{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T10:34:25Z","timestamp":1766486065996,"version":"build-2065373602"},"reference-count":115,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T00:00:00Z","timestamp":1745539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PT national funds (FCT\/MCTES, Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia and Minist\u00e9rio da Ci\u00eancia, Tecnologia e Ensino Superior)","award":["UID\/50006"],"award-info":[{"award-number":["UID\/50006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CIMB"],"abstract":"<jats:p>Cancers constitute a group of biological complex diseases, which are associated with great prevalence and mortality. These medical conditions are very difficult to tackle due to their multi-factorial nature, which includes their ability to evade the immune system and become resistant to current anticancer agents. There is a pressing need to search for novel anticancer agents with multi-target modes of action and\/or multi-cell inhibition versatility, which can translate into more efficacious and safer chemotherapeutic treatments. Computational methods are of paramount importance to accelerate multi-target drug discovery in cancer research but most of them have several disadvantages such as the use of limited structural information through homogeneous datasets of chemicals, the prediction of activity against a single target, and\/or lack of interpretability. This mini-review discusses the emergence, development, and application of perturbation-theory machine learning (PTML) as a cutting-edge approach capable of overcoming the aforementioned limitations in the context of multi-target small molecule anticancer discovery. Here, we analyze the most promising investigations on PTML modeling spanning over a decade to enable the discovery of versatile anticancer agents. We highlight the potential of the PTML approach for the modeling of multi-target anticancer activity while envisaging future applications of PTML modeling.<\/jats:p>","DOI":"10.3390\/cimb47050301","type":"journal-article","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T08:02:57Z","timestamp":1745568177000},"page":"301","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Perturbation-Theory Machine Learning for Multi-Target Drug Discovery in Modern Anticancer Research"],"prefix":"10.3390","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1928-853X","authenticated-orcid":false,"given":"Valeria V.","family":"Kleandrova","sequence":"first","affiliation":[{"name":"LAQV@REQUIMTE\/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3375-8670","authenticated-orcid":false,"given":"M. Nat\u00e1lia D. S.","family":"Cordeiro","sequence":"additional","affiliation":[{"name":"LAQV@REQUIMTE\/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9544-9016","authenticated-orcid":false,"given":"Alejandro","family":"Speck-Planche","sequence":"additional","affiliation":[{"name":"LAQV@REQUIMTE\/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"229","DOI":"10.3322\/caac.21834","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21590","article-title":"Cancer Statistics, 2020","volume":"70","author":"Siegel","year":"2020","journal-title":"CA Cancer J. Clin."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dominguez-Valentin, M., Nakken, S., Tubeuf, H., Vodak, D., Ekstrom, P.O., Nissen, A.M., Morak, M., Holinski-Feder, E., Holth, A., and Capella, G. (2019). Results of multigene panel testing in familial cancer cases without genetic cause demonstrated by single gene testing. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-54517-z"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Martin-Morales, L., Rofes, P., Diaz-Rubio, E., Llovet, P., Lorca, V., Bando, I., Perez-Segura, P., de la Hoya, M., Garre, P., and Garcia-Barberan, V. (2018). Novel genetic mutations detected by multigene panel are associated with hereditary colorectal cancer predisposition. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0203885"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1200\/PO.21.00079","article-title":"Multigene Panel Testing in Individuals With Hepatocellular Carcinoma Identifies Pathogenic Germline Variants","volume":"5","author":"Mezina","year":"2021","journal-title":"JCO Precis. Oncol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1038\/s41436-021-01262-2","article-title":"Uptake and acceptability of a mainstreaming model of hereditary cancer multigene panel testing among patients with ovarian, pancreatic, and prostate cancer","volume":"23","author":"Hamilton","year":"2021","journal-title":"Genet. Med."