Abstract
Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence.
Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.
Keywords: Drug discovery, virtual screening, structure-based virtual screening, ligand-based virtual screening, machine learning, deep learning.
[1]
Sundaram, K.; Srinivasan, S. Computer simulated modeling of biomolecular systems. Comput. Programs Biomed., 1979, 10(1), 29-33.
[2]
Kanethisa, M.; Klein, P.; Greif, P.; DeLisi, C. Computer analysis and structure prediction of nucleic acid and proteins. Nucleic Acids Res., 1984, 12(Part1), 417-428.
[3]
Macarron, R.; Banks, M.N.; Bojanic, D.; Burns, D.J.; Cirovic, D.A.; Garyantes, T.; Green, D.V.S.; Hertzberg, R.P.; Janzen, W.P.; Paslay, J.W.; Schopfer, U.; Sittampalam, G.S. Impact of high-throughput screening in biomedical research. Nat. Rev. Drug Discov., 2011, 10(3), 188-195.
[4]
Bolten, B.M.; DeGregorio, T. Trends in development cycles. Nat. Rev. Drug Discov., 2002, 1(5), 335-336.
[7]
Lionta, E.; Spyrou, G.; Vassilatis, D.; Cournia, Z. Structure-based virtual screening for drug discovery: Principles, applications and recent advances. Curr. Top. Med. Chem., 2014, 14(16), 1923-1938.
[8]
Ripphausen, P.; Nisius, B.; Bajorath, J. State-of-the-Art in Ligand-based virtual screening. Drug Discov. Today, 2011, 16(9-10), 372-376.
[9]
Lima, A.N.; Philot, E.A.; Trossini, G.H.G.; Scott, L.P.B.; Maltarollo, V.G.; Honorio, K.M. Use of machine learning approaches for novel drug discovery. Expert Opin. Drug Discov., 2016, 11(3), 225-239.
[11]
Schmidhuber, J. Deep learning in neural networks: An overview. Neural Networks., 2015, 61, 85-117.
[12]
Deep Learning Web Site - Competitions http://deeplearning.net/tag/competitions/ (accessed Oct 7, 2018).
[13]
Merck Molecular Activity Challenge Winners https://www.kaggle.com/c/MerckActivity#winners (Accessed Oct
7, 2018).
[14]
Unterthiner, T.; Mayr, A.; Klambauer, G.; Steijaert, M.; Ceulemans, H.; Wegner, J.K.; Hochreiter, S. Deep learning as an opportunity in virtual screening. Workshop on Deep Learning and Representation Learning (NIPS 2014), 2014.
[15]
Bajorath, J. Integration of virtual and high-throughput screening. Nat. Rev. Drug Discov., 2002, 1(11), 882-894.
[16]
Doman, T.N.; McGovern, S.L.; Witherbee, B.J.; Kasten, T.P.; Kurumbail, R.; Stallings, W.C.; Connolly, D.T.; Shoichet, B.K. Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. J. Med. Chem., 2002, 45(11), 2213-2221.
[17]
Evensen, E.; Eksterowicz, J.E.; Stanton, R.V.; Oshiro, C.; Grootenhuis, P.D.J.; Bradley, E.K. Comparing performance of computational tools for combinatorial library design. J. Med. Chem., 2003, 46(24), 5125-5128.
[18]
Stahura, F.L.; Bajorath, J. Virtual screening methods that complement HTS. Comb. Chem. High Throughput Screen., 2004, 7(4), 259-269.
[19]
Elowe, N.H.; Blanchard, J.E.; Cechetto, J.D.; Brown, E.D. Experimental screening of dihydrofolate reductase yields a “Test Set” of 50,000 small molecules for a computational data-mining and docking competition. J. Biomol. Screen., 2005, 10(7), 653-657.
[20]
Paiva, A.M.; Vanderwall, D.E.; Blanchard, J.S.; Kozarich, J.W.; Williamson, J.M.; Kelly, T.M. Inhibitors of dihydrodipicolinate reductase, a key enzyme of the diaminopimelate pathway of mycobacterium tuberculosis. Biochim. Biophys. Acta, 2001, 1545(1-2), 67-77.
[21]
Brenk, R.; Irwin, J.J.; Shoichet, B.K. Here Be Dragons: Docking and screening in an uncharted region of chemical space. J. Biomol. Screen., 2005, 10(7), 667-674.
[22]
Schneider, G. Virtual screening: An endless staircase? Nat. Rev. Drug Discov., 2010, 9(4), 273-276.
[23]
Lavecchia, A.; Di Giovanni, C. Virtual screening strategies in drug discovery: A critical review. Curr. Med. Chem., 2013, 20(23), 2839-2860.
[24]
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., 1997, 23(1-3), 3-25.
[25]
Eddershaw, P.J.; Beresford, A.P.; Bayliss, M.K. ADME/PK as Part of a rational approach to drug discovery. Drug Discov. Today, 2000, 5(9), 409-414.
[26]
Clark, D.; Pickett, S. Computational methods for the prediction of “Drug-Likeness.”. Drug Discov. Today, 2000, 5(2), 49-58.
[27]
Garcia-Serna, R.; Vidal, D.; Remez, N.; Mestres, J. Large-scale predictive drug safety: From structural alerts to biological mechanisms. Chem. Res. Toxicol., 2015, 28(10), 1875-1887.
[28]
Bielska, E.; Lucas, X.; Czerwoniec, A.; Kasprzak, J.M.; Kaminska, K.H.; Bujnicki, J.M. Virtual screening strategies in drug design - methods and applications. Biotechnologia, 2011, 92(3), 249-264.
[29]
Pérez-Sianes, J.; Pérez-Sánchez, H.; Díaz, F. Virtual screening: A challenge for deep learning.In 10th International Conference on Practical Applications of Computational Biology Bioinformatics; Saberi M.M.; Rocha, M.P.; Fdez-Riverola, F.; Domínguez Mayo, F.J.; De Paz, J.F.; Eds.; Springer International Publishing: Cham, 2016; pp. 13-22.
[30]
Kitchen, D.B.; Decornez, H.; Furr, J.R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat. Rev. Drug Discov., 2004, 3(11), 935-949.
[31]
Kroemer, R.T. Structure-based drug design: Docking and scoring. Curr. Protein Pept. Sci., 2007, 8(4), 312-328.
[32]
Kubinyi, H. Succes Stories of Computer-Aided Design.In:Computer Applications in Pharmaceutical Research and Development; Ekins, S., Ed.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2006.
[33]
Talele, T.; Khedkar, S.; Rigby, A. Successful applications of computer aided drug discovery: Moving drugs from concept to the clinic. Curr. Top. Med. Chem., 2010, 10(1), 127-141.
[34]
Mohan, V.; Gibbs, A.C.; Cummings, M.D.; Jaeger, E.P.; DesJarlais, R.L. Docking: Successes and challenges. Curr. Pharm. Des., 2005, 11(3), 323-333.
[35]
Johnson, A.M.; Maggiora, G.M. Concepts and Applications of Molecular Similarity; John Wiley & Sons, Inc.: New York, 1990.
[36]
Halperin, I.; Wolfson, H.; Nussinov, R. Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins Struct. Funct. Genet, 2002, 47(4), 409-443.
[37]
Brooijmans, N.; Kuntz, I.D. Molecular recognition and docking algorithms. Annu. Rev. Biophys. Biomol. Struct., 2003, 32, 335-373.
[38]
Reddy, A.S.; Pati, S.P.; Kumar, P.P.; Pradeep, H.N.; Sastry, G.N. Virtual screening in drug discovery: A computational perspective. Curr. Protein Pept. Sci., 2007, 8(4), 329-351.
