{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T10:39:11Z","timestamp":1766486351095,"version":"3.40.4"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T00:00:00Z","timestamp":1735776000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T00:00:00Z","timestamp":1735776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"FCT\/MCTES","award":["UIDB\/50006\/2020"],"award-info":[{"award-number":["UIDB\/50006\/2020"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Chemistry"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Mood disorders affect the daily lives of millions of people worldwide. The search for more efficient therapies for mood disorders remains an active field of research. In silico approaches can accelerate the search for inhibitors against protein targets related to mood disorders. Here, we developed the first model perturbation-theory machine learning model based on a multiplayer perceptron network (PTML-MLP) for the simultaneous prediction and design of virtual dual-target inhibitors against two proteins associated with mood disorders, namely norepinephrine and serotonin transporters (NET and SERT, respectively). The PTML-MLP model had an accuracy of around 80%. From a chemical point of view, the PTML-MLP model could accurately identify both single- and dual-target inhibitors present in the dataset used to build it. Through the application of the fragment-based topological design (FBTD) approach, the molecular descriptors (multi-label graph-based indices) present in the PTML-MLP model were physicochemically and structurally interpreted. Such interpretations enabled (a) the extraction of different molecular fragments with a positive influence on the enhancement of the dual-target activity and (b) the design of four new drug-like molecules by assembling (fusing and\/or connecting) several suitable molecular fragments. The designed molecules were predicted by the PTML-MLP model to exhibit dual-target activity against the NET and SERT proteins. These predictions, together with the estimated druglikeness suggest that the designed molecules could be new promising chemotypes to be considered for future synthesis and biological experimentation in the context of treatments for mood disorders.<\/jats:p>","DOI":"10.1186\/s13065-024-01376-z","type":"journal-article","created":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T03:20:41Z","timestamp":1735874441000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Perturbation-theory machine learning for mood disorders: virtual design of dual inhibitors of NET and SERT proteins"],"prefix":"10.1186","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1928-853X","authenticated-orcid":false,"given":"Valeria V.","family":"Kleandrova","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3375-8670","authenticated-orcid":false,"given":"M. Nat\u00e1lia D. S.","family":"Cordeiro","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9544-9016","authenticated-orcid":false,"given":"Alejandro","family":"Speck-Planche","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,2]]},"reference":[{"key":"1376_CR1","doi-asserted-by":"crossref","unstructured":"GBD(2019 Diseases and Injuries Collaborators). Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u20132019: a systematic analysis for the global burden of Disease Study 2019. Lancet. 2020;396:1204\u201322.","DOI":"10.1016\/S0140-6736(20)30925-9"},{"key":"1376_CR2","doi-asserted-by":"publisher","first-page":"e02167","DOI":"10.1002\/brb3.2167","volume":"11","author":"NA Martin-Key","year":"2021","unstructured":"Martin-Key NA, Olmert T, Barton-Owen G, Han SYS, Cooper JD, Eljasz P, et al. The Delta Study - Prevalence and characteristics of mood disorders in 924 individuals with low mood: results of the of the World Health Organization Composite International Diagnostic Interview (CIDI). Brain Behav. 