},{"key":"ref_7","first-page":"1","article-title":"Multigene Hereditary Cancer Panels Reveal High-Risk Pancreatic Cancer Susceptibility Genes","volume":"2","author":"Hu","year":"2018","journal-title":"JCO Precis. Oncol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"100796","DOI":"10.1016\/j.drup.2021.100796","article-title":"Anticancer drug resistance: An update and perspective","volume":"59","author":"Nussinov","year":"2021","journal-title":"Drug Resist. Updat."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1038\/s41392-021-00572-w","article-title":"Small molecules in targeted cancer therapy: Advances, challenges, and future perspectives","volume":"6","author":"Zhong","year":"2021","journal-title":"Signal Transduct. Target Ther."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1001\/jamainternmed.2020.2250","article-title":"Limitations in Clinical Trials Leading to Anticancer Drug Approvals by the US Food and Drug Administration","volume":"180","author":"Hilal","year":"2020","journal-title":"JAMA Intern. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s40169-017-0181-2","article-title":"A perspective on multi-target drug discovery and design for complex diseases","volume":"7","author":"Ramsay","year":"2018","journal-title":"Clin. Transl. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s13321-020-0408-x","article-title":"Towards reproducible computational drug discovery","volume":"12","author":"Schaduangrat","year":"2020","journal-title":"J. Cheminformatics"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Brogi, S., Ramalho, T.C., Kuca, K., Medina-Franco, J.L., and Valko, M. (2020). Editorial: In silico Methods for Drug Design and Discovery. Front. Chem., 8.","DOI":"10.3389\/fchem.2020.00612"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1080\/10799893.2020.1735418","article-title":"Dual potent c-Met and ALK inhibitors: From common feature pharmacophore modeling to structure based virtual screening","volume":"40","author":"Pirhadi","year":"2020","journal-title":"J. Recept. Signal Transduct. Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"He, Q., Liu, C., Wang, X., Rong, K., Zhu, M., Duan, L., Zheng, P., and Mi, Y. (2023). Exploring the mechanism of curcumin in the treatment of colon cancer based on network pharmacology and molecular docking. Front. Pharmacol., 14.","DOI":"10.3389\/fphar.2023.1102581"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Khalid, H.R., Aamir, M., Tabassum, S., Alghamdi, Y.S., Alzamami, A., and Ashfaq, U.A. (2022). Integrated System Pharmacology Approaches to Elucidate Multi-Target Mechanism of Solanum surattense against Hepatocellular Carcinoma. Molecules, 27.","DOI":"10.3390\/molecules27196220"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Batool, S., Javed, M.R., Aslam, S., Noor, F., Javed, H.M.F., Seemab, R., Rehman, A., Aslam, M.F., Paray, B.A., and Gulnaz, A. (2022). Network Pharmacology and Bioinformatics Approach Reveals the Multi-Target Pharmacological Mechanism of Fumaria indica in the Treatment of Liver Cancer. Pharmaceuticals, 15.","DOI":"10.3390\/ph15060654"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8825","DOI":"10.1080\/07391102.2021.1918253","article-title":"Exploring multi-target inhibitors using in silico approach targeting cell cycle dysregulator-CDK proteins","volume":"40","author":"Ahmed","year":"2022","journal-title":"J. Biomol. Struct. Dyn."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1080\/07391102.2020.1742792","article-title":"Amygdalin as multi-target anticancer drug against targets of cell division cycle: Double docking and molecular dynamics simulation","volume":"39","year":"2021","journal-title":"J. Biomol. Struct. Dyn."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Deng, Z., Chen, G., Shi, Y., Lin, Y., Ou, J., Zhu, H., Wu, J., Li, G., and Lv, L. (2022). Curcumin and its nano-formulations: Defining triple-negative breast cancer targets through network pharmacology, molecular docking, and experimental verification. Front. Pharmacol., 13.","DOI":"10.3389\/fphar.2022.920514"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3337","DOI":"10.1007\/s11030-022-10396-7","article-title":"Reckoning apigenin and kaempferol as a potential multi-targeted inhibitor of EGFR\/HER2-MEK pathway of metastatic colorectal cancer identified using rigorous computational workflow","volume":"26","author":"Sharma","year":"2022","journal-title":"Mol. Divers."