[1]
Alacreu, M.; Pardo, J.; Azorín, M.; Climent, M.T.; Gasull, V.; Moreno, L. Importance of increasing modifiable risk factors knowledge on alzheimer’s disease among community pharmacists and general practitioners in Spain. Front. Pharmacol., 2019, 10, 860. [http://dx.doi.org/10.3389/fphar.2019.00860]. [PMID: 31474852].
[2]
De Simone, A.; La Pietra, V.; Betari, N.; Petragnani, N.; Conte, M.; Daniele, S.; Pietrobono, D.; Martini, C.; Petralla, S.; Casadei, R.; Davani, L.; Frabetti, F.; Russomanno, P.; Novellino, E.; Montanari, S.; Tumiatti, V.; Ballerini, P.; Sarno, F.; Nebbioso, A.; Altucci, L.; Monti, B.; Andrisano, V.; Milelli, A. Discovery of the first-in-class gsk-3β/HDAC dual inhibitor as disease-modifying agent to combat Alzheimer’s Disease. ACS Med. Chem. Lett., 2019, 10(4), 469-474. [http://dx.doi.org/10.1021/acsmedchemlett.8b00507]. [PMID: 30996781].
[3]
Mullard, A. Pfizer exits neuroscience. Nat. Rev. Drug Discov., 2018, 17(2), 86. [PMID: 29386603].
[4]
Oset-Gasque, M.J.; Marco-Contelles, J. Alzheimer’s disease, the “one-molecule, one-target” paradigm, and the multitarget directed ligand approach. ACS Chem. Neurosci., 2018, 9(3), 401-403. [http://dx.doi.org/10.1021/acschemneuro.8b00069]. [PMID: 29465220].
[5]
Hooper, C.; Killick, R.; Lovestone, S. The GSK3 hypothesis of Alzheimer’s disease. J. Neurochem., 2008, 104(6), 1433-1439. [http://dx.doi.org/10.1111/j.1471-4159.2007.05194.x]. [PMID: 18088381].
[6]
Hwang, J.Y.; Aromolaran, K.A.; Zukin, R.S. The emerging field of epigenetics in neurodegeneration and neuroprotection. Nat. Rev. Neurosci., 2017, 18(6), 347-361. [http://dx.doi.org/10.1038/nrn.2017.46]. [PMID: 28515491].
[7]
Fischer, A. Targeting histone-modifications in Alzheimer’s disease. What is the evidence that this is a promising therapeutic avenue? Neuropharmacology, 2014, 80, 95-102. [http://dx.doi.org/10.1016/j.neuropharm.2014.01.038]. [PMID: 24486385].
[8]
Bardai, F.H.; Price, V.; Zaayman, M.; Wang, L.; D’Mello, S.R. Histone deacetylase-1 (HDAC1) is a molecular switch between neuronal survival and death. J. Biol. Chem., 2012, 287(42), 35444-35453. [http://dx.doi.org/10.1074/jbc.M112.394544]. [PMID: 22918830].
[9]
Noble, W.; Hanger, D.P.; Miller, C.C.; Lovestone, S. The importance of tau phosphorylation for neurodegenerative diseases. Front. Neurol., 2013, 4, 83. [http://dx.doi.org/10.3389/fneur.2013.00083]. [PMID: 23847585].
[10]
Fang, J.; Huang, D.; Zhao, W.; Ge, H.; Luo, H.B.; Xu, J. A new protocol for predicting novel GSK-3β ATP competitive inhibitors. J. Chem. Inf. Model., 2011, 51(6), 1431-1438. [http://dx.doi.org/10.1021/ci2001154]. [PMID: 21615159].
[11]
Paudel, P.; Seong, S.H.; Zhou, Y.; Park, C.H.; Yokozawa, T.; Jung, H.A.; Choi, J.S. Rosmarinic acid derivatives’ inhibition of glycogen synthase kinase-3β is the pharmacological basis of kangen-karyu in alzheimer’s disease. Molecules, 2018, 23(11), 23. [http://dx.doi.org/10.3390/molecules23112919]. [PMID: 30413117].