2021;11:e02167.","journal-title":"Brain Behav"},{"key":"1376_CR3","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1007\/s40273-021-01019-4","volume":"39","author":"PE Greenberg","year":"2021","unstructured":"Greenberg PE, Fournier AA, Sisitsky T, Simes M, Berman R, Koenigsberg SH, et al. The economic burden of adults with major depressive disorder in the United States (2010 and 2018). PharmacoEconomics. 2021;39:653\u201365.","journal-title":"PharmacoEconomics"},{"key":"1376_CR4","first-page":"1905","volume":"163","author":"AJ Rush","year":"2006","unstructured":"Rush AJ, Trivedi MH, Wisniewski SR, Nierenberg AA, Stewart JW, Warden D, et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. AJ Psychiatry. 2006;163:1905\u201317.","journal-title":"AJ Psychiatry"},{"key":"1376_CR5","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/s10654-007-9204-4","volume":"23","author":"RP Stolk","year":"2008","unstructured":"Stolk RP, Rosmalen JG, Postma DS, de Boer RA, Navis G, Slaets JP, et al. Universal risk factors for multifactorial diseases: LifeLines: a three-generation population-based study. Eur J Epidemiol. 2008;23:67\u201374.","journal-title":"Eur J Epidemiol"},{"key":"1376_CR6","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s13321-020-0408-x","volume":"12","author":"N Schaduangrat","year":"2020","unstructured":"Schaduangrat N, Lampa S, Simeon S, Gleeson MP, Spjuth O, Nantasenamat C. Towards reproducible computational drug discovery. J Cheminformatics. 2020;12:9.","journal-title":"J Cheminformatics"},{"key":"1376_CR7","doi-asserted-by":"publisher","first-page":"1018473","DOI":"10.3389\/fphar.2022.1018473","volume":"13","author":"H Liu","year":"2022","unstructured":"Liu H, Wu Y, Li C, Tang Q, Zhang YW. Molecular docking and biochemical validation of (-)-syringaresinol-4-O-beta-D-apiofuranosyl-(1\u2013>2)-beta-D-glucopyranoside binding to an allosteric site in monoamine transporters. Front Pharmacol. 2022;13:1018473.","journal-title":"Front Pharmacol"},{"key":"1376_CR8","doi-asserted-by":"publisher","first-page":"5883","DOI":"10.3390\/molecules27185883","volume":"27","author":"A Gaber","year":"2022","unstructured":"Gaber A, Alsanie WF, Alhomrani M, Alamri AS, Alyami H, Shakya S, et al. Multispectral and molecular Docking studies reveal potential effectiveness of antidepressant fluoxetine by forming pi-acceptor complexes. Molecules. 2022;27:5883.","journal-title":"Molecules"},{"key":"1376_CR9","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1016\/j.euroneuro.2018.07.096","volume":"28","author":"TT Zhang","year":"2018","unstructured":"Zhang TT, Xue R, Wang X, Zhao SW, An L, Li YF, et al. Network-based drug repositioning: a novel strategy for discovering potential antidepressants and their mode of action. Eur Neuropsychopharmacol. 2018;28:1137\u201350.","journal-title":"Eur Neuropsychopharmacol"},{"key":"1376_CR10","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.3390\/pharmaceutics13091449","volume":"13","author":"S Avram","year":"2021","unstructured":"Avram S, Stan MS, Udrea AM, Buiu C, Boboc AA, Mernea M. 3D-ALMOND-QSAR models to predict the antidepressant effect of some natural compounds. Pharmaceutics. 2021;13:1449.","journal-title":"Pharmaceutics"},{"key":"1376_CR11","doi-asserted-by":"publisher","first-page":"e07784","DOI":"10.1016\/j.heliyon.2021.e07784","volume":"7","author":"AA Islas","year":"2021","unstructured":"Islas AA, Moreno LG, Scior T. Induced fit, ensemble binding space docking and Monte Carlo simulations of MDMA \u2018ecstasy\u2019 and 3D pharmacophore design of MDMA derivatives on the human serotonin transporter (hSERT). Heliyon. 