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4713","DOI":"10.1080\/07391102.2020.1861982","article-title":"Molecular docking and dynamic simulation to identify potential phytocompound inhibitors for EGFR and HER2 as anti-breast cancer agents","volume":"40","author":"Prabhavathi","year":"2022","journal-title":"J. Biomol. Struct. Dyn."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"14135","DOI":"10.1080\/07391102.2023.2191719","article-title":"Integrating network pharmacology approaches for the investigation of multi-target pharmacological mechanism of 6-shogaol against cervical cancer","volume":"41","author":"Elasbali","year":"2023","journal-title":"J. Biomol. Struct. Dyn."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"De Simone, G., Sardina, D.S., Gulotta, M.R., and Perricone, U. (2022). KUALA: A machine learning-driven framework for kinase inhibitors repositioning. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-22324-8"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Brindha, G.R., Rishiikeshwer, B.S., Santhi, B., Nakendraprasath, K., Manikandan, R., and Gandomi, A.H. (2022). Precise prediction of multiple anticancer drug efficacy using multi target regression and support vector regression analysis. Comput. Methods Programs Biomed., 224.","DOI":"10.1016\/j.cmpb.2022.107027"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Al Taweraqi, N., and King, R.D. (2022). Improved prediction of gene expression through integrating cell signalling models with machine learning. BMC Bioinform., 23.","DOI":"10.1186\/s12859-022-04787-8"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Nguyen, L.C., Naulaerts, S., Bruna, A., Ghislat, G., and Ballester, P.J. (2021). Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles. Biomedicines, 9.","DOI":"10.3390\/biomedicines9101319"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7830","DOI":"10.2174\/0929867328666210419134708","article-title":"Characterizing the Relationship Between the Chemical Structures of Drugs and their Activities on Primary Cultures of Pediatric Solid Tumors","volume":"28","author":"Simeon","year":"2021","journal-title":"Curr. Med. Chem."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.2174\/1568026611313140011","article-title":"General theory for multiple input-output perturbations in complex molecular systems. 1. Linear QSPR electronegativity models in physical, organic, and medicinal chemistry","volume":"13","author":"Arrasate","year":"2013","journal-title":"Curr. Top. Med. Chem."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1517\/17460441.2015.1006195","article-title":"Multitasking models for quantitative structure-biological effect relationships: Current status and future perspectives to speed up drug discovery","volume":"10","author":"Cordeiro","year":"2015","journal-title":"Expert Opin. Drug Discov."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Halder, A.K., Moura, A.S., and Cordeiro, M.N.D.S. (2022). Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next?. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms23094937"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1080\/17460441.2023.2251385","article-title":"Optimizing drug discovery using multitasking models for quantitative structure-biological effect relationships: An update of the literature","volume":"18","author":"Kleandrova","year":"2023","journal-title":"Expert Opin. Drug Discov."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.4155\/fmc-2023-0241","article-title":"Current in silico methods for multi-target drug discovery in early anticancer research: The rise of the perturbation-theory machine learning approach","volume":"15","author":"Kleandrova","year":"2023","journal-title":"Future Med. Chem."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kleandrova, V.V., Cordeiro, M.N.D.S., and Speck-Planche, A. (2025). Perturbation-Theory Machine Learning for Multi-Objective Antibacterial Discovery: Current Status and Future Perspectives. Appl. Sci., 15.","DOI":"10.3390\/app15031166"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1841","DOI":"10.1021\/acs.jcim.3c01796","article-title":"Implementation of IFPTML Computational Models in Drug Discovery Against Flaviviridae Family","volume":"64","author":"Arrasate","year":"2024","journal-title":"J. Chem. Inf. Model."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Dieguez-Santana, K., and Gonzalez-Diaz, H. (2023). Machine learning in antibacterial discovery and development: A bibliometric and network analysis of research hotspots and trends. Comput. Biol. Med., 155.","DOI":"10.1016\/j.compbiomed.2023.106638"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3928","DOI":"10.1021\/acs.jcim.2c00731","article-title":"Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2-Acylpyrrole Derivatives","volume":"62","author":"Santiago","year":"2022","journal-title":"J. Chem. Inf. Model."