[39]
Renner, S.; Schneider, G. Fuzzy pharmacophore models from molecular alignments for correlation-vector-based virtual screening. J. Med. Chem., 2004, 47(19), 4653-4664.
[40]
Putta, S.; Lemmen, C.; Beroza, P.; Greene, J. A novel shape-feature based approach to virtual library screening. J. Chem. Inf. Comput. Sci., 2002, 42(5), 1230-1240.
[41]
Moffat, K.; Gillet, V.J.; Whittle, M.; Bravi, G.; Leach, A.R. A comparison of field-based similarity searching methods: CatShape, FBSS, and ROCS. J. Chem. Inf. Model., 2008, 48(4), 719-729.
[42]
Marialke, J.; Körner, R.; Tietze, S.; Apostolakis, A. Graph-based molecular alignment (GMA). J. Chem. Inf. Model., 2007, 47(2), 591-601.
[43]
Todeschini, R.; Consonni, V. Molecular Descriptors for Chemoinformatics, 2nd, Revis ed.; Wiley-VCH, 2009.
[44]
Wang, T.; Wu, M-B.; Lin, J-P.; Yang, L-R. Quantitative Structure–activity Relationship: Promising advances in drug discovery platforms. Expert Opin. Drug Discov., 2015, 10(12), 1283-1300.
[45]
Muegge, I.; Mukherjee, P. An overview of molecular fingerprint similarity search in virtual screening. Expert Opin. Drug Discov., 2016, 11(2), 137-148.
[46]
Zhang, Q.; Muegge, I. Scaffold hopping through virtual screening using 2D and 3D similarity descriptors: Ranking, voting, and consensus scoring. J. Med. Chem., 2006, 49(5), 1536-1548.
[47]
Venkatraman, V.; Pérez-Nueno, V.I.; Mavridis, L.; Ritchie, D.W. Comprehensive comparison of ligand-based virtual screening tools against the DUD data set reveals limitations of current 3D methods. J. Chem. Inf. Model., 2010, 50(12), 2079-2093.
[48]
Cruciani, G.; Pastor, M.; Mannhold, R. Suitability of molecular descriptors for database mining. a comparative analysis. J. Med. Chem., 2002, 45(13), 2685-2694.
[49]
Eckert, H.; Bajorath, J. Determination and mapping of activity-specific descriptor value ranges for the identification of active compounds. J. Med. Chem., 2006, 49(7), 2284-2293.
[50]
Gerlach, C.; Broughton, H.; Zaliani, A. FTree query construction for virtual screening: A statistical analysis. J. Comput. Aided Mol. Des., 2008, 22(2), 111-118.
[51]
Rönkkö, T.; Tervo, A.J.; Parkkinen, J.; Poso, A. BRUTUS: Optimization of a grid-based similarity function for rigid-body molecular superposition. II. description and characterization. J. Comput. Aided Mol. Des., 2006, 20(4), 227-236.
[52]
Rush, T.S.; Grant, J.A.; Mosyak, L.; Nicholls, A. A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction. J. Med. Chem., 2005, 48(5), 1489-1495.
[53]
Kim, K-H.; Kim, N.D.; Seong, B-L. Pharmacophore-based virtual screening: A review of recent applications. Expert Opin. Drug Discov., 2010, 5(3), 205-222.
[54]
Gund, P.; Wipke, W.T.; Langridge, R. Computer searching of a molecular structure file for pharmacophoric patterns.In Proceedings of the International Conference on Computers in Chemical Research and Education Elsevier: Amsterdam, 1974, Vol. 3, pp. 5-21.
[55]
Sanders, M.P.A.; Barbosa, A.J.M.; Zarzycka, B.; Nicolaes, G.A.F.; Klomp, J.P.G.; de Vlieg, J.; Del Rio, A. Comparative analysis of pharmacophore screening tools. J. Chem. Inf. Model., 2012, 52(6), 1607-1620.
[56]
Yang, S-Y. Pharmacophore modeling and applications in drug discovery: Challenges and recent advances. Drug Discov. Today, 2010, 15(11-12), 444-450.
[57]
Lorenzo, V.P.; Barbosa Filho, J.M.; Scotti, L.; Scotti, M.T. Combined structure-and ligand-based virtual screening to evaluate caulerpin analogs with potential inhibitory activity against monoamine oxidase B. Rev. Bras. Farmacogn., 2015, 25(6), 690-697.
[58]
Lorenzo, V.; Lúcio, A.; Scotti, L.; Tavares, J.; Filho, J.; Lima, T.; Rocha, J.; Scotti, M. Structure- and ligand-based approaches to evaluate aporphynic alkaloids from annonaceae as multi-target agent against leishmania donovani. Curr. Pharm. Des., 2016, 22(34), 5196-5203.
[59]
Wang, Y.; Li, R.; Zheng, Z.; Yi, H.; Li, Z. Identification of novel cathepsin k inhibitors using ligand-based virtual screening and structure-based docking. RSC Advances, 2016, 6(86), 82961-82968.
[60]
Drwal, M.N.; Griffith, R. Combination of ligand- and structure-based methods in virtual screening. Drug Discov. Today. Technol., 2013, 10(3), e395-e401.
[61]
Bissantz, C.; Schalon, C.; Guba, W.; Stahl, M. Focused library design in gpcr projects on the example of 5-ht(2c) agonists: Comparison of structure-based virtual screening with ligand-based search methods. Proteins, 2005, 61(4), 938-952.
[62]
Lazo, J.S.; Wipf, P. Combinatorial chemistry and contemporary pharmacology. J. Pharmacol. Exp. Ther., 2000, 293(3), 705-709.
[63]
Schneider, G.; Böhm, H.J. Virtual screening and fast automated docking methods. Drug Discov. Today, 2002, 7(1), 64-70.
[64]
Hou, T.; Xu, X. Recent development and application of virtual screening in drug discovery: An overview. Curr. Pharm. Des., 2004, 10(9), 1011-1033.
[65]
Murcko, M.A. Recent advances in ligand design methods.In Reviews in Computational Chemistry; Lipkowitz, K.B., Boyd, D. B., Eds.; Reviews in computational chemistry; John Wiley Sons, Inc.: Hoboken, NJ, USA, 1997; Vol. 11, pp. 1-66.
[66]
Brown, J.B.; Niijima, S.; Okuno, Y. Compound-Protein interaction prediction within chemogenomics: Theoretical concepts, practical usage, and future directions. Mol. Inform., 2013, 32(11-12), 906-921.
[67]
Klabunde, T. Chemogenomic approaches to drug discovery: Similar receptors bind similar ligands. Br. J. Pharmacol., 2007, 152(1), 5-7.
[68]
Konc, J.; Janežič, D. ProBiS algorithm for detection of structurally similar protein binding sites by local structural alignment. Bioinformatics, 2010, 26(9), 1160-1168.
[69]
Rognan, D. Chemogenomic approaches to rational drug design. Br. J. Pharmacol., 2007, 152(1), 38-52.
[70]
Frimurer, T.M.; Ulven, T.; Elling, C.E.; Gerlach, L-O.; Kostenis, E.; Högberg, T. A physicogenetic method to assign ligand-binding relationships between 7TM receptors. Bioorg. Med. Chem. Lett., 2005, 15(16), 3707-3712.
[71]
Bock, J.R.; Gough, D.A. Virtual screen for ligands of orphan g protein-coupled receptors. J. Chem. Inf. Model., 2005, 45(5), 1402-1414.
[72]
Huang, N.; Shoichet, B.K.; Irwin, J.J. Benchmarking sets for molecular docking. J. Med. Chem., 2006, 49(23), 6789-6801.