2021;7:e07784.","journal-title":"Heliyon"},{"key":"1376_CR12","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1038\/s41401-019-0217-9","volume":"40","author":"YQ Wang","year":"2019","unstructured":"Wang YQ, Lin WW, Wu N, Wang SY, Chen MZ, Lin ZH, et al. Structural insight into the serotonin (5-HT) receptor family by molecular docking, molecular dynamics simulation and systems pharmacology analysis. Acta Pharmacol Sin. 2019;40:1138\u201356.","journal-title":"Acta Pharmacol Sin"},{"key":"1376_CR13","doi-asserted-by":"publisher","first-page":"e02640","DOI":"10.1016\/j.heliyon.2019.e02640","volume":"5","author":"SB Olasupo","year":"2019","unstructured":"Olasupo SB, Uzairu A, Shallangwa G, Uba S. QSAR analysis and molecular docking simulation of norepinephrine transporter (NET) inhibitors as anti-psychotic therapeutic agents. Heliyon. 2019;5:e02640.","journal-title":"Heliyon"},{"key":"1376_CR14","doi-asserted-by":"publisher","first-page":"e04464","DOI":"10.1016\/j.heliyon.2020.e04464","volume":"6","author":"SB Olasupo","year":"2020","unstructured":"Olasupo SB, Uzairu A, Shallangwa GA, Uba S. Chemoinformatic studies on some inhibitors of dopamine transporter and the receptor targeting schizophrenia for developing novel antipsychotic agents. Heliyon. 2020;6:e04464.","journal-title":"Heliyon"},{"key":"1376_CR15","doi-asserted-by":"publisher","first-page":"619288","DOI":"10.3389\/fphar.2021.619288","volume":"12","author":"SY Qu","year":"2021","unstructured":"Qu SY, Li XY, Heng X, Qi YY, Ge PY, Ni SJ, et al. Analysis of antidepressant activity of Huang-Lian Jie-Du Decoction through Network Pharmacology and Metabolomics. Front Pharmacol. 2021;12:619288.","journal-title":"Front Pharmacol"},{"key":"1376_CR16","doi-asserted-by":"publisher","first-page":"1713","DOI":"10.2174\/1568026611313140011","volume":"13","author":"H Gonzalez-Diaz","year":"2013","unstructured":"Gonzalez-Diaz H, Arrasate S, Gomez-SanJuan A, Sotomayor N, Lete E, Besada-Porto L, et al. General theory for multiple input-output perturbations in complex molecular systems. 1. Linear QSPR electronegativity models in physical, organic, and medicinal chemistry. Curr Top Med Chem. 2013;13:1713\u201341.","journal-title":"Curr Top Med Chem"},{"key":"1376_CR17","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1517\/17460441.2015.1006195","volume":"10","author":"A Speck-Planche","year":"2015","unstructured":"Speck-Planche A, Cordeiro MNDS. Multitasking models for quantitative structure-biological effect relationships: current status and future perspectives to speed up drug discovery. Expert Opin Drug Discov. 2015;10:245\u201356.","journal-title":"Expert Opin Drug Discov"},{"key":"1376_CR18","doi-asserted-by":"publisher","first-page":"4937","DOI":"10.3390\/ijms23094937","volume":"23","author":"AK Halder","year":"2022","unstructured":"Halder AK, Moura AS, Cordeiro MNDS. Moving average-based Multitasking in Silico classification modeling: where do we stand and what is Next? Int J Mol Sci. 2022;23:4937.","journal-title":"Int J Mol Sci"},{"key":"1376_CR19","doi-asserted-by":"publisher","first-page":"491","DOI":"10.3390\/biomedicines10020491","volume":"10","author":"VV Kleandrova","year":"2022","unstructured":"Kleandrova VV, Speck-Planche A. PTML modeling for pancreatic Cancer Research: in Silico Design of Simultaneous Multi-protein and Multi-cell inhibitors. Biomedicines. 2022;10:491.","journal-title":"Biomedicines"},{"key":"1376_CR20","doi-asserted-by":"publisher","first-page":"2612","DOI":"10.1021\/acs.molpharmaceut.0c00308","volume":"17","author":"R Santana","year":"2020","unstructured":"Santana R, Zuluaga R, Ganan P, Arrasate S, Onieva E, Montemore MM, et al. PTML Model for Selection of Nanoparticles, anticancer drugs, and vitamins in the design of drug-vitamin nanoparticle Release systems for Cancer Cotherapy. Mol Pharm. 2020;17:2612\u201327.","journal-title":"Mol Pharm"},{"key":"1376_CR21","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1080\/1062936X.2020.1818617","volume":"31","author":"VV Kleandrova","year":"2020","unstructured":"Kleandrova VV, Scotti MT, Scotti L, Nayarisseri A, Speck-Planche A. Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines. SAR QSAR Environ Res. 2020;31:815\u201336.","journal-title":"SAR QSAR Environ Res"},{"key":"1376_CR22","doi-asserted-by":"publisher","first-page":"3122","DOI":"10.1021\/acsomega.8b03693","volume":"4","author":"A Speck-Planche","year":"2019","unstructured":"Speck-Planche A. Multicellular target QSAR Model for Simultaneous Prediction and Design of Anti-pancreatic Cancer agents. ACS Omega. 2019;4:3122\u201332.","journal-title":"ACS Omega"},{"key":"1376_CR23","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1021\/acscombsci.8b00090","volume":"20","author":"H Bediaga","year":"2018","unstructured":"Bediaga H, Arrasate S, Gonzalez-Diaz H. PTML Combinatorial Model of ChEMBL compounds assays for multiple types of Cancer. ACS Comb Sci. 2018;20:621\u201332.","journal-title":"ACS Comb Sci"},{"key":"1376_CR24","doi-asserted-by":"publisher","first-page":"2151","DOI":"10.1021\/acs.molpharmaceut.2c00029","volume":"19","author":"K Dieguez-Santana","year":"2022","unstructured":"Dieguez-Santana K, Casanola-Martin GM, Torres R, Rasulev B, Green JR, Gonzalez-Diaz H. Machine learning study of metabolic networks vs ChEMBL Data of Antibacterial compounds. Mol Pharm. 2022;19:2151\u201363.","journal-title":"Mol Pharm"},{"key":"1376_CR25","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1039\/D0NR07588D","volume":"13","author":"B Ortega-Tenezaca","year":"2021","unstructured":"Ortega-Tenezaca B, Gonzalez-Diaz H. IFPTML mapping of nanoparticle antibacterial activity vs. pathogen metabolic networks. Nanoscale. 2021;13:1318\u201330.","journal-title":"Nanoscale"},{"key":"1376_CR26","first-page":"198","volume":"9","author":"DV Urista","year":"2020","unstructured":"Urista DV, Carrue DB, Otero I, Arrasate S, Quevedo-Tumailli VF, Gestal M, et al. Prediction of Antimalarial Drug-decorated nanoparticle Delivery systems with Random Forest models. Biology (Basel). 2020;9:198.","journal-title":"Biology (Basel)"},{"key":"1376_CR27","doi-asserted-by":"publisher","first-page":"32119","DOI":"10.1021\/acsomega.2c03363","volume":"7","author":"A Speck-Planche","year":"2022","unstructured":"Speck-Planche A, Kleandrova VV. Multi-condition QSAR Model for the virtual design of chemicals with dual pan-antiviral and anti-cytokine storm profiles. ACS Omega. 2022;7:32119\u201330.","journal-title":"ACS Omega"},{"key":"1376_CR28","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.3390\/antibiotics10081005","volume":"10","author":"VV Kleandrova","year":"2021","unstructured":"Kleandrova VV, Scotti MT, Speck-Planche A. Computational drug repurposing for Antituberculosis Therapy: Discovery of Multi-strain inhibitors. Antibiot (Basel). 2021;10:1005.","journal-title":"Antibiot (Basel)"},{"key":"1376_CR29","doi-asserted-by":"publisher","first-page":"4476","DOI":"10.1021\/acschemneuro.9b00302","volume":"10","author":"R Diez-Alarcia","year":"2019","unstructured":"Diez-Alarcia R, Yanez-Perez V, Muneta-Arrate I, Arrasate S, Lete E, Meana JJ, et al. Big Data challenges Targeting proteins in GPCR Signaling pathways; combining PTML-ChEMBL models and [(35)S]GTPgammaS binding assays. ACS Chem Neurosci. 2019;10:4476\u201391.","journal-title":"ACS Chem Neurosci"},{"key":"1376_CR30","doi-asserted-by":"publisher","first-page":"1661","DOI":"10.2174\/1568026620666200607190951","volume":"20","author":"VV Kleandrova","year":"2020","unstructured":"Kleandrova VV, Speck-Planche A. PTML modeling for Alzheimer\u2019s Disease: design and prediction of virtual Multi-target inhibitors of GSK3B, HDAC1, and HDAC6. Curr Top Med Chem. 2020;20:1661\u201376.","journal-title":"Curr Top Med Chem"},{"key":"1376_CR31","doi-asserted-by":"publisher","first-page":"14704","DOI":"10.1021\/acsomega.8b02419","volume":"3","author":"A Speck-Planche","year":"2018","unstructured":"Speck-Planche A. Combining ensemble learning with a fragment-based Topological Approach to generate New Molecular Diversity in Drug Discovery: in Silico Design of Hsp90 inhibitors. ACS Omega. 