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2151","DOI":"10.1021\/acs.molpharmaceut.2c00029","article-title":"Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds","volume":"19","author":"Torres","year":"2022","journal-title":"Mol. Pharm."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4200","DOI":"10.1021\/acs.molpharmaceut.9b00538","article-title":"Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds","volume":"16","author":"Tejera","year":"2019","journal-title":"Mol. Pharm."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1021\/acs.jcim.9b00034","article-title":"Modeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Metabolic Networks","volume":"59","author":"Cornelio","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Quevedo-Tumailli, V., Ortega-Tenezaca, B., and Gonzalez-Diaz, H. (2021). IFPTML Mapping of Drug Graphs with Protein and Chromosome Structural Networks vs. Pre-Clinical Assay Information for Discovery of Antimalarial Compounds. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms222313066"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"113458","DOI":"10.1016\/j.ejmech.2021.113458","article-title":"Palladium-mediated synthesis and biological evaluation of C-10b substituted Dihydropyrrolo[1,2-b]isoquinolines as antileishmanial agents","volume":"220","author":"Barbolla","year":"2021","journal-title":"Eur. J. Med. Chem."},{"key":"ref_43","first-page":"20","article-title":"Mapping chemical structure-activity information of HAART-drug cocktails over complex networks of AIDS epidemiology and socioeconomic data of U.S. counties","volume":"132\u2013133","author":"Pazos","year":"2015","journal-title":"Biosystems"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"639","DOI":"10.2174\/1574893610666151008012648","article-title":"Multiscale mapping of AIDS in U.S. countries vs anti-HIV drugs activity with complex networks and information indices","volume":"10","year":"2015","journal-title":"Curr. Bioinform."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Baltasar-Marchueta, M., Llona, L., M-Alicante, S., Barbolla, I., Ibarluzea, M.G., Ramis, R., Salomon, A.M., Fundora, B., Araujo, A., and Muguruza-Montero, A. (2024). Identification of Riluzole derivatives as novel calmodulin inhibitors with neuroprotective activity by a joint synthesis, biosensor, and computational guided strategy. Biomed. Pharmacother., 174.","DOI":"10.1016\/j.biopha.2024.116602"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1021\/acschemneuro.0c00687","article-title":"Synthesis, Pharmacological, and Biological Evaluation of 2-Furoyl-Based MIF-1 Peptidomimetics and the Development of a General-Purpose Model for Allosteric Modulators (ALLOPTML)","volume":"12","author":"Arrasate","year":"2021","journal-title":"ACS Chem. Neurosci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4476","DOI":"10.1021\/acschemneuro.9b00302","article-title":"Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [(35)S]GTPgammaS Binding Assays","volume":"10","author":"Arrasate","year":"2019","journal-title":"ACS Chem. Neurosci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2572","DOI":"10.1021\/acschemneuro.8b00083","article-title":"Perturbation Theory\/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics","volume":"9","author":"Silva","year":"2018","journal-title":"ACS Chem. Neurosci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"511","DOI":"10.2174\/1389450116666151102095243","article-title":"Multi-Target Mining of Alzheimer Disease Proteome with Hansch\u2019s QSBR-Perturbation Theory and Experimental-Theoretic Study of New Thiophene Isosters of Rasagiline","volume":"18","author":"Abeijon","year":"2017","journal-title":"Curr. Drug Targets"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.neuropharm.2015.12.019","article-title":"Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives","volume":"103","author":"Alonso","year":"2016","journal-title":"Neuropharmacology"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"535","DOI":"10.3762\/bjnano.15.47","article-title":"On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems","volume":"15","author":"He","year":"2024","journal-title":"Beilstein J. Nanotechnol."