[73]
Kirchmair, J.; Distinto, S.; Markt, P.; Schuster, D.; Spitzer, G.M.; Liedl, K.R.; Wolber, G. How to optimize shape-based virtual screening: Choosing the right query and including chemical information. J. Chem. Inf. Model., 2009, 49(3), 678-692.
[74]
Nicholls, A. What do we know and when do we know it? J. Comput. Aided Mol. Des., 2008, 22(3-4), 239-255.
[75]
Fechner, N.; Jahn, A.; Hinselmann, G.; Zell, A. Estimation of the applicability domain of kernel-based machine learning models for virtual screening. J. Cheminform., 2010, 2, 1-21.
[76]
Michalski, R.S. A theory and methodology of inductive learning.In Machine Learning: An Artificial Intelligence Approach; Michalski, R.S.; Carbonell, J.G.; Mitchell, T.M., Eds.; Morgan-Kauffman, 1983, pp. 83-134.
[77]
Witten, I.H.; Frank, E.; Hall, M.A. Data Mining: Practical Machine Learning Tools and Techniques; Morgan Kaufmann Publishers Inc., 2011.
[78]
Terfloth, L.; Gasteiger, J. Neural networks and genetic algorithms in drug design. Drug Discov. Today, 2001, 6(01), 102-108.
[79]
Fukunishi, Y. Structure-based drug screening and ligand-based drug screening with machine learning.In Comb. Chem. High Throughput Screen., 2009, 12(4), 397-408.
[80]
Butkiewicz, M.; Mueller, R.; Selic, D.; Dawson, E.; Meiler, J. Application of machine learning approaches on quantitative structure activity relationships. 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology IEEE, 2009, pp. 255-262.
[81]
Melville, J.L.; Burke, E.K.; Hirst, J.D. Machine learning in virtual screening. Comb. Chem. High Throughput Screen., 2009, 12(4), 332-343.
[82]
Wale, N. Machine learning in drug discovery and development. Drug Dev. Res., 2011, 72(1), 112-119.
[83]
Hoskins, J.C.; Himmelblau, D.M. Artificial neural network models of knowledge representation in chemical engineering. Comput. Chem. Eng., 1988, 12(9-10), 881-890.
[84]
Winkler, D.A.; Burden, F.R. Bayesian neural nets for modeling in drug discovery. Drug Discov. Today, 2004, 2(3), 104-111.
[85]
Zheng, F.; Zheng, G.; Deaciuc, A.G.; Zhan, C-G.; Dwoskin, L.P.; Crooks, P.A. Computational neural network analysis of the affinity of lobeline and tetrabenazine analogs for the vesicular monoamine transporter-2. Bioorg. Med. Chem., 2007, 15(8), 2975-2992.
[86]
Qin, Y.; Deng, H.; Yan, H.; Zhong, R. An accurate nonlinear QSAR model for the antitumor activities of chloroethylnitrosoureas using neural networks. J. Mol. Graph. Model., 2011, 29(6), 826-833.
[87]
Durrant, J.D.; Friedman, A.J.; Rogers, K.E.; McCammon, J.A. Comparing neural-network scoring functions and the state of the art: Applications to common library screening. J. Chem. Inf. Model., 2013, 53(7), 1726-1735.
[88]
Betzi, S.; Suhre, K.; Chétrit, B.; Guerlesquin, F.; Morelli, X. GFscore: A general nonlinear consensus scoring function for high-throughput docking. J. Chem. Inf. Model., 2006, 46(4), 1704-1712.
[89]
Schneider, P.; Tanrikulu, Y.; Schneider, G. Self-Organizing maps in drug discovery: Compound library design, scaffold-hopping, repurposing. Curr. Med. Chem., 2009, 16(3), 258-266.
[90]
Vracko, M. Kohonen artificial neural network and counter propagation neural network in molecular structure-toxicity studies. Curr. Comput. Aided-Drug Des., 2005, 1(1), 73-78.
[91]
de Molfetta, F.A.; Angelotti, W.F.D.; Romero, R.A.F.; Montanari, C.A.; da Silva, A.B.F. A neural networks study of quinone compounds with trypanocidal activity. J. Mol. Model., 2008, 14(10), 975-985.
[92]
Schneider, P.; Müller, A.T.; Gabernet, G.; Button, A.L.; Posselt, G.; Wessler, S.; Hiss, J.A.; Schneider, G. Hybrid network model for “Deep Learning” of chemical data: Application to antimicrobial peptides. Mol. Inform., 2017, 36(1-2), 1600011.
[93]
Mlinsek, G.; Novic, M.; Hodoscek, M.; Solmajer, T. Prediction of enzyme binding: Human thrombin inhibition study by quantum chemical and artificial intelligence methods based on X-Ray structures. J. Chem. Inf. Comput. Sci., 2001, 41(5), 1286-1294.
[94]
Sabet, R.; Fassihi, A.; Hemmateenejad, B.; Saghaei, L.; Miri, R.; Gholami, M. Computer-aided design of novel antibacterial 3-hydroxypyridine-4-ones: Application of QSAR methods based on the MOLMAP approach. J. Comput. Aided Mol. Des., 2012, 26(3), 349-361.
[95]
Heikamp, K.; Bajorath, J. Support vector machines for drug discovery. Expert Opin. Drug Discov., 2014, 9(1), 93-104.
[96]
Sun, H.; Veith, H.; Xia, M.; Austin, C.P.; Huang, R. Predictive models for cytochrome p450 isozymes based on quantitative high throughput screening data. J. Chem. Inf. Model., 2011, 51(10), 2474-2481.
[97]
Heikamp, K.; Bajorath, J. Prediction of compounds with closely related activity profiles using weighted support vector machine linear combinations. J. Chem. Inf. Model., 2013, 53(4), 791-801.
[98]
Kinnings, S.L.; Liu, N.; Tonge, P.J.; Jackson, R.M.; Xie, L.; Bourne, P.E. A machine learning-based method to improve docking scoring functions and its application to drug repurposing. J. Chem. Inf. Model., 2011, 51(2), 408-419.
[99]
Yamazaki, K.; Kusunose, N.; Fujita, K.; Sato, H.; Asano, S.; Dan, A.; Kanaoka, M. Identification of phosphodiesterase-1 and 5 dual inhibitors by a ligand-based virtual screening optimized for lead evolution. Bioorg. Med. Chem. Lett., 2006, 16(5), 1371-1379.
[100]
Scotti, M.; Speck-Planche, A.; Tavares, J.; da Silva, M.D.S.; Cordeiro, M.; Scotti, L. Virtual screening of alkaloids from apocynaceae with potential antitrypanosomal activity. Curr. Bioinform., 2015, 10(5), 509-519.
[101]
Deconinck, E.; Zhang, M.H.; Coomans, D.; Vander Heyden, Y. Classification tree models for the prediction of blood-brain barrier passage of drugs. J. Chem. Inf. Model., 2006, 46(3), 1410-1419.
[102]
Schneider, N.; Jäckels, C.; Andres, C.; Hutter, M.C. Gradual in silico filtering for druglike substances. J. Chem. Inf. Model., 2008, 48(3), 613-628.
[103]
Lei, T.; Li, Y.; Song, Y.; Li, D.; Sun, H.; Hou, T. ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling. J. Cheminform., 2016, 8(1), 6.
[104]
Cano, G.; Garcia-Rodriguez, J.; Garcia-Garcia, A.; Perez-Sanchez, H.; Benediktsson, J.A.; Thapa, A.; Barr, A. Automatic selection of molecular descriptors using random forest: Application to drug discovery. Expert Syst. Appl., 2017, 72, 151-159.
