{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T01:49:11Z","timestamp":1791856151243,"version":"4.3.4"},"reference-count":70,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T00:00:00Z","timestamp":1652313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772023"],"award-info":[{"award-number":["61772023"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019QY1803"],"award-info":[{"award-number":["2019QY1803"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021H6015"],"award-info":[{"award-number":["2021H6015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["61772023"],"award-info":[{"award-number":["61772023"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019QY1803"],"award-info":[{"award-number":["2019QY1803"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021H6015"],"award-info":[{"award-number":["2021H6015"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fujian Science and Technology Plan Industry-University-Research Cooperation Project","award":["61772023"],"award-info":[{"award-number":["61772023"]}]},{"name":"Fujian Science and Technology Plan Industry-University-Research Cooperation Project","award":["2019QY1803"],"award-info":[{"award-number":["2019QY1803"]}]},{"name":"Fujian Science and Technology Plan Industry-University-Research Cooperation Project","award":["2021H6015"],"award-info":[{"award-number":["2021H6015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Molecules"],"abstract":"<jats:p>In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.<\/jats:p>","DOI":"10.3390\/molecules27103112","type":"journal-article","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T21:46:53Z","timestamp":1652392013000},"page":"3112","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8983-9686","authenticated-orcid":false,"given":"Bowei","family":"Yan","sequence":"first","affiliation":[{"name":"Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China"},{"name":"Department of Biotechnology, Institute of Radiation Medicine, Beijing 100850, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaona","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen 361005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Medicine, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4232-038X","authenticated-orcid":false,"given":"Junshan","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China"},{"name":"Department of Biotechnology, Institute of Radiation Medicine, Beijing 100850, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianlian","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4136-6151","authenticated-orcid":false,"given":"Song","family":"He","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China"},{"name":"Department of Biotechnology, Institute of Radiation Medicine, Beijing 100850, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1222-8876","authenticated-orcid":false,"given":"Kunhong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen 361005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaochen","family":"Bo","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China"},{"name":"Department of Biotechnology, Institute of Radiation Medicine, Beijing 100850, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1038\/s41573-019-0048-x","article-title":"Managing the challenge of drug-induced liver injury: A roadmap for the development and deployment of preclinical predictive models","volume":"19","author":"Blomme","year":"2020","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1093\/bib\/bbz165","article-title":"Predicting drug-induced hepatotoxicity based on biological feature maps and diverse classification strategies","volume":"22","author":"Su","year":"2021","journal-title":"Brief. Bioinform."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"120529","DOI":"10.1016\/j.biomaterials.2020.120529","article-title":"Generation of uniform liver spheroids from human pluripotent stem cells for imaging-based drug toxicity analysis","volume":"269","author":"Lee","year":"2021","journal-title":"Biomaterials"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.2174\/15680266113139990033","article-title":"In silico ADMET prediction: Recent advances, current challenges and future trends","volume":"13","author":"Cheng","year":"2013","journal-title":"Curr. Top. Med. Chem."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1021\/acs.chemrestox.8b00054","article-title":"Advancing Predictive Hepatotoxicity at the Intersection of Experimental, in Silico, and Artificial Intelligence Technologies","volume":"31","author":"Fraser","year":"2018","journal-title":"Chem. Res. Toxicol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2085","DOI":"10.1021\/acs.jcim.5b00238","article-title":"Deep Learning for Drug-Induced Liver Injury","volume":"55","author":"Xu","year":"2015","journal-title":"J. Chem. Inf. Model."