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"He, S., Nader, K., Abarrategi, J.S., Bediaga, H., Nocedo-Mena, D., Ascencio, E., Casanola-Martin, G.M., Castellanos-Rubio, I., Insausti, M., and Rasulev, B. (2024). NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study. J. Nanobiotechnol., 22.","DOI":"10.1186\/s12951-024-02660-9"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1039\/D1EN00967B","article-title":"Towards rational nanomaterial design by predicting drug\u2013nanoparticle system interaction vs. bacterial metabolic networks","volume":"9","author":"Rasulev","year":"2022","journal-title":"Environ. Sci. Nano"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1039\/D0NR07588D","article-title":"IFPTML mapping of nanoparticle antibacterial activity vs. pathogen metabolic networks","volume":"13","year":"2021","journal-title":"Nanoscale"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Munteanu, C.R., Gutierrez-Asorey, P., Blanes-Rodriguez, M., Hidalgo-Delgado, I., Blanco Liverio, M.J., Castineiras Galdo, B., Porto-Pazos, A.B., Gestal, M., Arrasate, S., and Gonzalez-Diaz, H. (2021). Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms222111519"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"17854","DOI":"10.1039\/D1NR04178A","article-title":"Towards machine learning discovery of dual antibacterial drug-nanoparticle systems","volume":"13","year":"2021","journal-title":"Nanoscale"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Urista, D.V., Carrue, D.B., Otero, I., Arrasate, S., Quevedo-Tumailli, V.F., Gestal, M., Gonzalez-Diaz, H., and Munteanu, C.R. (2020). Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models. Biology, 9.","DOI":"10.3390\/biology9080198"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2612","DOI":"10.1021\/acs.molpharmaceut.0c00308","article-title":"PTML Model for Selection of Nanoparticles, Anticancer Drugs, and Vitamins in the Design of Drug-Vitamin Nanoparticle Release Systems for Cancer Cotherapy","volume":"17","author":"Santana","year":"2020","journal-title":"Mol. Pharm."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"13471","DOI":"10.1039\/D0NR01849J","article-title":"Predicting coated-nanoparticle drug release systems with perturbation-theory machine learning (PTML) models","volume":"12","author":"Santana","year":"2020","journal-title":"Nanoscale"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1811","DOI":"10.1021\/acs.chemrestox.9b00154","article-title":"Perturbation Theory Machine Learning Modeling of Immunotoxicity for Drugs Targeting Inflammatory Cytokines and Study of the Antimicrobial G1 Using Cytometric Bead Arrays","volume":"32","author":"Castanedo","year":"2019","journal-title":"Chem. Res. Toxicol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1007\/s11030-017-9749-4","article-title":"A study of the Immune Epitope Database for some fungi species using network topological indices","volume":"21","author":"Paniagua","year":"2017","journal-title":"Mol. Divers."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"4093","DOI":"10.1021\/acs.jproteome.7b00477","article-title":"PTML Model for Proteome Mining of B-Cell Epitopes and Theoretical-Experimental Study of Bm86 Protein Sequences from Colima, Mexico","volume":"16","year":"2017","journal-title":"J. Proteome Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.ejmech.2013.08.035","article-title":"Model for high-throughput screening of drug immunotoxicity\u2014Study of the anti-microbial G1 over peritoneal macrophages using flow cytometry","volume":"72","author":"Castanedo","year":"2014","journal-title":"Eur. J. Med. Chem."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"10116","DOI":"10.1021\/acs.est.4c01017","article-title":"Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling","volume":"58","author":"Daghighi","year":"2024","journal-title":"Environ. Sci. Technol."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Todeschini, R., and Consonni, V. (2009). Molecular Descriptors for Chemoinformatics, WILEY-VCH Verlag GmbH & Co. KGaA.","DOI":"10.1002\/9783527628766"},{"key":"ref_66","unstructured":"Estrada, E., and Guti\u00e9rrez, Y. (MODESLAB, 2004). MODESLAB, v1.5."},{"key":"ref_67","unstructured":"Todeschini, R., Consonni, V., Mauri, A., and Pavan, M. (DRAGON for Windows (Software for Molecular Descriptor Calculations), 2005). DRAGON for Windows (Software for Molecular Descriptor Calculations), v5.3."},{"key":"ref_68","unstructured":"Vald\u00e9s-Martini, J.R., Garc\u00eda-Jacas, C.R., Marrero-Ponce, Y., Silveira Vaz \u2018d Almeida, Y., and Morell, C. (2025, February 15). QUBILs-MAS: Free Software for Molecular Descriptors Calculator from Quadratic, Bilinear and Linear Maps Based on Graph-Theoretic Electronic-Density Matrices and Atomic Weightings, v1.0. CAMD-BIR Unit, CENDA Registration Number: 2373-2012: Villa Clara, Cuba, 2012. Available online: https:\/\/tomocomd.com\/."