[12]
Ruzic, D.; Petkovic, M.; Agbaba, D.; Ganesan, A.; Nikolic, K. Combined ligand and fragment-based drug design of selective histone deacetylase - 6 inhibitors. Mol. Inform., 2019, 38(5)e1800083 [http://dx.doi.org/10.1002/minf.201800083]. [PMID: 30632697].
[13]
Patel, P.; Patel, V.K.; Singh, A.; Jawaid, T.; Kamal, M.; Rajak, H. Identification of hydroxamic acid based selective hdac1 inhibitors: computer aided drug design studies. Curr Comput Aided Drug Des, 2019, 15(2), 145-166. [http://dx.doi.org/10.2174/1573409914666180502113135]. [PMID: 29732991].
[14]
Choubey, S.K.; Jeyaraman, J. A mechanistic approach to explore novel HDAC1 inhibitor using pharmacophore modeling, 3D- QSAR analysis, molecular docking, density functional and molecular dynamics simulation study. J. Mol. Graph. Model., 2016, 70, 54-69. [http://dx.doi.org/10.1016/j.jmgm.2016.09.008]. [PMID: 27668885].
[15]
Zhu, J.; Wu, Y.; Xu, L.; Jin, J. Theoretical studies on the selectivity mechanisms of glycogen synthase kinase 3beta (gsk3beta) with pyrazine atp-competitive inhibitors by 3d-qsar, molecular docking, molecular dynamics simulation and free energy calculations. Curr Comput Aided Drug Des, 2020, 16(1), 17-30.
[16]
Speck-Planche, A.; Cordeiro, M.N.D.S. Speck-Planche, A.; Cordeiro, M.N.D.S. Multi-tasking chemoinformatic model for the efficient discovery of potent and safer anti-bladder cancer agents. In: Bladder cancer: Risk factors, emerging treatment strategies and challenges; Haggerty, S., Ed.; Nova Science Publishers, Inc.: New York, , 2014; pp. 71-93.
[17]
Bediaga, H.; Arrasate, S.; González-Díaz, H. PTML combinatorial model of chembl compounds assays for multiple types of cancer. ACS Comb. Sci., 2018, 20(11), 621-632. [http://dx.doi.org/10.1021/acscombsci.8b00090]. [PMID: 30240186].
[18]
Speck-Planche, A.; Cordeiro, M.N.D.S. Speck-Planche, A.; Cordeiro, M.N.D.S. Speeding up the virtual design and screening of therapeutic peptides: simultaneous prediction of anticancer activity and cytotoxicity. In: Multi-Scale Approaches in Drug Discovery; Speck-Planche, A., Ed.; Elsevier: Amsterdam, , 2017; pp. 127-147. [http://dx.doi.org/10.1016/B978-0-08-101129-4.00006-0]
[19]
Kleandrova, V.V.; Ruso, J.M.; Speck-Planche, A.; Dias Soeiro Cordeiro, M.N. Enabling the discovery and virtual screening of potent and safe antimicrobial peptides. simultaneous prediction of antibacterial activity and cytotoxicity. ACS Comb. Sci., 2016, 18(8), 490-498. [http://dx.doi.org/10.1021/acscombsci.6b00063]. [PMID: 27280735].
[20]
Speck-Planche, A.; Cordeiro, M.N.D.S. Enabling virtual screening of potent and safer antimicrobial agents against noma: mtk-QSBER model for simultaneous prediction of antibacterial activities and ADMET properties. Mini Rev. Med. Chem., 2015, 15(3), 194-202. [http://dx.doi.org/10.2174/138955751503150312120519]. [PMID: 25769968].
[21]
Speck-Planche, A.; Cordeiro, M.N.D.S. Computer-aided discovery in antimicrobial research: In silico model for virtual screening of potent and safe anti-pseudomonas agents. Comb. Chem. High Throughput Screen., 2015, 18(3), 305-314. [http://dx.doi.org/10.2174/1386207318666150305144249]. [PMID: 25747443].
[22]
Speck-Planche, A.; Cordeiro, M.N.D.S. Chemoinformatics for medicinal chemistry: in silico model to enable the discovery of potent and safer anti-cocci agents. Future Med. Chem., 2014, 6(18), 2013-2028. [http://dx.doi.org/10.4155/fmc.14.136]. [PMID: 25531966].