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1021\/acs.chemrestox.5b00465","article-title":"Computational Models for Human and Animal Hepatotoxicity with a Global Application Scope","volume":"29","author":"Mulliner","year":"2016","journal-title":"Chem. Res. Toxicol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"He, S., Ye, T., Wang, R., Zhang, C., Zhang, X., Sun, G., and Sun, X. (2019). An In Silico Model for Predicting Drug-Induced Hepatotoxicity. Int. J. Mol. Sci., 20.","DOI":"10.3390\/ijms20081897"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xiao, Q., Chen, P., and Wang, B. (2019). In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method. Int. J. Mol. Sci., 20.","DOI":"10.3390\/ijms20174106"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1093\/toxsci\/kfy121","article-title":"Predicting Drug-Induced Liver Injury Using Ensemble Learning Methods and Molecular Fingerprints","volume":"165","author":"Ai","year":"2018","journal-title":"Toxicol. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"638410","DOI":"10.3389\/frai.2021.638410","article-title":"The Promise of AI for DILI Prediction","volume":"4","author":"Vall","year":"2021","journal-title":"Front. Artif. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1038\/s42256-021-00418-8","article-title":"Geometric deep learning on molecular representations","volume":"3","author":"Atz","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8749","DOI":"10.1021\/acs.jmedchem.9b00959","article-title":"Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism","volume":"63","author":"Xiong","year":"2020","journal-title":"J. Med. Chem."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-020-00479-8","article-title":"Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models","volume":"13","author":"Jiang","year":"2021","journal-title":"J. Cheminform."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1021\/acs.chemrestox.0c00322","article-title":"Deep Graph Learning with Property Augmentation for Predicting Drug-Induced Liver Injury","volume":"34","author":"Ma","year":"2021","journal-title":"Chem. Res. Toxicol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6924","DOI":"10.1021\/acs.jmedchem.1c00421","article-title":"Mining Toxicity Information from Large Amounts of Toxicity Data","volume":"64","author":"Wu","year":"2021","journal-title":"J. Med. Chem."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13062-020-00288-x","article-title":"An ensemble learning approach for modeling the systems biology of drug-induced injury","volume":"16","author":"Pinero","year":"2021","journal-title":"Biol. Direct"},{"key":"ref_18","unstructured":"Holland, J.H. (1975). Adaptation in Natural And Artificial Systems, University of Michigan Press."},{"key":"ref_19","unstructured":"Rechenberg, I. (1965). Cybernetic solution path of an experimental problem. Royal Aircraft Establishment Translation 1122, IEEE Press."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/4235.585888","article-title":"Evolutionary computation: Comments on the history and current state","volume":"1","author":"Thomas","year":"1997","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_21","unstructured":"Davis, L.D. (1991). Handbook of genetic algorithms. Handbook of Genetic Algorithms, Van Nostrand Reinhold."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s12293-021-00350-9","article-title":"The Design of Evolutionary Feature Selection Operator for the Micro-expression Recognition","volume":"14","author":"Zhan","year":"2022","journal-title":"Memetic Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1016\/j.patcog.2009.01.021","article-title":"Ensemble component selection for improving ICA based microarray data prediction models","volume":"42","author":"Liu","year":"2009","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1093\/bioinformatics\/btn644","article-title":"A genetic programming-based approach to the classification of multiclass microarray datasets","volume":"25","author":"Liu","year":"2009","journal-title":"Bioinformatics"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"100564","DOI":"10.1016\/j.swevo.2019.100564","article-title":"A novel Error-Correcting Output Codes algorithm based on genetic programming","volume":"50","author":"Li","year":"2019","journal-title":"Swarm Evol. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107642","DOI":"10.1016\/j.patcog.2020.107642","article-title":"A Novel Error-Correcting Output Codes Based on Genetic Programming and Ternary Digit Operators","volume":"110","author":"Liang","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.ins.2020.05.088","article-title":"A Ternary Bitwise Calculator Based Genetic Algorithm for Improving Error Correcting Output Codes","volume":"537","author":"Ye","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"100709","DOI":"10.1016\/j.swevo.2020.100709","article-title":"A Novel Multi-Objective Genetic Algorithm Based Error Correcting Output Codes","volume":"57","author":"Zhang","year":"2020","journal-title":"Swarm Evol. Comput."},{"key":"ref_29","first-page":"T94","article-title":"HARD: Bit-Split String Matching Using a Heuristic Algorithm to Reduce Memory Demand","volume":"23","author":"Li","year":"2020","journal-title":"Rom. J. Inf. Sci. Technol."