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1080\/1062936X.2015.1104517","article-title":"QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents","volume":"26","author":"Barigye","year":"2015","journal-title":"SAR QSAR Environ. Res."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s13321-017-0211-5","article-title":"QuBiLS-MAS, open source multi-platform software for atom- and bond-based topological (2D) and chiral (2.5D) algebraic molecular descriptors computations","volume":"9","author":"Barigye","year":"2017","journal-title":"J. Cheminformatics"},{"key":"ref_71","unstructured":"TIBCO-Software-Inc (STATISTICA (Data Analysis Software System), 2018). STATISTICA (Data Analysis Software System), v13.5.0.17."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2185","DOI":"10.1016\/j.ejmech.2011.02.072","article-title":"Using the TOPS-MODE approach to fit multi-target QSAR models for tyrosine kinases inhibitors","volume":"46","author":"Marzaro","year":"2011","journal-title":"Eur. J. Med. Chem."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"5910","DOI":"10.1016\/j.ejmech.2011.09.055","article-title":"Fragment-based QSAR model toward the selection of versatile anti-sarcoma leads","volume":"46","author":"Kleandrova","year":"2011","journal-title":"Eur. J. Med. Chem."},{"key":"ref_74","unstructured":"Haggerty, S. (2014). Multi-tasking chemoinformatic model for the efficient discovery of potent and safer anti-bladder cancer agents. Bladder Cancer: Risk Factors, Emerging Treatment Strategies and Challenges, Nova Science Publishers, Inc."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Pelon, M., Krzeminski, P., Tracz-Gaszewska, Z., and Misiewicz-Krzeminska, I. (2024). Factors determining the sensitivity to proteasome inhibitors of multiple myeloma cells. Front. Pharmacol., 15.","DOI":"10.3389\/fphar.2024.1351565"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"e2308417120","DOI":"10.1073\/pnas.2308417120","article-title":"Rational design of proteasome inhibitors based on the structure of the endogenous inhibitor PI31\/Fub1","volume":"120","author":"Velez","year":"2023","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neo.2021.11.009","article-title":"Resensitising proteasome inhibitor-resistant myeloma with sphingosine kinase 2 inhibition","volume":"24","author":"Bennett","year":"2022","journal-title":"Neoplasia"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1007\/s11030-015-9571-9","article-title":"Multi-output model with Box-Jenkins operators of linear indices to predict multi-target inhibitors of ubiquitin-proteasome pathway","volume":"19","author":"Abad","year":"2015","journal-title":"Mol. Divers."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1021\/acscombsci.8b00090","article-title":"PTML Combinatorial Model of ChEMBL Compounds Assays for Multiple Types of Cancer","volume":"20","author":"Bediaga","year":"2018","journal-title":"ACS Comb. Sci."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"27211","DOI":"10.1021\/acsomega.0c03356","article-title":"Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds","volume":"5","author":"Munteanu","year":"2020","journal-title":"ACS Omega"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"3408","DOI":"10.1038\/s41467-024-47613-w","article-title":"Prospective de novo drug design with deep interactome learning","volume":"15","author":"Atz","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.sbi.2021.10.001","article-title":"Deep learning approaches for de novo drug design: An overview","volume":"72","author":"Wang","year":"2022","journal-title":"Curr. Opin. Struct. Biol."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1038\/s41467-022-35692-6","article-title":"Leveraging molecular structure and bioactivity with chemical language models for de novo drug design","volume":"14","author":"Moret","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Mouchlis, V.D., Afantitis, A., Serra, A., Fratello, M., Papadiamantis, A.G., Aidinis, V., Lynch, I., Greco, D., and Melagraki, G. (2021). Advances in de Novo Drug Design: From Conventional to Machine Learning Methods. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22041676"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.2174\/1389557520666200204123156","article-title":"The QSAR Paradigm in Fragment-Based Drug Discovery: From the Virtual Generation of Target Inhibitors to Multi-Scale Modeling","volume":"20","author":"Kleandrova","year":"2020","journal-title":"Mini Rev. Med. Chem."