2018;3:14704\u201316.","journal-title":"ACS Omega"},{"key":"1376_CR32","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1080\/17460441.2023.2251385","volume":"18","author":"VV Kleandrova","year":"2023","unstructured":"Kleandrova VV, Cordeiro MNDS, Speck-Planche A. Optimizing drug discovery using multitasking models for quantitative structure-biological effect relationships: an update of the literature. Expert Opin Drug Discov. 2023;18:1231\u201343.","journal-title":"Expert Opin Drug Discov"},{"key":"1376_CR33","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1039\/9781839160233-00398","volume-title":"Machine learning in Chemistry: the impact of Artificial Intelligence","author":"A Speck-Planche","year":"2020","unstructured":"Speck-Planche A, Kleandrova VV. Demystifying Artificial neural networks as generators of New Chemical Knowledge: Antimalarial Drug Discovery as a case study. In: Cartwright HM, editor. Machine learning in Chemistry: the impact of Artificial Intelligence. London, United Kingdom: The Royal Society of Chemistry; 2020. pp. 398\u2013423."},{"key":"1376_CR34","doi-asserted-by":"publisher","first-page":"988648","DOI":"10.3389\/fphar.2022.988648","volume":"13","author":"K Stachowicz","year":"2022","unstructured":"Stachowicz K, Sowa-Kucma M. The treatment of depression - searching for new ideas. Front Pharmacol. 2022;13:988648.","journal-title":"Front Pharmacol"},{"key":"1376_CR35","doi-asserted-by":"publisher","first-page":"2080","DOI":"10.1038\/sj.npp.1301652","volume":"33","author":"SJ Mathew","year":"2008","unstructured":"Mathew SJ, Manji HK, Charney DS. Novel drugs and therapeutic targets for severe mood disorders. Neuropsychopharmacology. 2008;33:2080\u201392.","journal-title":"Neuropsychopharmacology"},{"key":"1376_CR36","doi-asserted-by":"publisher","first-page":"634663","DOI":"10.3389\/fchem.2021.634663","volume":"9","author":"VV Kleandrova","year":"2021","unstructured":"Kleandrova VV, Scotti L, Bezerra Mendon\u00e7a Junior FJ, Muratov E, Scotti MT, Speck-Planche A. QSAR modeling for Multi-target Drug Discovery: Designing simultaneous inhibitors of proteins in diverse pathogenic parasites. Front Chem. 2021;9:634663.","journal-title":"Front Chem"},{"key":"1376_CR37","doi-asserted-by":"publisher","first-page":"D1100","DOI":"10.1093\/nar\/gkr777","volume":"40","author":"A Gaulton","year":"2012","unstructured":"Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012;40:D1100\u20137.","journal-title":"Nucleic Acids Res"},{"key":"1376_CR38","doi-asserted-by":"publisher","first-page":"D930","DOI":"10.1093\/nar\/gky1075","volume":"47","author":"D Mendez","year":"2019","unstructured":"Mendez D, Gaulton A, Bento AP, Chambers J, De Veij M, Felix E, et al. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res. 2019;47:D930\u201340.","journal-title":"Nucleic Acids Res"},{"key":"1376_CR39","unstructured":"Estrada E, Guti\u00e9rrez Y. MODESLAB. v1.5. Santiago de Compostela, Spain; 2002."},{"key":"1376_CR40","doi-asserted-by":"publisher","first-page":"1832","DOI":"10.3390\/biom11121832","volume":"11","author":"A Speck-Planche","year":"2021","unstructured":"Speck-Planche A, Kleandrova VV, Scotti MT. Silico Drug Repurposing for anti-inflammatory therapy: virtual search for dual inhibitors of Caspase-1 and TNF-Alpha. Biomolecules. 2021;11:1832.","journal-title":"Biomolecules"},{"key":"1376_CR41","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1007\/s11030-014-9565-z","volume":"19","author":"RW Urias","year":"2015","unstructured":"Urias RW, Barigye SJ, Marrero-Ponce Y, Garcia-Jacas CR, Valdes-Martini JR, Perez-Gimenez F. IMMAN: free software for information theory-based chemometric analysis. Mol Divers. 