[105]
Li, B-K.; He, B.; Tian, Z-Y.; Chen, Y-Z.; Xue, Y. Modeling, predicting and virtual screening of selective inhibitors of MMP-3 and MMP-9 over MMP-1 using random forest classification. Chemom. Intell. Lab. Syst., 2015, 147, 30-40.
[106]
Jensen, B.F.; Vind, C.; Brockhoff, P.B.; Refsgaard, H.H.F. In silico prediction of cytochrome p450 2d6 and 3a4 inhibition using gaussian kernel weighted k -nearest neighbor and extended connectivity fingerprints, including structural fragment analysis of inhibitors versus noninhibitors. J. Med. Chem., 2007, 50(3), 501-511.
[107]
Cao, G.P.; Arooj, M.; Thangapandian, S.; Park, C.; Arulalapperumal, V.; Kim, Y.; Kwon, Y.J.; Kim, H.H.; Suh, J.K.; Lee, K.W. A Lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors. SAR QSAR Environ. Res., 2015, 26(5), 397-420.
[108]
Helma, C. Lazy Structure-activity relationships (Lazar) for the prediction of rodent carcinogenicity and salmonella mutagenicity. Mol. Divers., 2006, 10(2), 147-158.
[109]
Domingos, P.; Pazzani, M. On the optimality of the simple bayesian classifier under zero-one loss. Mach. Learn., 1997, 29(2-3), 103-130.
[110]
Klon, A.E.; Glick, M.; Davies, J.W. Application of machine learning to improve the results of high-throughput docking against the HIV-1 protease. J. Chem. Inf. Comput. Sci., 2004, 44(6), 2216-2224.
[111]
Glick, M.; Jenkins, J.L.; Nettles, J.H.; Hitchings, H.; Davies, J.W. Enrichment of high-throughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and laplacian-modified naive bayesian classifiers. J. Chem. Inf. Model., 2006, 46(1), 193-200.
[112]
Lang, P.T.; Kuntz, I.D.; Maggiora, G.M.; Bajorath, J. Evaluating the high-throughput screening computations. J. Biomol. Screen., 2005, 10(7), 649-652.
[113]
Soulère, L.; Soulage, C.O. Exploring docking methods for virtual screening: application to the identification of neuraminidase and ftsz potential inhibitors. Mol. Simul., 2017, 43(8), 656-663.
[114]
Bera, I.; Marathe, M.V.; Payghan, P.V.; Ghoshal, N. Identification of novel hits as highly prospective dual agonists for mu and kappa opioid receptors: An integrated in silico approach. J. Biomol. Struct. Dyn., 2017, 2, 1-23.
[115]
Barrett, S.; Langdon, W. Advances in the application of machine learning techniques in drug discovery, design and development. Appl. Soft Comput., 2006, 13, 346.
[116]
Liu, P.; Long, W. Current mathematical methods used in QSAR/QSPR Studies. Int. J. Mol. Sci., 2009, 10(5), 1978-1998.
[117]
Plewczynski, D.; Spieser, S.A.H.; Koch, U. Assessing different classification methods for virtual screening. J. Chem. Inf. Model., 2006, 46(3), 1098-1106.
[118]
Plewczynski, D.; Spieser, S.A.H.; Koch, U. Performance of machine learning methods for ligand-based virtual screening. Comb. Chem. High Throughput Screen., 2009, 12(4), 358-368.
[119]
Ma, X.H.; Jia, J.; Zhu, F.; Xue, Y.; Li, Z.R.; Chen, Y.Z. Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries. Comb. Chem. High Throughput Screen., 2009, 12(4), 344-357.
[120]
Vyas, R.; Bapat, S.; Jain, E.; Tambe, S.S.; Karthikeyan, M.; Kulkarni, B.D. A study of applications of machine learning based classification methods for virtual screening of lead molecules. Comb. Chem. High Throughput Screen., 2015, 18(7), 658-672.
[121]
Svetnik, V.; Liaw, A.; Tong, C.; Wang, T. Application of breiman’s random forest to modeling structure-activity relationships of pharmaceutical molecules.In multiple classifier systems: 5th international workshop proceedings; Roli, F., Kittler, J., Windeatt, T., Eds.; Lecture Notes in Computer Science; Springer, Berlin, Heidelberg, 2004; Vol. 3077, pp 334-343.
[122]
Jorissen, R.N.; Gilson, M.K. Virtual screening of molecular databases using a support vector machine. J. Chem. Inf. Model., 2005, 45(3), 549-561.
[123]
Li, Y.; Wang, Y.; Ding, J.; Wang, Y.; Chang, Y.; Zhang, S. In silico prediction of androgenic and nonandrogenic compounds using random forest. QSAR Comb. Sci., 2009, 28(4), 396-405.
[124]
Ma, X.H.; Wang, R.; Yang, S.Y.; Li, Z.R.; Xue, Y.; Wei, Y.C.; Low, B.C.; Chen, Y.Z. Evaluation of virtual screening performance of support vector machines trained by sparsely distributed active compounds. J. Chem. Inf. Model., 2008, 48(6), 1227-1237.
[125]
Ballester, P.J.; Mitchell, J.B.O. A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking. Bioinformatics, 2010, 26(9), 1169-1175.
[126]
Liu, Z.; Su, M.; Han, L.; Liu, J.; Yang, Q.; Li, Y.; Wang, R. Forging the basis for developing protein-ligand interaction scoring functions. Acc. Chem. Res., 2017, 50(2), 302-309.
[127]
Durrant, J.D.; McCammon, J.A. NNScore 2.0: A neural-network receptor-ligand scoring function. J. Chem. Inf. Model., 2011, 51(11), 2897-2903.
[128]
Ouyang, X.; Handoko, S.D.; Kwoh, C.K.C. Score: A simple yet effective scoring function for protein-ligand binding affinity prediction using modified CMAC learning architecture. J. Bioinform. Comput. Biol., 2011, 9(Suppl. 1), 1-14.
[129]
Zilian, D.; Sotriffer, C.A. SFCscore RF : A random forest-based scoring function for improved affinity prediction of protein-ligand complexes. J. Chem. Inf. Model., 2013, 53(8), 1923-1933.
[130]
Li, G-B.; Yang, L-L.; Wang, W-J.; Li, L-L.; Yang, S-Y. ID-Score: A new empirical scoring function based on a comprehensive set of descriptors related to protein-ligand interactions. J. Chem. Inf. Model., 2013, 53(3), 592-600.
[131]
Ashtawy, H.M.; Mahapatra, N.R. A comparative assessment of predictive accuracies of conventional and machine learning scoring functions for protein-ligand binding affinity prediction. IEEE/ACM Trans. Comput. Biol. Bioinforma., 2015, 12(2), 335-347.
[132]
Ain, Q.U.; Aleksandrova, A.; Roessler, F.D.; Ballester, P.J. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2015, 5(6), 405-424.
[133]
Geppert, H.; Vogt, M.; Bajorath, J. Current trends in ligand-based virtual screening: Molecular representations, data mining methods, new application areas, and performance evaluation. J. Chem. Inf. Model., 2010, 50(2), 205-216.
[134]
Jain, A.N.; Nicholls, A. Recommendations for evaluation of computational methods. J. Comput. Aided Mol. Des., 2008, 22(3-4), 133-139.
[135]
Mysinger, M.M.; Carchia, M.; Irwin, J.J.; Shoichet, B.K. Directory of useful decoys, enhanced (dud-e): Better ligands and decoys for better benchmarking. J. Med. Chem., 2012, 55(14), 6582-6594.