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1021\/ci000170v","article-title":"Can 3D structural parameters be predicted from 2D (topological) molecular descriptors?","volume":"41","author":"Estrada","year":"2001","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"9085","DOI":"10.1021\/jp026238m","article-title":"Physicochemical Interpretation of Molecular Connectivity Indices","volume":"106","author":"Estrada","year":"2002","journal-title":"J. Phys. Chem. A"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1021\/ci00023a004","article-title":"Edge adjacency relationship and a novel topological index related to molecular volume","volume":"35","author":"Estrada","year":"1995","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1021\/ci950187r","article-title":"Spectral moments of the edge adjacency matrix in molecular graphs. 1. Definition and applications for the prediction of physical properties of alkanes","volume":"36","author":"Estrada","year":"1996","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1021\/ci960113v","article-title":"Spectral moments of the edge adjacency matrix in molecular graphs. 2. Molecules containing heteroatoms and QSAR applications","volume":"37","author":"Estrada","year":"1997","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1021\/ci970030u","article-title":"Spectral moments of the edge adjacency matrix in molecular graphs. 3. Molecules containing cycles","volume":"38","author":"Estrada","year":"1998","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1023\/A:1008048003720","article-title":"Designing sedative\/hypnotic compounds from a novel substructural graph-theoretical approach","volume":"12","author":"Estrada","year":"1998","journal-title":"J. Comput. Aided Mol. Des."},{"key":"ref_93","unstructured":"Kier, L.B., and Hall, L.H. (1986). Molecular Connectivity in Structure-Activity Analysis, John Wiley & Sons."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1021\/ci00025a021","article-title":"On the basis of invariants of labeled molecular graphs","volume":"35","author":"Baskin","year":"1995","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Varnek, A., and Tropsha, A. (2008). Fragment descriptors in SAR\/QSAR\/QSPR studies, molecular similarity analysis and in virtual screening. Chemoinformatics Approaches to Virtual Screening, Royal Society of Chemistry.","DOI":"10.1039\/9781847558879"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Kleandrova, V.V., Cordeiro, M.N.D.S., and Speck-Planche, A. (2025). In Silico Approach for Antibacterial Discovery: PTML Modeling of Virtual Multi-Strain Inhibitors Against Staphylococcus aureus. Pharmaceuticals, 18.","DOI":"10.3390\/ph18020196"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"6239","DOI":"10.1016\/j.bmc.2011.09.015","article-title":"Multi-target drug discovery in anti-cancer therapy: Fragment-based approach toward the design of potent and versatile anti-prostate cancer agents","volume":"19","author":"Kleandrova","year":"2011","journal-title":"Bioorg. Med. Chem."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.ejps.2012.04.012","article-title":"Chemoinformatics in anti-cancer chemotherapy: Multi-target QSAR model for the in silico discovery of anti-breast cancer agents","volume":"47","author":"Kleandrova","year":"2012","journal-title":"Eur. J. Pharm. Sci."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"678","DOI":"10.2174\/187152012800617722","article-title":"Chemoinformatics in multi-target drug discovery for anti-cancer therapy: In silico design of potent and versatile anti-brain tumor agents","volume":"12","author":"Kleandrova","year":"2012","journal-title":"Anticancer Agents Med. Chem."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"4848","DOI":"10.1016\/j.bmc.2012.05.071","article-title":"Rational drug design for anti-cancer chemotherapy: Multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents","volume":"20","author":"Kleandrova","year":"2012","journal-title":"Bioorg. Med. Chem."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"791","DOI":"10.2174\/1871520611313050013","article-title":"Unified multi-target approach for the rational in silico design of anti-bladder cancer agents","volume":"13","author":"Kleandrova","year":"2013","journal-title":"Anticancer Agents Med. Chem."