2015;19:305\u201319.","journal-title":"Mol Divers"},{"key":"1376_CR42","doi-asserted-by":"publisher","first-page":"1935","DOI":"10.1021\/ci100319n","volume":"50","author":"AM Wassermann","year":"2010","unstructured":"Wassermann AM, Nisius B, Vogt M, Bajorath J. Identification of descriptors capturing compound class-specific features by mutual information analysis. J Chem Inf Model. 2010;50:1935\u201340.","journal-title":"J Chem Inf Model"},{"key":"1376_CR43","unstructured":"TIBCO-Software-Inc. STATISTICA (Data Analysis Software System), v13.5.0.17. Palo Alto, California, USA, 2018."},{"key":"1376_CR44","doi-asserted-by":"publisher","first-page":"9344","DOI":"10.3390\/app14209344","volume":"14","author":"VV Kleandrova","year":"2024","unstructured":"Kleandrova VV, Cordeiro MNDS, Speck-Planche A. Perturbation theory machine learning model for phenotypic early antineoplastic drug Discovery: design of virtual Anti-lung-cancer agents. Appl Sci. 2024;14:9344.","journal-title":"Appl Sci"},{"key":"1376_CR45","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s13040-023-00322-4","volume":"16","author":"D Chicco","year":"2023","unstructured":"Chicco D, Jurman G. The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification. BioData Min. 2023;16:4.","journal-title":"BioData Min"},{"key":"1376_CR46","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/S0079-6107(98)00026-1","volume":"70","author":"G Schneider","year":"1998","unstructured":"Schneider G, Wrede P. Artificial neural networks for computer-based molecular design. Prog Biophys Mol Biol. 1998;70:175\u2013222.","journal-title":"Prog Biophys Mol Biol"},{"key":"1376_CR47","doi-asserted-by":"crossref","unstructured":"Manallack DT, Livingstone DJ, A-Razzak M, Glen RC. Neural Networks and Expert Systems in Molecular Design. In: van de Waterbeemd H, ed. Advanced Computer-Assisted Techniques in Drug Discovery, 1994:293\u2013331.","DOI":"10.1002\/9783527615674.ch5"},{"key":"1376_CR48","doi-asserted-by":"publisher","first-page":"661","DOI":"10.2174\/1568026621666210119112845","volume":"21","author":"VV Kleandrova","year":"2021","unstructured":"Kleandrova VV, Scotti MT, Scotti L, Speck-Planche A. Multi-target Drug Discovery Via PTML modeling: applications to the design of virtual dual inhibitors of CDK4 and HER2. Curr Top Med Chem. 2021;21:661\u201375.","journal-title":"Curr Top Med Chem"},{"key":"1376_CR49","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.2174\/1389557520666200204123156","volume":"20","author":"VV Kleandrova","year":"2020","unstructured":"Kleandrova VV, Speck-Planche A. The QSAR paradigm in fragment-based drug Discovery: from the virtual generation of target inhibitors to Multi-scale modeling. Mini Rev Med Chem. 2020;20:1357\u201374.","journal-title":"Mini Rev Med Chem"},{"key":"1376_CR50","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1007\/978-0-387-35973-1_538","volume-title":"Encyclopedia of GIS","author":"X Zhou","year":"2008","unstructured":"Zhou X, Lin H, Lin H. Global sensitivity analysis. In: Shekhar S, Xiong H, editors. Encyclopedia of GIS. Boston, MA: Springer US; 2008. pp. 408\u20139."},{"key":"1376_CR51","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1021\/ci950187r","volume":"36","author":"E Estrada","year":"1996","unstructured":"Estrada E. Spectral moments of the edge adjacency matrix in molecular graphs. 1. Definition and applications for the prediction of physical properties of alkanes. J Chem Inf Comput Sci. 1996;36:844\u201349.","journal-title":"J Chem Inf Comput Sci"},{"key":"1376_CR52","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1021\/ci960113v","volume":"37","author":"E Estrada","year":"1997","unstructured":"Estrada E. Spectral moments of the edge adjacency matrix in molecular graphs. 