[136]
Bauer, M.R.; Ibrahim, T.M.; Vogel, S.M.; Boeckler, F.M. Evaluation and optimization of virtual screening workflows with dekois 2.0 – A public library of challenging docking benchmark sets. J. Chem. Inf. Model., 2013, 53(6), 1447-1462.
[137]
Jahn, A.; Hinselmann, G.; Fechner, N.; Zell, A. Optimal assignment methods for ligand-based virtual screening. J. Cheminform., 2009, 1(1), 14.
[138]
Rohrer, S.G.; Baumann, K. Maximum Unbiased Validation (MUV) data sets for virtual screening based on pubchem bioactivity data. J. Chem. Inf. Model., 2009, 49(2), 169-184.
[139]
Kurczab, R.; Smusz, S.; Bojarski, A.J.; Melville, J.; Burke, E.; Hirst, J.; Ma, X.; Wang, R.; Yang, S.; Li, Z.; Xue, Y.; Wei, Y.; Low, B.; Chen, Y.; Plewczynski, D.; Spieser, S.; Koch, U.; Bruce, C.; Melville, J.; Pickett, S.; Hirst, J.; Smusz, S.; Kurczab, R.; Bojarski, A.; Smusz, S.; Kurczab, R.; Bojarski, A.; Irwin, J.; Sterling, T.; Mysinger, M.; Bolstad, E.; Coleman, R.; Huang, N.; Shoichet, B.; Irwin, J.; Heikamp, K.; Bajorath, J.; Wang, Y.; Xiao, J.; Suzek, T.; Zhang, J.; Wang, J.; Zhou, Z.; Han, L.; Karapetyan, K.; Dracheva, S.; Shoemaker, B.; Bolton, E.; Gindulyte, A.; Bryant, S.; Gaulton, A.; Bellis, L.; Bento, A.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; Mcglinchey, S.; Michalovich, D.; Al-Lazikani, B.; Overington, J.; Davis, J.; Goadrich, M.; Chen, B.; Harrison, R.; Papadatos, G.; Willett, P.; Wood, D.; Lewell, X.; Greenidge, P.; Stiefl, N.; Ma, X.; Jia, J.; Zhu, F.; Xue, Y.; Li, Z.; Chen, Y.; Cannon, E.; Amini, A.; Bender, A.; Sternberg, M.; Muggleton, S.; Glen, R.; Mitchell, J.; Mitchell, T.; Aha, D.; Kibler, D.; Albert, M.; Brighton, H.; Mellish, C.; Quinlan, J.; Svetnik, V.; Liaw, A.; Tong, C.; Culberson, J.; Sheridan, R.; Feuston, B.; Breiman, L.; Steinbeck, C.; Han, Y.; Kuhn, S.; Horlacher, O.; Luttmann, E.; Willighagen, E.; Yap, C. The influence of negative training set size on machine learning-based virtual screening. J. Cheminform., 2014, 6(1), 32.
[140]
Xia, J.; Tilahun, E.L.; Reid, T-E.; Zhang, L.; Wang, X.S. Benchmarking methods and data sets for ligand enrichment assessment in virtual screening. Methods, 2015, 71, 146-157.
[141]
Réau, M.; Langenfeld, F.; Zagury, J-F.; Lagarde, N.; Montes, M. Decoys selection in benchmarking datasets: Overview and perspectives. Front. Pharmacol., 2018, 9, 11.
[142]
Gonczarek, A.; Tomczak, J.M.; Zaręba, S.; Kaczmar, J.; Dąbrowski, P.; Walczak, M.J. Learning deep architectures for interaction
prediction in structure-based virtual screening.
arXiv:1610.07187[stat.ML]2016 https://arxiv.org/abs/1610.07187/
[143]
Hu, Y.; Stumpfe, D.; Bajorath, J. Recent advances in scaffold hopping. J. Med. Chem., 2017, 60(4), 1238-1246.
[144]
Marchese Robinson, R.L.; Palczewska, A.; Palczewski, J.; Kidley, N. Comparison of the predictive performance and interpretability of random forest and linear models on benchmark data sets. J. Chem. Inf. Model., 2017, 57(8), 1773-1792.
[145]
Stumpfe, D.; Bajorath, J. Similarity searching. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2011, 1(2), 260-282.
[146]
Wallach, I.; Heifets, A. Most ligand-based classification benchmarks reward memorization rather than generalization. J. Chem. Inf. Model., 2018, 58(5), 916-932.
[147]
Markoff, J. Scientists see promise in deep-learning programs, http://www.nytimes.com/2012/11/24/science/scientists-see-advances-in-deep-learning-a-part-of-artificial-intelligence.html (Accessed Oct 7, 2018).
[148]
Hof, R.D. 10 Breakthrough Technologies 2013 - Deep Learning, http://www.technologyreview.com/featuredstory/513696/deep-learning/ (Accessed Oct 7, 2018).
[151]
Stanford University. Unsupervised feature learning and deep learning tutorial, http://deeplearning.stanford.edu/tutorial/ (Accessed Oct 7, 2018).
[152]
Deep learning and representation learning workshop: NIPS 2014 http://www.dlworkshop.org/ (Accessed Oct
7, 2018).
[153]
Bengio, Y.; Memisevic, R.; LeCun, Y. Deep learning summer
school. 2015 https://sites.google.com/site/deeplearningsummerschool/ (Accessed
Oct 7, 2018).
[154]
Deep learning workshop, ICML 15 https://sites.google.com/site/deeplearning2015/ (Accessed Oct 7,
2018).
[155]
Bergstra, J.; Breuleux, O.; Bastien, F.; Lamblin, P.; Pascanu, R.; Desjardins, G.; Turian, J.; Warde-Farley, D.; Bengio, Y. Theano: A CPU and GPU math expression compiler. Proceedings of the Python for Scientific Computing Conference (SciPy), 2010.
[156]
Abadi, M.; Agarwal, A.; Barham, P.; Brevdo, E.; Chen, Z.; Citro, C.; Corrado, G.S.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Goodfellow, I.; Harp, A.; Irving, G.; Isard, M.; Jia, Y.; Jozefowicz, R.; Kaiser, L.; Kudlur, M.; Levenberg, J.; Mane, D.; Monga, R.; Moore, S.; Murray, D.; Olah, C.; Schuster, M.; Shlens, J.; Steiner, B.; Sutskever, I.; Talwar, K.; Tucker, P.; Vanhoucke, V.; Vasudevan, V.; Viegas, F.; Vinyals, O.; Warden, P.; Wattenberg, M.; Wicke, M.; Yu, Y.; Zheng, X. TensorFlow: Large-scale machine
learning on heterogeneous distributed systems arXiv:1603.04467v2[cs.DC]2016. https://arxiv.org/abs/1603.04467v2/
[157]
Deeplearning4j - Open-source, distributed deep learning for the
JVM http://deeplearning4j.org/ (Accessed Oct 7, 2018).
[158]
NVIDIA - Deep Learning Software https://developer.nvidia.com/deep-learning-software (Accessed Oct
7, 2018).
[159]
Bengio, Y.; Courville, A.; Vincent, P. Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35(8), 1798-1828.
[161]
Fukushima, K. Neocognitron: A self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern., 1980, 36(4), 193-202.
[162]
Bengio, Y.; Lamblin, P.; Popovici, D.; Larochelle, H. Greedy layer-wise training of deep networks.InAdvances in Neural Information Processing Systems 19; Schölkopf, B.; Platt, J.C.; Hoffman, T., Eds.; MIT Press, 2007, pp. 153-160.