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1007\/s11030-017-9731-1","article-title":"Fragment-based in silico modeling of multi-target inhibitors against breast cancer-related proteins","volume":"21","author":"Cordeiro","year":"2017","journal-title":"Mol. Divers."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"661","DOI":"10.2174\/1568026621666210119112845","article-title":"Multi-Target Drug Discovery Via PTML Modeling: Applications to the Design of Virtual Dual Inhibitors of CDK4 and HER2","volume":"21","author":"Kleandrova","year":"2021","journal-title":"Curr. Top. Med. Chem."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s11030-018-9890-8","article-title":"BET bromodomain inhibitors: Fragment-based in silico design using multi-target QSAR models","volume":"23","author":"Scotti","year":"2019","journal-title":"Mol. Divers."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1080\/1062936X.2020.1818617","article-title":"Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines","volume":"31","author":"Kleandrova","year":"2020","journal-title":"SAR QSAR Environ. Res."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"3122","DOI":"10.1021\/acsomega.8b03693","article-title":"Multicellular Target QSAR Model for Simultaneous Prediction and Design of Anti-Pancreatic Cancer Agents","volume":"4","year":"2019","journal-title":"ACS Omega"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Kleandrova, V.V., and Speck-Planche, A. (2022). PTML Modeling for Pancreatic Cancer Research: In Silico Design of Simultaneous Multi-Protein and Multi-Cell Inhibitors. Biomedicines, 10.","DOI":"10.3390\/biomedicines10020491"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Kleandrova, V.V., Cordeiro, M.N.D.S., and Speck-Planche, A. (2024). Perturbation Theory Machine Learning Model for Phenotypic Early Antineoplastic Drug Discovery: Design of Virtual Anti-Lung-Cancer Agents. Appl. Sci., 14.","DOI":"10.3390\/app14209344"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1038\/s41571-024-00932-9","article-title":"Tumour mutational burden: Clinical utility, challenges and emerging improvements","volume":"21","author":"Budczies","year":"2024","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Lagunin, A.A., Rudik, A.V., Pogodin, P.V., Savosina, P.I., Tarasova, O.A., Dmitriev, A.V., Ivanov, S.M., Biziukova, N.Y., Druzhilovskiy, D.S., and Filimonov, D.A. (2023). CLC-Pred 2.0: A Freely Available Web Application for In Silico Prediction of Human Cell Line Cytotoxicity and Molecular Mechanisms of Action for Druglike Compounds. Int. J. Mol. Sci., 24.","DOI":"10.3390\/ijms24021689"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"3238","DOI":"10.1021\/acs.jcim.2c01355","article-title":"Generative Models Should at Least Be Able to Design Molecules That Dock Well: A New Benchmark","volume":"63","author":"Cieplinski","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Kleandrova, V.V., Scotti, L., Bezerra Mendon\u00e7a Junior, F.J., Muratov, E., Scotti, M.T., and Speck-Planche, A. (2021). QSAR Modeling for Multi-Target Drug Discovery: Designing Simultaneous Inhibitors of Proteins in Diverse Pathogenic Parasites. Front. Chem., 9.","DOI":"10.3389\/fchem.2021.634663"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Chinnadurai, R.K., Khan, N., Meghwanshi, G.K., Ponne, S., Althobiti, M., and Kumar, R. (2023). Current research status of anti-cancer peptides: Mechanism of action, production, and clinical applications. Biomed. Pharmacother., 164.","DOI":"10.1016\/j.biopha.2023.114996"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"529","DOI":"10.21873\/anticanres.13144","article-title":"MicroRNA-based Targeted Therapeutics in Pancreatic Cancer","volume":"39","author":"Gurbuz","year":"2019","journal-title":"Anticancer Res."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"32231","DOI":"10.1021\/acsomega.3c04345","article-title":"Recent Advancements of Aptamers in Cancer Therapy","volume":"8","author":"Venkatesan","year":"2023","journal-title":"ACS Omega"}],"container-title":["Current Issues in Molecular Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1467-3045\/47\/5\/301\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:21:31Z","timestamp":1760030491000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1467-3045\/47\/5\/301"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,25]]},"references-count":115,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["cimb47050301"],"URL":"https:\/\/doi.org\/10.3390\/cimb47050301","relation":{},"ISSN":["1467-3045"],"issn-type":[{"type":"electronic","value":"1467-3045"}],"subject":[],"published":{"date-parts":[[2025,4,25]]}}}