[23]
Speck-Planche, A.; Cordeiro, M.N.D.S. Review of current chemoinformatic tools for modeling important aspects of CYPs-mediated drug metabolism. Integrating metabolism data with other biological profiles to enhance drug discovery. Curr. Drug Metab., 2014, 15(4), 429-440. [http://dx.doi.org/10.2174/1389200215666140605124002]. [PMID: 24909424].
[24]
Speck-Planche, A.; Cordeiro, M.N.D.S. Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: A chemoinformatic complementary approach for high-throughput screening. ACS Comb. Sci., 2014, 16(2), 78-84. [http://dx.doi.org/10.1021/co400115s]. [PMID: 24383958].
[25]
Herrera-Ibatá, D.M.; Pazos, A.; Orbegozo-Medina, R.A.; Romero-Durán, F.J.; González-Díaz, H. Mapping chemical structure-activity information of HAART-drug cocktails over complex networks of AIDS epidemiology and socioeconomic data of U.S. counties. Biosystems, 2015, 132-133, 20-34. [http://dx.doi.org/10.1016/j.biosystems.2015.04.007]. [PMID: 25916548].
[26]
Herrera-Ibata, D.M.; Orbegozo-Medina, R.A.; Gonzalez-Diaz, H. Multiscale mapping of AIDS in U.S. countries vs anti-HIV drugs activity with complex networks and information indices. Curr. Bioinform., 2015, 10, 639-657. [http://dx.doi.org/10.2174/1574893610666151008012648].
[27]
Herrera-Ibata, D.M.; Pazos, A.; Orbegozo-Medina, R.A.; Gonzalez-Diaz, H. Mapping networks of anti-HIV drug cocktails vs. AIDS epidemiology in the US counties. Chemom. Intell. Lab. Syst., 2014, 138, 161-170. [http://dx.doi.org/10.1016/j.chemolab.2014.08.006].
[28]
González-Díaz, H.; Herrera-Ibatá, D.M.; Duardo-Sánchez, A.; Munteanu, C.R.; Orbegozo-Medina, R.A.; Pazos, A. ANN multiscale model of anti-HIV drugs activity vs AIDS prevalence in the US at county level based on information indices of molecular graphs and social networks. J. Chem. Inf. Model., 2014, 54(3), 744-755. [http://dx.doi.org/10.1021/ci400716y]. [PMID: 24521170].
[29]
Vásquez-Domínguez, E.; Armijos-Jaramillo, V.D.; Tejera, E.; González-Díaz, H. Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds. Mol. Pharm., 2019, 16(10), 4200-4212. [http://dx.doi.org/10.1021/acs.molpharmaceut.9b00538]. [PMID: 31426639].
[30]
Speck-Planche, A.; Kleandrova, V.V.; Cordeiro, M.N.D.S. Chemoinformatics for rational discovery of safe antibacterial drugs: simultaneous predictions of biological activity against streptococci and toxicological profiles in laboratory animals. Bioorg. Med. Chem., 2013, 21(10), 2727-2732. [http://dx.doi.org/10.1016/j.bmc.2013.03.015]. [PMID: 23582445].
[31]
Speck-Planche, A.; Kleandrova, V.V.; Cordeiro, M.N.D.S. New insights toward the discovery of antibacterial agents: multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugs. Eur. J. Pharm. Sci., 2013, 48(4-5), 812-818. [http://dx.doi.org/10.1016/j.ejps.2013.01.011]. [PMID: 23376211].
[32]
Speck Planche, A.; Cordeiro, M.N.D.S. In: Chemoinformatics in drug design. Artificial neural networks for simultaneous prediction of anti-enterococci activities and toxicological profiles. Proceedings of the 5th International Joint Conference on Computational Intelligence, NCTA-International Conference on Neural Computation Theory and Applications, Vilamoura, Algarve, Portugal, September 20-22, 2013; Institute for Systems and Technologies of Information, Control and Communication (INSTICC): Vilamoura, Algarve, Portugal, , 2013; pp. 458-465.
