Pharmacophore Modeling in Drug Discovery: Methodology and Current Status

Abstract

A pharmacophore describes the framework of molecular features that are vital for the biological activity of a compound. Pharmacophore models are built by using the structural information about the active ligands or targets. The pharmacophore models developed are used to identify novel compounds that satisfy the pharmacophore requirements and thus expected to be biologically active. Drug discovery process is a challenging task that requires the contribution of multidisciplinary approaches. Pharmacophore modeling has been used in various stages of the drug discovery process. The major application areas are virtual screening, docking, drug target fishing, ligand profiling, and ADMET prediction. There are several pharmacophore modeling programs in use. The user must select the right program for the right purpose carefully. There are new developments in pharmacophore modeling with the involvement of the other computational methods. It has been integrated with molecular dynamics simulations. The latest computational approaches like machine learning have also played an important role in the advances achieved. Moreover, with the rapid advance in computing capacity, data storage, software and algorithms, more advances are anticipated. Pharmacophore modeling has contributed to a faster, cheaper, and more effective drug discovery process. With the integration of pharmacophore modeling with the other computational methods and advances in the latest algorithms, programs that have better perfomance are emerging. Thus, improvements in the quality of the pharmacophore models generated have been achieved with these new developments.

Keywords

• Computational approaches
• computer-aided drug design
• drug discovery
• molecular modeling
• pharmacophore

References

1. 1. Deore A, Dhumane J, Wagh R, Sonawane R. The Stages of Drug Discovery and Development Process. Asian J Pharma Res Dev. 2019;7(6):62–7. DOI: https://doi.org/10.22270/ajprd.v7i6.616.
2. 2. Sliwoski G, Kothiwale S, Meiler J, Lowe EW. Computational methods in drug discovery. Pharmacol Rev. 2014;66(1):334–95. DOI: https://doi.org/10.1124/pr.112.007336.
3. 3. Anh Vu L, Thi Cam Quyen P, Thuy Huong N. In silico Drug Design: Prospective for Drug Lead Discovery. Int J Eng Sci Invent [Internet]. 2015;4(10):60–70. URL: www.ijesi.org%7C%7CVolumewww.ijesi.org.
4. 4. Macalino SJY, Gosu V, Hong S, Choi S. Role of computer-aided drug design in modern drug discovery. Arch Pharm Res. 2015;38(9):1686–701. DOI: https://doi.org/10.1007/s12272-015-0640-5.
5. 5. Prachayasittikul V, Worachartcheewan A, Shoombuatong W, Songtawee N, Simeon S, Prachayasittikul V, et al. Computer-Aided Drug Design of Bioactive Natural Products. Curr Top Med Chem. 2015;15(18):1780–800. URL: https://www.ingentaconnect.com/content/ben/ctmc/2015/00000015/00000018/art00004.
6. 6. Guner O. History and Evolution of the Pharmacophore Concept in Computer-Aided Drug Design. Curr Top Med Chem. 2005;2(12):1321–32. DOI: https://doi.org/10.2174/1568026023392940.
7. 7. Sanders MPA, McGuire R, Roumen L, De Esch IJP, De Vlieg J, Klomp JPG, et al. From the protein’s perspective: The benefits and challenges of protein structure-based pharmacophore modeling. Medchemcomm. 2012;3(1):28–38. DOI: https://doi.org/10.1039/C1MD00210D.
8. 8. Lin, Shu-Kun Sutter, J.M. Hoffman R. HypoGen: An automated system for generating predictive 3D pharmacophore models. In: Güner O, editor. Pharmacophore Perception, Development and Use in Drug Design. International University Line; 2000. p. 171–89.