2. Molecules containing heteroatoms and QSAR applications. J Chem Inf Comput Sci. 1997;37:320\u201328.","journal-title":"J Chem Inf Comput Sci"},{"key":"1376_CR53","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1021\/ci970030u","volume":"38","author":"E Estrada","year":"1998","unstructured":"Estrada E. Spectral moments of the edge adjacency matrix in molecular graphs. 3. Molecules containing cycles. J Chem Inf Comput Sci. 1998;38:23\u20137.","journal-title":"J Chem Inf Comput Sci"},{"key":"1376_CR54","doi-asserted-by":"publisher","first-page":"213","DOI":"10.2174\/138955708783744128","volume":"8","author":"E Estrada","year":"2008","unstructured":"Estrada E. How the parts organize in the whole? A top-down view of molecular descriptors and properties for QSAR and drug design. Mini Rev Med Chem. 2008;8:213\u201321.","journal-title":"Mini Rev Med Chem"},{"key":"1376_CR55","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1021\/ci0342425","volume":"44","author":"E Estrada","year":"2004","unstructured":"Estrada E, Patlewicz G, Gutierrez Y. From knowledge generation to knowledge archive. A general strategy using TOPS-MODE with DEREK to formulate new alerts for skin sensitization. J Chem Inf Comput Sci. 2004;44:688\u201398.","journal-title":"J Chem Inf Comput Sci"},{"key":"1376_CR56","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.jmgm.2006.01.002","volume":"25","author":"E Estrada","year":"2006","unstructured":"Estrada E, Molina E. Automatic extraction of structural alerts for predicting chromosome aberrations of organic compounds. J Mol Graph Model. 2006;25:275\u201388.","journal-title":"J Mol Graph Model"},{"key":"1376_CR57","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1021\/ci00023a004","volume":"35","author":"E Estrada","year":"1995","unstructured":"Estrada E. Edge adjacency relationship and a novel topological index related to molecular volume. J Chem Inf Comput Sci. 1995;35:31\u20133.","journal-title":"J Chem Inf Comput Sci"},{"key":"1376_CR58","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1021\/ci00026a005","volume":"35","author":"E Estrada","year":"1995","unstructured":"Estrada E. Edge adjacency relationships in molecular graphs containing heteroatoms: a new topological index related to molar volume. J Chem Inf Comput Sci. 1995;35:701\u20137.","journal-title":"J Chem Inf Comput Sci"},{"key":"1376_CR59","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1021\/ci990030p","volume":"39","author":"E Estrada","year":"1999","unstructured":"Estrada E, Rodr\u00edguez L. Edge-connectivity indices in QSPR\/QSAR studies. 1. Comparison to other Topological indices in QSPR studies. J Chem Inf Comput Sci. 1999;39:1037\u201341.","journal-title":"J Chem Inf Comput Sci"},{"key":"1376_CR60","doi-asserted-by":"publisher","first-page":"9085","DOI":"10.1021\/jp026238m","volume":"106","author":"E Estrada","year":"2002","unstructured":"Estrada E. Physicochemical Interpretation of Molecular Connectivity Indices. J Phys Chem A. 2002;106:9085\u201391.","journal-title":"J Phys Chem A"},{"key":"1376_CR61","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1021\/ci000095o","volume":"41","author":"M Randi\u0107","year":"2001","unstructured":"Randi\u0107 M, Zupan J. On interpretation of well-known topological indices. J Chem Inf Comput Sci. 2001;41:550\u201360.","journal-title":"J Chem Inf Comput Sci"},{"key":"1376_CR62","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/s10822-009-9260-9","volume":"23","author":"J Overington","year":"2009","unstructured":"Overington J, ChEMBL. An interview with John Overington, team leader, chemogenomics at the European Bioinformatics Institute Outstation of the European Molecular Biology Laboratory (EMBL-EBI). Interview by Wendy A. Warr. J Comput Aided Mol Des. 2009;23:195\u20138.","journal-title":"J Comput Aided Mol Des"},{"key":"1376_CR63","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1021\/ci049714+","volume":"45","author":"JJ Irwin","year":"2005","unstructured":"Irwin JJ, Shoichet BK. ZINC\u2013a free database of commercially available compounds for virtual screening. J Chem Inf Model. 