[33]
Nocedo-Mena, D.; Cornelio, C.; Camacho-Corona, M.D.R.; Garza-González, E.; Waksman de Torres, N.; Arrasate, S.; Sotomayor, N.; Lete, E.; González-Díaz, H. Modeling antibacterial activity with machine learning and fusion of chemical structure information with microorganism metabolic networks. J. Chem. Inf. Model., 2019, 59(3), 1109-1120. [http://dx.doi.org/10.1021/acs.jcim.9b00034]. [PMID: 30802402].
[34]
Concu, R.; Kleandrova, V.V.; Speck-Planche, A.; Cordeiro, M.N.D.S. Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory. Nanotoxicology, 2017, 11(7), 891-906. [http://dx.doi.org/10.1080/17435390.2017.1379567]. [PMID: 28937298].
[35]
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N.D.S. Computational modeling in nanomedicine: prediction of multiple antibacterial profiles of nanoparticles using a quantitative structure-activity relationship perturbation model. Nanomedicine (Lond.), 2015, 10(2), 193-204. [http://dx.doi.org/10.2217/nnm.14.96]. [PMID: 25600965].
[36]
Luan, F.; Kleandrova, V.V.; González-Díaz, H.; Ruso, J.M.; Melo, A.; Speck-Planche, A.; Cordeiro, M.N.D.S. Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach. Nanoscale, 2014, 6(18), 10623-10630. [http://dx.doi.org/10.1039/C4NR01285B]. [PMID: 25083742].
[37]
Kleandrova, V.V.; Luan, F.; González-Díaz, H.; Ruso, J.M.; Speck-Planche, A.; Cordeiro, M.N.D.S. Computational tool for risk assessment of nanomaterials: novel QSTR-perturbation model for simultaneous prediction of ecotoxicity and cytotoxicity of uncoated and coated nanoparticles under multiple experimental conditions. Environ. Sci. Technol., 2014, 48(24), 14686-14694. [http://dx.doi.org/10.1021/es503861x]. [PMID: 25384130].
[38]
Kleandrova, V.V.; Luan, F.; González-Díaz, H.; Ruso, J.M.; Melo, A.; Speck-Planche, A.; Cordeiro, M.N.D.S. Computational ecotoxicology: simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions. Environ. Int., 2014, 73, 288-294. [http://dx.doi.org/10.1016/j.envint.2014.08.009]. [PMID: 25173945].
[39]
Martínez-Arzate, S.G.; Tenorio-Borroto, E.; Barbabosa Pliego, A.; Díaz-Albiter, H.M.; Vázquez-Chagoyán, J.C.; González-Díaz, H. PTML Model for proteome mining of B-cell epitopes and theoretical-experimental study of bm86 protein sequences from Colima, Mexico. J. Proteome Res., 2017, 16(11), 4093-4103. [http://dx.doi.org/10.1021/acs.jproteome.7b00477]. [PMID: 28922600].
[40]
Tenorio-Borroto, E.; Castañedo, N.; García-Mera, X.; Rivadeneira, K.; Vázquez Chagoyán, J.C.; Barbabosa Pliego, A.; Munteanu, C.R.; González-Díaz, H. Perturbation theory machine learning modeling of immunotoxicity for drugs targeting inflammatory cytokines and study of the antimicrobial g1 using cytometric bead arrays. Chem. Res. Toxicol., 2019, 32(9), 1811-1823. [http://dx.doi.org/10.1021/acs.chemrestox.9b00154]. [PMID: 31327231].
[41]
Tenorio-Borroto, E.; Ramirez, F.R.; Speck-Planche, A.; Cordeiro, M.N.D.S.; Luan, F.; Gonzalez-Diaz, H. QSPR and flow cytometry analysis (QSPR-FCA): review and new findings on parallel study of multiple interactions of chemical compounds with immune cellular and molecular targets. Curr. Drug Metab., 2014, 15(4), 414-428. [http://dx.doi.org/10.2174/1389200215666140908101152]. [PMID: 25204826].
