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Current Topics in Medicinal Chemistry


ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

Research Article

PTML Modeling for Alzheimer’s Disease: Design and Prediction of Virtual Multi-Target Inhibitors of GSK3B, HDAC1, and HDAC6

Author(s): Valeria V. Kleandrova and Alejandro Speck-Planche*

Volume 20 , Issue 19 , 2020

Page: [1661 - 1676] Pages: 16

DOI: 10.2174/1568026620666200607190951

Price: $65


Background: Alzheimer’s disease is characterized by a progressive pattern of cognitive and functional impairment, which ultimately leads to death. Computational approaches have played an important role in the context of drug discovery for anti-Alzheimer's therapies. However, most of the computational models reported to date have been focused on only one protein associated with Alzheimer's, while relying on small datasets of structurally related molecules.

Objective: We introduce the first model combining perturbation theory and machine learning based on artificial neural networks (PTML-ANN) for simultaneous prediction and design of inhibitors of three Alzheimer’s disease-related proteins, namely glycogen synthase kinase 3 beta (GSK3B), histone deacetylase 1 (HDAC1), and histone deacetylase 6 (HDAC6).

Methods: The PTML-ANN model was obtained from a dataset retrieved from ChEMBL, and it relied on a classification approach to predict chemicals as active or inactive.

Results: The PTML-ANN model displayed sensitivity and specificity higher than 85% in both training and test sets. The physicochemical and structural interpretation of the molecular descriptors in the model permitted the direct extraction of fragments suggested to favorably contribute to enhancing the multitarget inhibitory activity. Based on this information, we assembled ten molecules from several fragments with positive contributions. Seven of these molecules were predicted as triple target inhibitors while the remaining three were predicted as dual-target inhibitors. The estimated physicochemical properties of the designed molecules complied with Lipinski’s rule of five and its variants.

Conclusion: This work opens new horizons toward the design of multi-target inhibitors for anti- Alzheimer's therapies.

Keywords: Alzheimer's, Box-Jenkins approach, Molecular fragment, Multi-target, PTML-ANN model, Virtual design.

Graphical Abstract
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. []. [PMID: 31474852].
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. []. [PMID: 30996781].
Mullard, A. Pfizer exits neuroscience. Nat. Rev. Drug Discov., 2018, 17(2), 86. [PMID: 29386603].
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. []. [PMID: 29465220].
Hooper, C.; Killick, R.; Lovestone, S. The GSK3 hypothesis of Alzheimer’s disease. J. Neurochem., 2008, 104(6), 1433-1439. []. [PMID: 18088381].
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. []. [PMID: 28515491].
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. []. [PMID: 24486385].
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. []. [PMID: 22918830].
Noble, W.; Hanger, D.P.; Miller, C.C.; Lovestone, S. The importance of tau phosphorylation for neurodegenerative diseases. Front. Neurol., 2013, 4, 83. []. [PMID: 23847585].
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. []. [PMID: 21615159].
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. []. [PMID: 30413117].
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 []. [PMID: 30632697].
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. []. [PMID: 29732991].
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. []. [PMID: 27668885].
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.
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.
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. []. [PMID: 30240186].
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. []
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. []. [PMID: 27280735].
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. []. [PMID: 25769968].
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. []. [PMID: 25747443].
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. []. [PMID: 25531966].
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. []. [PMID: 24909424].
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. []. [PMID: 24383958].
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. []. [PMID: 25916548].
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. [].
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. [].
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. []. [PMID: 24521170].
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. []. [PMID: 31426639].
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. []. [PMID: 23582445].
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. []. [PMID: 23376211].
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.
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. []. [PMID: 30802402].
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. []. [PMID: 28937298].
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. []. [PMID: 25600965].
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. []. [PMID: 25083742].
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. []. [PMID: 25384130].
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. []. [PMID: 25173945].
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. []. [PMID: 28922600].
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. []. [PMID: 31327231].
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. []. [PMID: 25204826].
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. []. [PMID: 24445280].
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 []. [PMID: 24741624].
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. []. [PMID: 23889053].
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. []. [PMID: 26721628].
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. []. [PMID: 25255029].
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. []. [PMID: 23415089].
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. []. [PMID: 29791132].
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. []. [PMID: 22515494].
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. []. [PMID: 23855599].
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. []. [PMID: 21948594].
Anderson, A.C. The process of structure-based drug design. Chem. Biol., 2003, 10(9), 787-797. []. [PMID: 14522049].
ChemAxon. Standardizer, v19.18.0, Budapest, Hungary,1998-2019. Available from:
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. []. [PMID: 29086120].
Valdés-Martini, J.R.; García-Jacas, C.R.; Marrero-Ponce, Y. 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, CAMDBIR Unit; CENDA registration number: 2373-2012: Villa Clara, Cuba, 2012. available from:
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.
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. []. [PMID: 26567876].
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. []. [PMID: 22000935].
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. []. [PMID: 25694074].
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. []. [PMID: 23889050].
TIBCO-Software-Inc. STATISTICA (Data Analysis Software System), v13.5.0.17, Palo Alto, California, USA, 2018.
Matthews, B.W. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta, 1975, 405(2), 442-451. []. [PMID: 1180967].
Pearson, K. Notes on regression and inheritance in the case of two parents. Proc. R. Soc. Lond., 1895, 58, 240-242. [].
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. []. [PMID: 22534664].
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. []. [PMID: 23030609].
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. []. [PMID: 30555986].
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. [].
Speck-Planche, A. Multicellular target QSAR model for simultaneous prediction and design of anti-pancreatic cancer agents. ACS Omega, 2019, 4, 3122-3132. [].
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. []. [PMID: 30421269].
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. []. [PMID: 28194627].
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. [
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. [].
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. []. [PMID: 28437091].
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. [].
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. []. [PMID: 15667143].
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. []. [PMID: 11259830].
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. []. [PMID: 10746014].
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. []. [PMID: 12036371].
Alvascience-Srl. AlvaDesc (software for molecular descriptor calculation), v1.0.14 Available from, 2019. [:]

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