2005;45:177\u201382.","journal-title":"J Chem Inf Model"},{"key":"1376_CR64","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.ddtec.2015.01.005","volume":"14","author":"A Hersey","year":"2015","unstructured":"Hersey A, Chambers J, Bellis L, Patricia Bento A, Gaulton A, Overington JP. Chemical databases: curation or integration by user-defined equivalence? Drug Discov Today Technol. 2015;14:17\u201324.","journal-title":"Drug Discov Today Technol"},{"key":"1376_CR65","doi-asserted-by":"publisher","first-page":"D1220","DOI":"10.1093\/nar\/gkv1253","volume":"44","author":"G Papadatos","year":"2016","unstructured":"Papadatos G, Davies M, Dedman N, Chambers J, Gaulton A, Siddle J, et al. SureChEMBL: a large-scale, chemically annotated patent document database. Nucleic Acids Res. 2016;44:D1220\u20138.","journal-title":"Nucleic Acids Res"},{"key":"1376_CR66","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1007\/978-1-0716-0150-1_32","volume-title":"Ecotoxicological QSARs","author":"A Mauri","year":"2020","unstructured":"Mauri A, alvaDesc. A Tool to calculate and analyze molecular descriptors and fingerprints. In: Roy K, editor. Ecotoxicological QSARs. New York, NY: Springer US; 2020. pp. 801\u201320."},{"key":"1376_CR67","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0169-409X(00)00129-0","volume":"46","author":"CA Lipinski","year":"2001","unstructured":"Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001;46:3\u201326.","journal-title":"Adv Drug Deliv Rev"},{"key":"1376_CR68","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1021\/cc9800071","volume":"1","author":"AK Ghose","year":"1999","unstructured":"Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J Comb Chem. 1999;1:55\u201368.","journal-title":"J Comb Chem"},{"key":"1376_CR69","doi-asserted-by":"publisher","first-page":"2615","DOI":"10.1021\/jm020017n","volume":"45","author":"DF Veber","year":"2002","unstructured":"Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002;45:2615\u201323.","journal-title":"J Med Chem"},{"key":"1376_CR70","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s13321-018-0283-x","volume":"10","author":"J Dong","year":"2018","unstructured":"Dong J, Wang NN, Yao ZJ, Zhang L, Cheng Y, Ouyang D, et al. ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. J Cheminformatics. 2018;10:29.","journal-title":"J Cheminformatics"}],"container-title":["BMC Chemistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13065-024-01376-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13065-024-01376-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13065-024-01376-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T08:03:01Z","timestamp":1745913781000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcchem.biomedcentral.com\/articles\/10.1186\/s13065-024-01376-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,2]]},"references-count":70,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1376"],"URL":"https:\/\/doi.org\/10.1186\/s13065-024-01376-z","relation":{},"ISSN":["2661-801X"],"issn-type":[{"type":"electronic","value":"2661-801X"}],"subject":[],"published":{"date-parts":[[2025,1,2]]},"assertion":[{"value":"4 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2025","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The original online version of this article was revised: The ORCID IDs are correctly given for the authors.","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"2"}}