[163]
Bengio, Y.; LeCun, Y. Scaling learning algorithms toward AI.In Large Scale Kernel Machines; Bottou, L., Chapelle, O., DeCoste, D., Weston, J., Eds.; MIT Press, 2007; pp. 321-359.
[164]
Hinton, G.E.; Osindero, S.; Teh, Y-W. A fast learning algorithm for deep belief nets. Neural Comput., 2006, 18(7), 1527-1554.
[165]
Vincent, P.; Larochelle, H.; Bengio, Y.; Manzagol, P-A. Extracting and composing robust features with denoising autoencoders.In Proceedings of the 25th international conference on Machine learning - ICML ’08; ACM Press: New York, New York, USA, 2008; pp 1096-1103
[166]
Poultney, C.; Chopra, S.; Lecun, Y. Efficient learning of sparse representations with an energy-based model. In Advances in Neural Information Processing Systems (NIPS 2006); MIT Press, 2006, pp. 1137-1144.
[167]
Dahl, G.E.; Yu, D.; Deng, L.; Acero, A. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. Audio, Speech. Lang. Process. IEEE Trans., 2012, 20(1), 30-42.
[168]
Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet classification with deep convolutional neural networks.In Advances in Neural Information Processing Systems 25; Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K. Q., Eds.; Curran Associates, Inc., 2012; pp 1097-1105
[170]
Glorot, X.; Bordes, A.; Bengio, Y. Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2011) Gordon, G., Dunson, D., Dudík, M., Eds.; Proceedings of Machine Learning Research; PMLR: Fort Lauderdale, FL, USA,, 2011, Vol. 15, pp. 315-323.
[171]
Deng, L.; Yu, D. Deep learning: Methods and applications. Found.
Trends Signal Process. 2014, 7 (3-4), 197-387.
[172]
Kaggle - Merck Molecular Activity Challenge
https://www.kaggle.com/c/MerckActivity (Accessed Oct 7, 2018).
[173]
Dahl, G.E.; Jaitly, N.; Salakhutdinov, R. Multi-Task neural networks
for QSAR predictions. arXiv:1406.1231 [stat.ML] 2014.
https://arxiv.org/abs/1406.1231/
[174]
Ma, J.; Sheridan, R.P.; Liaw, A.; Dahl, G.E.; Svetnik, V. Deep
neural nets as a method for quantitative structure-activity relationships.
J. Chem. Inf. Model., 2015, 55(2), 263-274.
[175]
Ghasemi, F.; Fassihi, A.; Pérez-Sánchez, H.; Mehri Dehnavi, A. The role of different sampling methods in improving biological activity prediction using deep belief network. J. Comput. Chem., 2017, 38(4), 195-203.
[176]
Ghasemi, F.; Mehridehnavi, A.; Fassihi, A.; Pérez-Sánchez, H. Deep neural network in QSAR studies using deep belief network. Appl. Soft Comput., 2018, 62, 251-258.
[177]
Ramsundar, B.; Kearnes, S.; Riley, P.; Webster, D.; Konerding, D.; Pande, V. Massively multitask networks for drug discovery.
arXiv:1502.02072 [stat.ML] 2015. https://arxiv.org/abs/1502.02072/
[178]
Kearnes, S.; Goldman, B.; Pande, V. Modeling industrial ADMET
data with multitask networks. arXiv:1606.08793 [stat.ML] 2016 https://arxiv.org/abs/1606.08793/
[179]
Xu, Y.; Ma, J.; Liaw, A.; Sheridan, R.P.; Svetnik, V. Demystifying multi-task deep neural networks for quantitative structure-activity relationships. J. Chem. Inf. Model., 2017, 57(10), 2490-2504.
[180]
Mayr, A.; Klambauer, G.; Unterthiner, T.; Hochreiter, S. DeepTox: Toxicity prediction using deep learning. Front. Environ. Sci., 2016, 3, 80.
[181]
Lenselink, E.B.; Ten Dijke, N.; Bongers, B.; Papadatos, G.; Van Vlijmen, H.W.T.; Kowalczyk, W.; Ijzerman, A.P.; Van Westen, G.J.P. Beyond the hype: Deep neural networks outperform established methods using a chembl bioactivity benchmark set. J. Cheminform., 2017, 9(1), 45.
[182]
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(D1), D1100-D1107.
[183]
Aliper, A.; Plis, S.; Artemov, A.; Ulloa, A.; Mamoshina, P.; Zhavoronkov, A. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol. Pharm., 2016, 13(7), 2524-2530.
[184]
Koutsoukas, A.; Monaghan, K.J.; Li, X.; Huan, J. Deep-learning: Investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data. J. Cheminform., 2017, 9(1), 42.
[185]
Ramsundar, B.; Liu, B.; Wu, Z.; Verras, A.; Tudor, M.; Sheridan, R.P.; Pande, V. Is multitask deep learning practical for pharma? J. Chem. Inf. Model., 2017, 57(8), 2068-2076.
[186]
Jing, Y.; Bian, Y.; Hu, Z.; Wang, L.; Xie, X-Q.S. Deep learning for drug design: An artificial intelligence paradigm for drug discovery in the big data era. AAPS J., 2018, 20(3), 58.
[187]
Lusci, A.; Pollastri, G.; Baldi, P. Deep architectures and deep learning in chemoinformatics: The prediction of aqueous solubility for drug-like molecules. J. Chem. Inf. Model., 2013, 53(7), 1563-1575.
[188]
Xu, Y.; Dai, Z.; Chen, F.; Gao, S.; Pei, J.; Lai, L. Deep learning for drug-induced liver injury. J. Chem. Inf. Model., 2015, 55(10), 2085-2093.
[189]
Liu, W.; Wang, Z.; Liu, X.; Zeng, N.; Liu, Y.; Alsaadi, F.E. A Survey of deep neural network architectures and their applications. Neurocomputing, 2017, 234, 11-26.
[190]
Hughes, T.B.; Miller, G.P.; Swamidass, S.J. Modeling epoxidation of drug-like molecules with a deep machine learning network. ACS Cent. Sci., 2015, 1(4), 168-180.
[191]
Hughes, T.B.; Le Dang, N.; Miller, G.P.; Swamidass, S.J. Modeling reactivity to biological macromolecules with a deep multitask network. ACS Cent. Sci., 2016, 2(8), 529-537.
[192]
Duvenaud, D.; Maclaurin, D.; Iparraguirre, J.; Bombarell, R.;
Hirzel, T.; Aspuru-Guzik, A.; Adams, R.P. Convolutional networks
on graphs for learning molecular fingerprints. In Proceedings of the
28th International Conference on Neural Information Processing
Systems; MIT Press, 2015; pp 2224-2232.
[193]
Kearnes, S.; McCloskey, K.; Berndl, M.; Pande, V.; Riley, P. Molecular graph convolutions: Moving beyond fingerprints. J. Comput. Aided Mol. Des., 2016, 30(8), 595-608.
[194]
Coley, C.W.; Barzilay, R.; Green, W.H.; Jaakkola, T.S.; Jensen, K.F. Convolutional embedding of attributed molecular graphs for Physical Property Prediction. J. Chem. Inf. Model., 2017, 57(8), 1757-1772.
[195]
Xu, Y.; Pei, J.; Lai, L. Deep learning based regression and multiclass models for acute oral toxicity prediction with automatic chemical feature extraction. J. Chem. Inf. Model., 2017, 57(11), 2672-2685.
[196]
Xu, Y.; Pei, J.; Lai, L. Deep learning based regression and multiclass models for acute oral toxicity prediction with automatic chemical feature extraction. J. Chem. Inf. Model., 2017, 57(11), 2672-2685.