[42]
Tenorio-Borroto, E.; Peñuelas-Rivas, C.G.; Vásquez-Chagoyán, J.C.; Castañedo, N.; Prado-Prado, F.J.; García-Mera, X.; González-Díaz, H. Model for high-throughput screening of drug immunotoxicity--study of the anti-microbial G1 over peritoneal macrophages using flow cytometry. Eur. J. Med. Chem., 2014, 72, 206-220. [http://dx.doi.org/10.1016/j.ejmech.2013.08.035]. [PMID: 24445280].
[43]
González-Díaz, H.; Pérez-Montoto, L.G.; Ubeira, F.M. Model for vaccine design by prediction of B-epitopes of IEDB given perturbations in peptide sequence, in vivo process, experimental techniques, and source or host organisms. J. Immunol. Res., 2014, 2014768515 [http://dx.doi.org/10.1155/2014/768515]. [PMID: 24741624].
[44]
Tenorio-Borroto, E.; García-Mera, X.; Peñuelas-Rivas, C.G.; Vásquez-Chagoyán, J.C.; Prado-Prado, F.J.; Castañedo, N.; González-Díaz, H. Entropy model for multiplex drug-target interaction endpoints of drug immunotoxicity. Curr. Top. Med. Chem., 2013, 13(14), 1636-1649. [http://dx.doi.org/10.2174/15680266113139990114]. [PMID: 23889053].
[45]
Romero-Durán, F.J.; Alonso, N.; Yañez, M.; Caamaño, O.; García-Mera, X.; González-Díaz, H. Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives. Neuropharmacology, 2016, 103, 270-278. [http://dx.doi.org/10.1016/j.neuropharm.2015.12.019]. [PMID: 26721628].
[46]
Romero Durán, F.J.; Alonso, N.; Caamaño, O.; García-Mera, X.; Yañez, M.; Prado-Prado, F.J.; González-Díaz, H. Prediction of multi-target networks of neuroprotective compounds with entropy indices and synthesis, assay, and theoretical study of new asymmetric 1,2-rasagiline carbamates. Int. J. Mol. Sci., 2014, 15(9), 17035-17064. [http://dx.doi.org/10.3390/ijms150917035]. [PMID: 25255029].
[47]
Luan, F.; Cordeiro, M.N.D.S.; Alonso, N.; García-Mera, X.; Caamaño, O.; Romero-Duran, F.J.; Yañez, M.; González-Díaz, H. TOPS-MODE model of multiplexing neuroprotective effects of drugs and experimental-theoretic study of new 1,3-rasagiline derivatives potentially useful in neurodegenerative diseases. Bioorg. Med. Chem., 2013, 21(7), 1870-1879. [http://dx.doi.org/10.1016/j.bmc.2013.01.035]. [PMID: 23415089].
[48]
Ferreira da Costa, J.; Silva, D.; Caamaño, O.; Brea, J.M.; Loza, M.I.; Munteanu, C.R.; Pazos, A.; García-Mera, X.; González-Díaz, H. Perturbation theory/machine learning model of chembl data for dopamine targets: docking, synthesis, and assay of new l-prolyl-l-leucyl-glycinamide peptidomimetics. ACS Chem. Neurosci., 2018, 9(11), 2572-2587. [http://dx.doi.org/10.1021/acschemneuro.8b00083]. [PMID: 29791132].
[49]
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N.D.S. Multi-target inhibitors for proteins associated with Alzheimer: In silico discovery using fragment-based descriptors. Curr. Alzheimer Res., 2013, 10(2), 117-124. [http://dx.doi.org/10.2174/1567205011310020001]. [PMID: 22515494].
[50]
Alonso, N.; Caamaño, O.; Romero-Duran, F.J.; Luan, F.D.S.; Cordeiro, M.N.; Yañez, M.; González-Díaz, H.; García-Mera, X. Model for high-throughput screening of multitarget drugs in chemical neurosciences: synthesis, assay, and theoretic study of rasagiline carbamates. ACS Chem. Neurosci 2013, 4(10), 1393-1403. [http://dx.doi.org/10.1021/cn400111n]. [PMID: 23855599].
[51]
Gaulton, A.; Bellis, L.J.; Bento, A.P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; Overington, J.P. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res., 2012, 40(Database issue), D1100-D1107. [http://dx.doi.org/10.1093/nar/gkr777]. [PMID: 21948594].