},{"key":"ref_30","first-page":"141","article-title":"Gravitational Search Algorithm-Based Tuning of Fuzzy Control Systems with a Reduced Parametric Sensitivity","volume":"96","author":"Precup","year":"2011","journal-title":"Soft Comput. Ind. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.ins.2021.11.051","article-title":"Policy Iteration Reinforcement Learning-based control using a Grey Wolf Optimizer algorithm","volume":"585","author":"Zamfirache","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.swevo.2018.03.002","article-title":"A constructive evolutionary approach for feature selection in unsupervised learning","volume":"42","author":"Martarelli","year":"2018","journal-title":"Swarm Evol. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1016\/j.compbiomed.2013.03.010","article-title":"An ensemble of SVM classifiers based on gene pairs","volume":"43","author":"Tong","year":"2013","journal-title":"Comput. Biol. Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.eswa.2019.06.056","article-title":"Automatic Clustering by Multi-Objective Genetic Algorithm with Numeric and Categorical Features","volume":"137","author":"Dutta","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"107528","DOI":"10.1016\/j.apacoust.2020.107528","article-title":"The detection of Parkinson disease using the genetic algorithm and SVM classifier","volume":"171","author":"Soumaya","year":"2021","journal-title":"Appl. Acoust."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1016\/j.procs.2017.11.283","article-title":"Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks","volume":"120","author":"Uyar","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1007\/s12553-019-00375-8","article-title":"Cervical cancer prognosis using genetic algorithm and adaptive boosting approach","volume":"9","author":"Sharma","year":"2019","journal-title":"Health Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"406","DOI":"10.5001\/omj.2015.82","article-title":"The Applications of Genetic Algorithms in Medicine","volume":"30","author":"Ghaheri","year":"2015","journal-title":"Oman Med. J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"110","DOI":"10.19101\/IJACR.2018.836007","article-title":"Decision tree-based expert system for adverse drug reaction detection using fuzzy logic and genetic algorithm","volume":"8","author":"Mansour","year":"2018","journal-title":"Int. J. Adv. Comput. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-020-00429-4","article-title":"AutoGrow4: An open-source genetic algorithm for de novo drug design and lead optimization","volume":"12","author":"Spiegel","year":"2020","journal-title":"J. Cheminforma."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.asoc.2014.09.042","article-title":"Evolutionary algorithms for de novo drug design\u2014A survey","volume":"27","author":"Devi","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"445","DOI":"10.2174\/1573409916666200620194143","article-title":"Multi-objective Genetic Algorithm for De Novo Drug Design (MoGADdrug)","volume":"17","author":"Devi","year":"2021","journal-title":"Curr. Comput.-Aided Drug Des."},{"key":"ref_43","first-page":"601","article-title":"Cancer classification using Rotation Forest. Comput","volume":"38","author":"Liu","year":"2008","journal-title":"Biol. Med."},{"key":"ref_44","unstructured":"Landrumetal, G. (2022, April 01). Rdkit: Open-Source Cheminformatics. Available online: http:\/\/www.rdkit.org\/."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1021\/tx1000865","article-title":"Developing Structure-Activity Relationships for the Prediction of Hepatotoxicity","volume":"23","author":"Greene","year":"2010","journal-title":"Chem. Res. Toxicol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1016\/j.drudis.2016.02.015","article-title":"DILIrank: The largest reference drug list ranked by the risk for developing drug-induced liver injury in humans","volume":"21","author":"Chen","year":"2016","journal-title":"Drug Discov. Today"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1002\/hep.26175","article-title":"LiverTox: A website on drug-induced liver injury","volume":"57","author":"Hoofnagle","year":"2013","journal-title":"Hepatology"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1038\/clpt.2013.16","article-title":"The Liver Toxicity Knowledge Base: A Systems Approach to a Complex End Point","volume":"93","author":"Chen","year":"2013","journal-title":"Clin. Pharmacol. Ther."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Bajzelj, B., and Drgan, V. (2020). Hepatotoxicity Modeling Using Counter-Propagation Artificial Neural Networks: Handling an Imbalanced Classification Problem. Molecules, 25.","DOI":"10.3390\/molecules25030481"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1093\/toxsci\/kfaa005","article-title":"Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data","volume":"174","author":"Zhao","year":"2020","journal-title":"Toxicol. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-015-0053-y","article-title":"Data-driven identification of structural alerts for mitigating the risk of drug-induced human liver injuries","volume":"7","author":"Liu","year":"2015","journal-title":"J. Cheminform."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1021\/ci034160g","article-title":"Random forest: A classification and regression tool for compound classification and QSAR modeling","volume":"43","author":"Svetnik","year":"2003","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1063\/1.3516299","article-title":"Applications of Support Vector Machines In Chemo And Bioinformatics","volume":"1298","author":"Jayaraman","year":"2010","journal-title":"AIP Conf. Proc."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2353","DOI":"10.1021\/acs.jcim.6b00591","article-title":"Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships","volume":"56","author":"Sheridan","year":"2016","journal-title":"J. Chem. Inf. Model."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1021\/ci025501d","article-title":"Structure-based classification of antibacterial activity","volume":"42","author":"Cronin","year":"2002","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"128459","DOI":"10.1016\/j.molstruc.2020.128459","article-title":"A machine learning approach to discriminate MR1 binders: The importance of the phenol and carbonyl fragments","volume":"1217","author":"Shamsara","year":"2020","journal-title":"J. Mol. Struct."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"8373","DOI":"10.1039\/D0CP00305K","article-title":"Are 2D fingerprints still valuable for drug discovery?","volume":"22","author":"Gao","year":"2020","journal-title":"Phys. Chem. Chem. Phys."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"5545","DOI":"10.1093\/bioinformatics\/btaa1005","article-title":"DeepPurpose: A deep learning library for drug-target interaction prediction","volume":"36","author":"Huang","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"27233","DOI":"10.1021\/acsomega.1c04017","article-title":"DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science","volume":"6","author":"Li","year":"2021","journal-title":"Acs Omega"},{"key":"ref_62","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"bbaa321","DOI":"10.1093\/bib\/bbaa321","article-title":"Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets","volume":"22","author":"Wu","year":"2021","journal-title":"Brief. Bioinform."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1021\/ci400187y","article-title":"Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules","volume":"53","author":"Lusci","year":"2013","journal-title":"J. Chem. Inf. Model."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1021\/ci800038f","article-title":"Mold(2), molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics","volume":"48","author":"Hong","year":"2008","journal-title":"J. Chem. Inf. Model."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1002\/jcc.21707","article-title":"PaDEL-Descriptor: An Open Source Software to Calculate Molecular Descriptors and Fingerprints","volume":"32","author":"Yap","year":"2011","journal-title":"J. Comput. Chem."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1093\/toxsci\/kft189","article-title":"Quantitative Structure-Activity Relationship Models for Predicting Drug-Induced Liver Injury Based on FDA-Approved Drug Labeling Annotation and Using a Large Collection of Drugs","volume":"136","author":"Chen","year":"2013","journal-title":"Toxicol. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1007\/s10822-011-9468-3","article-title":"Mixed learning algorithms and features ensemble in hepatotoxicity prediction","volume":"25","author":"Liew","year":"2011","journal-title":"J. Comput. Aid. Mol. Des."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1093\/toxsci\/kfn109","article-title":"Cellular imaging predictions of clinical drug-induced liver injury","volume":"105","author":"Xu","year":"2008","journal-title":"Toxicol. Sci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"5054","DOI":"10.1021\/acs.jcim.1c00589","article-title":"Pharmacoprint: A Combination of a Pharmacophore Fingerprint and Artificial Intelligence as a Tool for Computer-Aided Drug Design","volume":"61","author":"Warszycki","year":"2021","journal-title":"J. Chem. Inf. Model."}],"container-title":["Molecules"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1420-3049\/27\/10\/3112\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:09:56Z","timestamp":1760137796000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1420-3049\/27\/10\/3112"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,12]]},"references-count":70,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["molecules27103112"],"URL":"https:\/\/doi.org\/10.3390\/molecules27103112","relation":{},"ISSN":["1420-3049"],"issn-type":[{"value":"1420-3049","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,12]]}}}