[197]
Wallach, I.; Dzamba, M.; Heifets, A. AtomNet: A deep convolutional
neural network for bioactivity prediction in structure-based
drug discovery. arXiv:1510.02855 [cs.LG] 2015 https://arxiv.org/abs/1510.02855/
[198]
Ragoza, M.; Hochuli, J.; Idrobo, E.; Sunseri, J.; Koes, D.R. Protein-ligand scoring with convolutional neural networks. J. Chem. Inf. Model., 2017, 57(4), 942-957.
[199]
Ragoza, M.; Turner, L.; Koes, D.R. Ligand pose optimization with
atomic grid-based convolutional neural networks.arXiv:1710.07400 [stat.ML] 2017. https://arxiv.org/abs/1710.07400
[200]
Jiménez, J.; Škalič, M.; Martínez-Rosell, G.; De Fabritiis, G. KDEEP: Protein-ligand absolute binding affinity prediction via 3d-convolutional neural networks. J. Chem. Inf. Model., 2018, 58(2), 287-296.
[201]
Stepniewska-Dziubinska, M.M.; Zielenkiewicz, P.; Siedlecki, P. Development and evaluation of a deep learning model for protein-ligand binding affinity prediction. Bioinformatics, 2018, 34(21), 3666-3674.
[202]
Gomes, J.; Ramsundar, B.; Feinberg, E.N.; Pande, V.S. Atomic
convolutional networks for predicting protein-ligand binding affinity.
arXiv:1703.10603 [cs.LG] 2017 https://arxiv.org/abs/1703.10603
[203]
Feinberg, E.N.; Sur, D.; Husic, B.E.; Mai, D.; Li, Y.; Yang, J.; Ramsundar, B.; Pande, V.S. Spatial graph convolutions for drug
discovery. arXiv:1803.04465 [cs.LG] 2018 https://arxiv.org/abs/1803.04465
[204]
Cang, Z.; Wei, G-W. TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions. PLOS Comput. Biol., 2017, 13(7), e1005690.
[205]
Cang, Z.; Mu, L.; Wei, G-W.; Yin, C.; He, R.; Yau, S. Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening. PLOS Comput. Biol., 2018, 14(1), e1005929.
[206]
Pereira, J.C.; Caffarena, E.R.; dos Santos, C.N. Boosting docking-based virtual screening with deep learning. J. Chem. Inf. Model., 2016, 56(12), 2495-2506.
[207]
Wan, F.; Zeng, J. Deep learning with feature embedding for compound-protein interaction prediction. bioRxiv, 086033 2016. doi:
[http://dx.doi.org/10.1101/086033]
[http://dx.doi.org/10.1101/086033]
[208]
Tsubaki, M.; Tomii, K.; Sese, J.; Wren, J. Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. Bioinformatics, 2018.
[http://dx.doi.org/10.1093/bioinformatics/bty535]
[http://dx.doi.org/10.1093/bioinformatics/bty535]
[209]
Goh, G.B.; Siegel, C.; Vishnu, A.; Hodas, N.O.; Baker, N. Chemception:
A deep neural network with minimal chemistry knowledge
matches the performance of expert-developed QSAR/QSPR models.
2017 arXiv:1706.06689 >
[210]
Goh, G.B.; Siegel, C.; Vishnu, A.; Hodas, N.O.; Baker, N. How
Much Chemistry Does a Deep Neural Network Need to Know to
Make Accurate Predictions? arXiv:1710.02238v2 [stat.ML] 2017 https://arxiv.org/abs/1710.02238v2
[211]
Jastrzębski, S.; Leśniak, D.; Czarnecki, W.M. Learning to
SMILE(S). arXiv:1602.06289v2 [cs.CL] 2016 https://arxiv.org/abs/1602.06289v2
[212]
Bjerrum, E.J. SMILES Enumeration as Data Augmentation for
Neural Network Modeling of Molecules. arXiv:1703.07076v2
[cs.LG] 2017 https://arxiv.org/abs/1703.07076v2
[213]
Jørgensen, P.B.; Schmidt, M.N.; Winther, O. Deep generative models for molecular science. Mol. Inform., 2018, 37(1-2), 1700133.
[214]
Chen, H.; Engkvist, O.; Wang, Y.; Olivecrona, M.; Blaschke, T. The Rise of Deep Learning in Drug Discovery. Drug Discov. Today, 2018, 23(6), 1241-1250.
[215]
Ching, T.; Himmelstein, D.S.; Beaulieu-Jones, B.K.; Kalinin, A.A.; Do, B.T.; Way, G.P.; Ferrero, E.; Agapow, P-M.; Zietz, M.; Hoffman, M.M.; Xie, W.; Rosen, G.L.; Lengerich, B.J.; Israeli, J.; Lanchantin, J.; Woloszynek, S.; Carpenter, A.E.; Shrikumar, A.; Xu, J.; Cofer, E.M.; Lavender, C.A.; Turaga, S.C.; Alexandari, A.M.; Lu, Z.; Harris, D.J.; DeCaprio, D.; Qi, Y.; Kundaje, A.; Peng, Y.; Wiley, L.K.; Segler, M.H.S.; Boca, S.M.; Swamidass, S.J.; Huang, A.; Gitter, A.; Greene, C.S. Opportunities and Obstacles for deep learning in biology and medicine. J. R. Soc. Interface, 2018, 15(141), 20170387.
[216]
Kadurin, A.; Aliper, A.; Kazennov, A.; Mamoshina, P.; Vanhaelen, Q.; Khrabrov, K.; Zhavoronkov, A.; Kadurin, A.; Aliper, A.; Kazennov, A.; Mamoshina, P.; Vanhaelen, Q.; Khrabrov, K.; Zhavoronkov, A.; Kadurin, A.; Aliper, A.; Kazennov, A.; Mamoshina, P.; Vanhaelen, Q.; Khrabrov, K.; Zhavoronkov, A. The cornucopia of meaningful leads: applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget, 2017, 8(7), 10883-10890.
[217]
Kim, S.; Thiessen, P.A.; Bolton, E.E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B.A.; Wang, J.; Yu, B.; Zhang, J.; Bryant, S.H. PubChem substance and compound databases. Nucleic Acids Res., 2016, 44(D1), D1202-D1213.
[218]
Kaneko, T. Generative adversarial networks: Foundations and applications. Acoust. Sci. Technol., 2018, 39(3), 189-197.
[219]
Kadurin, A.; Nikolenko, S.; Khrabrov, K.; Aliper, A.; Zhavoronkov, A. DruGAN: An advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico. Mol. Pharm., 2017, 14(9), 3098-3104.
[220]
Graves, A. Generating Sequences With Recurrent Neural
Networks. arXiv:1308.0850v5 [cs.NE] 2013.
https://arxiv.org/abs/1308.0850v5
[221]
Bowman, S.R.; Vilnis, L.; Vinyals, O.; Dai, A.M.; Jozefowicz, R.;
Bengio, S. Generating Sentences from a Continuous Space.
arXiv:1511.06349v4 [cs.LG] 2015.
https://arxiv.org/abs/1511.06349v4
[222]
Xie, Z. Neural Text Generation: A Practical Guide.
arXiv:1711.09534 [cs.CL] 2017. https://arxiv.org/abs/1711.09534
[223]
Gómez-Bombarelli, R.; Wei, J.N.; Duvenaud, D.; Hernández-Lobato, J.M.; Sánchez-Lengeling, B.; Sheberla, D.; Aguilera-Iparraguirre, J.; Hirzel, T.D.; Adams, R.P.; Aspuru-Guzik, A. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci., 2018, 4(2), 268-276.
