume":"173","author":"Usefi","year":"2022","journal-title":"Comput.\nStat. Data. Anal"},{"key":"2026092210570204800_cit51","first-page":"281","article-title":"Some Methods for\nClassification and Analysis of Multivariate Observations","volume-title":"Proceedings of the Fifth Berkeley Symposium on Mathematical\nStatistics and Probability, Volume 1: Statistics","author":"MacQueen","year":"1967"},{"key":"2026092210570204800_cit52","first-page":"14","article-title":"On Spectral Clustering: Analysis\nand an Algorithm","volume-title":"Advances in Neural Information\nProcessing Systems","author":"Ng","year":"2001"},{"issue":"301","key":"2026092210570204800_cit53","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1080\/01621459.1963.10500845","article-title":"Hierarchical Grouping to Optimize an Objective Function","volume":"58","author":"Ward","year":"1963","journal-title":"J. Am. Stat. Assoc."},{"issue":"1","key":"2026092210570204800_cit54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum\nLikelihood from Incomplete\nData via the EM Algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. R. Stat. Soc.\nSer. B Methodol"},{"issue":"2","key":"2026092210570204800_cit55","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1145\/235968.233324","article-title":"BIRCH: An Efficient Data Clustering\nMethod for Very Large Databases","volume":"25","author":"Zhang","year":"1996","journal-title":"SIGMOD Rec."},{"key":"2026092210570204800_cit56","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes:\nA Graphical Aid to the Interpretation and Validation of Cluster Analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."},{"issue":"1","key":"2026092210570204800_cit57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/03610927408827101","article-title":"A Dendrite Method for Cluster Analysis","volume":"3","author":"Calinski","year":"1974","journal-title":"Commun. Stat. - Theory Methods"},{"issue":"2","key":"2026092210570204800_cit58","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","article-title":"A Cluster Separation\nMeasure","volume":"PAMI-1","author":"Davies","year":"1979","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2065","key":"2026092210570204800_cit59","doi-asserted-by":"publisher","first-page":"20150202","DOI":"10.1098\/rsta.2015.0202","article-title":"Principal Component Analysis: A Review and Recent Developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos. Trans. R. Soc. Math. Phys. Eng. Sci."},{"issue":"86","key":"2026092210570204800_cit60","first-page":"2579","article-title":"Visualizing Data Using T-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"2026092210570204800_cit61","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1802.03426","article-title":"UMAP: Uniform Manifold\nApproximation\nand Projection for Dimension Reduction","author":"McInnes","year":"2020","journal-title":"arXiv"},{"issue":"2","key":"2026092210570204800_cit62","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1021\/c160017a018","article-title":"The Generation\nof a Unique Machine Description for Chemical Structures-A Technique\nDeveloped at Chemical Abstracts Service","volume":"5","author":"Morgan","year":"1965","journal-title":"J.\nChem. Doc."},{"issue":"5","key":"2026092210570204800_cit63","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","article-title":"Extended-Connectivity Fingerprints","volume":"50","author":"Rogers","year":"2010","journal-title":"J. Chem.\nInf. Model"},{"issue":"11","key":"2026092210570204800_cit64","doi-asserted-by":"publisher","first-page":"9465","DOI":"10.1021\/acsomega.1c06805","article-title":"Improving Compound Activity Classification\nvia Deep Transfer and Representation Learning","volume":"7","author":"Dey","year":"2022","journal-title":"ACS Omega"}],"container-title":["ACS Omega"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acsomega.6c00795","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/acsodf\/article-pdf\/11\/37\/56104\/68480758\/acsomega.6c00795.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/acsodf\/article-pdf\/11\/37\/56104\/68480758\/acsomega.6c00795.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T15:23:15Z","timestamp":1790090595000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/acsodf\/article\/11\/37\/56104\/5428193\/Chemically-Informed-Machine-Learning-Approach-for"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,11]]},"references-count":64,"journal-issue":{"issue":"37","published-online":{"date-parts":[[2026,9,11]]},"published-print":{"date-parts":[[2026,9,22]]}},"URL":"https:\/\/doi.org\/10.1021\/acsomega.6c00795","relation":{},"ISSN":["2470-1343"],"issn-type":[{"value":"2470-1343","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,11]]}}}