[52]
Anderson, A.C. The process of structure-based drug design. Chem. Biol., 2003, 10(9), 787-797. [http://dx.doi.org/10.1016/j.chembiol.2003.09.002]. [PMID: 14522049].
[53]
ChemAxon. Standardizer, v19.18.0, Budapest, Hungary,1998-2019. Available from: https://www.chemaxon.com.
[54]
Valdés-Martiní, J.R.; Marrero-Ponce, Y.; García-Jacas, C.R.; Martinez-Mayorga, K.; Barigye, S.J.; Vaz d’Almeida, Y.S.; Pham-The, H.; Pérez-Giménez, F.; Morell, C.A. QuBiLS-MAS, open source multi-platform software for atom- and bond-based topological (2D) and chiral (2.5D) algebraic molecular descriptors computations. J. Cheminform., 2017, 9(1), 35. [http://dx.doi.org/10.1186/s13321-017-0211-5]. [PMID: 29086120].
[56]
Speck-Planche, A.; Kleandrova, V.V.; Cordeiro, M.N.D.S. Speck-Planche, A.; Kleandrova, V.V.; Cordeiro, M.N.D.S. In:Chemoinformatics in antibacterial drug discovery: Simultaneousmodeling of anti-enterococci activities and ADMET profiles throughthe use of probabilistic quadratic indices. Proceedings of 19th Int.Electron. Conf. Synth. Org. Chem., Multidisciplinary DigitalPublishing Institute (MDPI), and University of Santiago de Compostela (USC): Santiago, Spain, 2015, 19,p. e003.
[57]
Medina Marrero, R.; Marrero-Ponce, Y.; Barigye, S.J.; Echeverría Díaz, Y.; Acevedo-Barrios, R.; Casañola-Martín, G.M.; García Bernal, M.; Torrens, F.; Pérez-Giménez, F. QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents. SAR QSAR Environ. Res., 2015, 26(11), 943-958. [http://dx.doi.org/10.1080/1062936X.2015.1104517]. [PMID: 26567876].
[58]
Marrero-Ponce, Y.; Siverio-Mota, D.; Gálvez-Llompart, M.; Recio, M.C.; Giner, R.M.; García-Domènech, R.; Torrens, F.; Arán, V.J.; Cordero-Maldonado, M.L.; Esguera, C.V.; de Witte, P.A.; Crawford, A.D. Discovery of novel anti-inflammatory drug-like compounds by aligning in silico and in vivo screening: the nitroindazolinone chemotype. Eur. J. Med. Chem., 2011, 46(12), 5736-5753. [http://dx.doi.org/10.1016/j.ejmech.2011.07.053]. [PMID: 22000935].
[59]
Kleandrova, V.V.; Luan, F.; Speck-Planche, A.; Cordeiro, M.N.D.S. In silico assessment of the acute toxicity of chemicals: recent advances and new model for multitasking prediction of toxic effect. Mini Rev. Med. Chem., 2015, 15(8), 677-686. [http://dx.doi.org/10.2174/1389557515666150219143604]. [PMID: 25694074].
[60]
González-Díaz, H.; Arrasate, S.; Gómez-SanJuan, A.; Sotomayor, N.; Lete, E.; Besada-Porto, L.; Ruso, J.M. 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(14), 1713-1741. [http://dx.doi.org/10.2174/1568026611313140011]. [PMID: 23889050].
[62]
Matthews, B.W. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta, 1975, 405(2), 442-451. [http://dx.doi.org/10.1016/0005-2795(75)90109-9]. [PMID: 1180967].
[63]
Pearson, K. Notes on regression and inheritance in the case of two parents. Proc. R. Soc. Lond., 1895, 58, 240-242. [http://dx.doi.org/10.1098/rspl.1895.0041].
[64]
Sahigara, F.; Mansouri, K.; Ballabio, D.; Mauri, A.; Consonni, V.; Todeschini, R. Comparison of different approaches to define the applicability domain of QSAR models. Molecules, 2012, 17(5), 4791-4810. [http://dx.doi.org/10.3390/molecules17054791]. [PMID: 22534664].