[224]
Blaschke, T.; Olivecrona, M.; Engkvist, O.; Bajorath, J.; Chen, H. Application of generative autoencoder in de novo molecular design. Mol. Inform., 2018, 37(1-2), 1700123.
[225]
Kusner, M.J.; Paige, B.; Hernández-Lobato, J.M. Grammar
Variational Autoencoder. arXiv:1703.01925 [stat.ML] 2017. https://arxiv.org/abs/1703.01925
[226]
Polykovskiy, D.; Zhebrak, A.; Vetrov, D.; Ivanenkov, Y.; Aladinskiy, V.; Mamoshina, P.; Bozdaganyan, M.; Aliper, A.; Zhavoronkov, A.; Kadurin, A. Entangled conditional adversarial autoencoder for de novo drug discovery. Mol. Pharm., 2018, 15(10), 4398-4405.
[227]
De Boom, C.; Demeester, T.; Dhoedt, B. Character-Level
Recurrent Neural Networks in Practice: Comparing Training and
Sampling Schemes. arXiv:1801.00632v2 [cs.LG] 2018. https://arxiv.org/abs/1801.00632v2
[228]
Yuan, W.; Jiang, D.; Nambiar, D.K.; Liew, L.P.; Hay, M.P.; Bloomstein, J.; Lu, P.; Turner, B.; Le, Q-T.; Tibshirani, R.; Khatri, P.; Moloney, M.G.; Koong, A.C. Chemical space mimicry for drug discovery. J. Chem. Inf. Model., 2017, 57(4), 875-882.
[229]
Segler, M.H.S.; Kogej, T.; Tyrchan, C.; Waller, M.P. Generating focussed molecule libraries for drug discovery with recurrent neural networks. ACS Cent. Sci., 2018, 4(1), 120-131.
[230]
Gupta, A.; Müller, A.T.; Huisman, B.J.H.; Fuchs, J.A.; Schneider, P.; Schneider, G. Generative recurrent networks for de novo drug design. Mol. Inform., 2018, 37(1-2), 1700111.
[231]
Merk, D.; Friedrich, L.; Grisoni, F.; Schneider, G. De Novo design of bioactive small molecules by artificial intelligence. Mol. Inform., 2018, 37(1-2), 1700153.
[232]
Jaques, N.; Gu, S.; Bahdanau, D.; Hernández-Lobato, J.M.; Turner, R.E.; Eck, D. Sequence Tutor: Conservative fine-tuning of sequence
generation models with KL-control. arXiv:1611.02796v9
[cs.LG] 2016 https://arxiv.org/abs/1611.02796v9
[233]
Popova, M.; Isayev, O.; Tropsha, A. Deep reinforcement learning
for de-novo drug design. arXiv:1711.10907v2 [cs.AI] 2017. https://arxiv.org/abs/1711.10907v2
[234]
Olivecrona, M.; Blaschke, T.; Engkvist, O.; Chen, H. Molecular de-novo design through deep reinforcement learning. J. Cheminform., 2017, 9(1), 48.
[235]
Guimaraes, G.L.; Sanchez-Lengeling, B.; Outeiral, C.; Farias, P.L.C.; Aspuru-Guzik, A. Objective-Reinforced Generative Adversarial
Networks (ORGAN) for sequence generation models.
arXiv:1705.10843v3 [stat.ML] 2017. https://arxiv.org/abs/1705.10843v3
[236]
Putin, E.; Asadulaev, A.; Vanhaelen, Q.; Ivanenkov, Y.; Aladinskaya, A.V.; Aliper, A.; Zhavoronkov, A. Adversarial threshold neural computer for molecular de novo design. Mol. Pharm., 2018, 15(10), 4386-4397.
[237]
Putin, E.; Asadulaev, A.; Ivanenkov, Y.; Aladinskiy, V.; Sanchez-Lengeling, B.; Aspuru-Guzik, A.; Zhavoronkov, A. Reinforced adversarial neural computer for de novo molecular design. J. Chem. Inf. Model., 2018, 58(6), 1194-1204.
[238]
Simonovsky, M.; Komodakis, N. GraphVAE: Towards generation
of small graphs using variational autoencoders. arXiv:1802.03480
[cs.LG] 2018 https://arxiv.org/abs/1802.03480
[239]
Li, Y.; Zhang, L.; Liu, Z. Multi-Objective de novo drug design with conditional graph generative model. J. Cheminform., 2018, 10(1), 33.
[241]
Tiikkainen, P.; Markt, P.; Wolber, G.; Kirchmair, J.; Distinto, S.; Poso, A.; Kallioniemi, O. Critical comparison of virtual screening methods against the MUV data set. J. Chem. Inf. Model., 2009, 49(10), 2168-2178.
[242]
Huang, Q.; Kang, H.; Zhang, D.; Sheng, Z.; Liu, Q.; Zhu, R.; Cao, Z. Comparison of ligand-, target structure-, and protein-ligand interaction fingerprint-based virtual screening methods. Acta Chimi. Sin., 2011, 69(5), 515-522.
[243]
Niinivehmas, S.P.; Virtanen, S.I.; Lehtonen, J.V.; Postila, P.A.; Pentikäinen, O.T. Comparison of virtual high-throughput screening methods for the identification of phosphodiesterase-5 inhibitors. J. Chem. Inf. Model., 2011, 51(6), 1353-1363.
[244]
Ramasamy, T.; Selvam, C. Performance evaluation of structure based and ligand based virtual screening methods on ten selected anti-cancer targets. Bioorg. Med. Chem. Lett., 2015, 25(20), 4632-4636.
[245]
Klebe, G. Virtual ligand screening: Strategies, perspectives and limitations. Drug Discov. Today, 2006, 11(13-14), 580-594.
[246]
Scior, T.; Bender, A.; Tresadern, G.; Medina-Franco, J.L.; Martínez-Mayorga, K.; Langer, T.; Cuanalo-Contreras, K.; Agrafiotis, D.K. Recognizing pitfalls in virtual screening: A critical review. J. Chem. Inf. Model., 2012, 52(4), 867-881.
[248]
Cereto-Massagué, A.; Ojeda, M.J.; Valls, C.; Mulero, M.; Garcia-Vallvé, S.; Pujadas, G. Molecular fingerprint similarity search in virtual screening. Methods, 2015, 71, 58-63.
[249]
Spyrakis, F.; Cavasotto, C.N. Open challenges in structure-based virtual screening: Receptor modeling, target flexibility consideration and active site water molecules description. Arch. Biochem. Biophys., 2015, 583, 105-119.
[250]
Ripphausen, P.; Nisius, B.; Peltason, L.; Bajorath, J. Quo Vadis, Virtual Screening? A comprehensive survey of prospective applications. J. Med. Chem., 2010, 53(24), 8461-8467.
[251]
Sliwoski, G.; Kothiwale, S.; Meiler, J.; Lowe, E.W. Computational methods in drug discovery. Pharmacol. Rev., 2014, 66(1), 334-395.
[252]
Lavecchia, A. Machine-Learning approaches in drug discovery: Methods and applications. Drug Discov. Today, 2015, 20(3), 318-331.
[253]
Ramakrishnan, R.; von Lilienfeld, O.A. Machine learning, quantum
mechanics, and chemical compound space. arXiv:1510.07512
[physics.chem-ph] 2015 https://arxiv.org/abs/1510.07512/
[254]
Maggiora, G.M. On outliers and activity cliffs-why QSAR often disappoints. J. Chem. Inf. Model., 2006, 46(4), 1535.
[255]
Goh, G.B.; Hodas, N.O.; Vishnu, A. Deep learning for computational chemistry. J. Comput. Chem., 2017, 38(16), 1291-1307.
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