[65]
Speck-Planche, A.; Kleandrova, V.V. QSAR and molecular docking techniques for the discovery of potent monoamine oxidase B inhibitors: computer-aided generation of new rasagiline bioisosteres. Curr. Top. Med. Chem., 2012, 12(16), 1734-1747. [http://dx.doi.org/10.2174/1568026611209061734]. [PMID: 23030609].
[66]
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(11), 14704-14716. [http://dx.doi.org/10.1021/acsomega.8b02419]. [PMID: 30555986].
[67]
Baskin, I.I.; Skvortsova, M.I.; Stankevich, I.V.; Zefirov, N.S. On the basis of invariants of labeled molecular graphs. J. Chem. Inf. Comput. Sci., 1995, 35, 527-531. [http://dx.doi.org/10.1021/ci00025a021].
[68]
Speck-Planche, A. Multicellular target QSAR model for simultaneous prediction and design of anti-pancreatic cancer agents. ACS Omega, 2019, 4, 3122-3132. [http://dx.doi.org/10.1021/acsomega.8b03693].
[69]
Speck-Planche, A.; Scotti, M.T. BET bromodomain inhibitors: fragment-based in silico design using multi-target QSAR models. Mol. Divers., 2019, 23(3), 555-572. [http://dx.doi.org/10.1007/s11030-018-9890-8]. [PMID: 30421269].
[70]
Speck-Planche, A.; Cordeiro, M.N.D.S. Fragment-based in silico modeling of multi-target inhibitors against breast cancer-related proteins. Mol. Divers., 2017, 21(3), 511-523. [http://dx.doi.org/10.1007/s11030-017-9731-1]. [PMID: 28194627].
[71]
Kleandrova, V.V.; Speck-Planche, A. Multitasking model for computer-aided design and virtual screening of compounds with high anti-hiv activity and desirable admet properties. Multi-Scale Approaches in Drug Discovery; Speck-Planche, A., Ed.; Elsevier: Amsterdam, , 2017; pp. 55-81. [http://dx.doi.org/10.1016/B978-0-08-101129-4.00003-5.
[72]
Ghose, A.K.; Viswanadhan, V.N.; Wendoloski, J.J. Prediction of hydrophobic (lipophilic) properties of small organic molecules using fragmental methods: an analysis of ALOGP and CLOGP methods. J. Phys. Chem. A, 1998, 102, 3762-3772. [http://dx.doi.org/10.1021/jp980230o].
[73]
Speck-Planche, A.; Dias Soeiro Cordeiro, M.N. Speeding up early drug discovery in antiviral research: a fragment-based in silico approach for the design of virtual anti-hepatitis C leads. ACS Comb. Sci., 2017, 19(8), 501-512. [http://dx.doi.org/10.1021/acscombsci.7b00039]. [PMID: 28437091].
[74]
Speck-Planche, A.; Cordeiro, M.N.D.S. De novo computational design of compounds virtually displaying potent antibacterial activity and desirable in vitro ADMET profiles. Med. Chem. Res., 2017, 26, 2345-2356. [http://dx.doi.org/10.1007/s00044-017-1936-4].
[75]
Irwin, J.J.; Shoichet, B.K. ZINC--a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model., 2005, 45(1), 177-182. [http://dx.doi.org/10.1021/ci049714+]. [PMID: 15667143].
[76]
Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev., 2001, 46(1-3), 3-26. [http://dx.doi.org/10.1016/S0169-409X(00)00129-0]. [PMID: 11259830].
[77]
Ghose, A.K.; Viswanadhan, V.N.; Wendoloski, J.J. 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(1), 55-68. [http://dx.doi.org/10.1021/cc9800071]. [PMID: 10746014].
[78]
Veber, D.F.; Johnson, S.R.; Cheng, H.Y.; Smith, B.R.; Ward, K.W.; Kopple, K.D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem., 2002, 45(12), 2615-2623. [http://dx.doi.org/10.1021/jm020017n]. [PMID: 12036371].
[79]
Alvascience-Srl. AlvaDesc (software for molecular descriptor calculation), v1.0.14 Available from, 2019. [: https://www.alvascience.com/.]