9. 9. Gao Q, Yang L, Zhu Y. Pharmacophore Based Drug Design Approach as a Practical Process in Drug Discovery. Curr Comput Aided-Drug Des. 2010;6(1):37–49. DOI: https://doi.org/10.2174/157340910790980151.
10. 10. Langer T, Hoffmann RD. Pharmacophore Modelling: Applications in Drug Discovery. Expert Opin Drug Discov. 2006;1(3):261–7. DOI: https://doi.org/10.1517/17460441.1.3.261.
11. 11. Vel EP, Guti PA. Generation of pharmacophores and classification of drugs using protein-ligand complexes Generación de farmacóforos y clasificación de drogas utilizando complejos proteína-ligando Geração de farmacóforos e classificação de fármacos usando-se complexo prote. Rev Colomb Química. 2012;41(3):337–48. URL: http://www.scielo.org.co/scielo.php?pid=S0120-28042012000300001&script=sci_arttext&tlng=en.
12. 12. Schuster D. 3D pharmacophores as tools for activity profiling. Drug Discov Today Technol. 2010;7(4):e205–11. DOI: https://doi.org/10.1016/j.ddtec.2010.11.006.
13. 13. Yang SY. Pharmacophore modeling and applications in drug discovery: Challenges and recent advances. Drug Discov Today. 2010;15(11–12):444–50. DOI: https://doi.org/10.1016/j.drudis.2010.03.013.
14. 14. Vazquez J, Lopez M, Gibert E, Herrero E, Luque FJ. Merging ligand-based and structure-based methods in drug discovery : An overview of combined virtual screening approaches. Molecules. 2020;25:4723–50. DOI: https://doi.org/10.3390/molecules25204723.
15. 15. Zeng L, Guan M, Jin H, Liu Z, Zhang L. Integrating pharmacophore into membrane molecular dynamics simulations to improve homology modeling of G protein-coupled receptors with ligand selectivity: A2A adenosine receptor as an example. Chem Biol Drug Des. 2015;86(6):1438–50. DOI: https://doi.org/10.1111/cbdd.12607.
16. 16. Schaller D, Šribar D, Noonan T, Deng L, Nguyen TN, Pach S, et al. Next generation 3D pharmacophore modeling. Wiley Interdiscip Rev Comput Mol Sci. 2020;10(4):1–20. DOI: https://doi.org/10.1002/wcms.1468.
17. 17. Güner OF, Bowen JP. Setting the record straight: The origin of the pharmacophore concept. J Chem Inf Model. 2014;54(5):1269–83. DOI: https://doi.org/10.1021/ci5000533.
18. 18. Kaserer T, Beck KR, Akram M, Odermatt A, Schuster D, Willett P. Pharmacophore models and pharmacophore-based virtual screening: Concepts and applications exemplified on hydroxysteroid dehydrogenases. Molecules. 2015;20(12):22799–832. DOI: https://doi.org/10.3390/molecules201219880.
19. 19. Bajorath J. Pharmacophore. In: Schwab M, editor. Encyclopedia of Cancer. Berlin Heidelberg: Springer; 2015. p. 2–5. ISBN: 978-3-540-47648-1.
20. 20. McGregor MJ, Muskal SM. Pharmacophore fingerprinting. 1. Application to QSAR and focused library design. J Chem Inf Comput Sci. 1999;39(3):569–74. DOI: https://doi.org/10.1021/ci980159j.
21. 21. Horvath D, Mao B, Gozalbes R, Barbosa F, Rogalski SL. Strengths and Limitations of Pharmacophore-Based Virtual Screening. Chemoinformatics Drug Discov. 2005;23:117–40. DOI: https://doi.org/10.1002/3527603743.ch5.
22. 22. Sheridan RP, Rusinko A, Nilakantan R, Venkataraghavan R. Searching for pharmacophores in large coordinate data bases and its use in drug design. Proc Natl Acad Sci U S A. 1989;86(20):8165–9. DOI: https://doi.org/10.1073/pnas.86.20.8165.
23. 23. Noha SM, Schuster D. Pharmacophore modeling. In: Lill MA, editor. In Silico Drug Discovery and Design. 2013. p. 80–93. ISBN: 9781909453029.
24. 24. Horvath D. Pharmacophore-Based Virtual Screening. In: Bajorah J, editor. Chemoinformatics and Computational Chemical Biology. Springer; 2011. p. 261–97. ISBN: 9781493957934.
25. 25. Leelananda SP, Lindert S. Computational methods in drug discovery. Beilstein J Org Chem. 2016;12:2694–718. DOI: https://doi.org/10.3762/bjoc.12.267.
26. 26. Wolber G, Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model. 2005;45(1):160–9. DOI: https://doi.org/10.1021/ci049885e.
27. 27. Vilar S, Cozza G, Moro S. Medicinal chemistry and the Molecular Operating Environment (MOE): Application of QSAR and molecular docking to drug discovery. Curr Top Med Chem. 2008;8(18):1555–72. DOI: https://doi.org/10.2174/156802608786786624.
28. 28. Chen J, Lai L. Pocket v.2: Further developments on receptor-based pharmacophore modeling. J Chem Inf Model. 2006;46(6):2684–91. DOI: https://doi.org/10.1021/ci600246s.
29. 29. Sanders MPA, Verhoeven S, De Graaf C, Roumen L, Vroling B, Nabuurs SB, et al. Snooker: A structure-based pharmacophore generation tool applied to class A GPCRs. J Chem Inf Model. 2011;51(9):2277–92. DOI: https://doi.org/10.1021/ci200088d.
30. 30. Barnum D, Greeene J, Smellie A, Sprague P. Identification of common functional configurations among molecules. J Chem Inf Comput Sci. 1996;36:563–71. DOI: https://doi.org/10.1021/ci950273r.
31. 31. Richmond NJ, Abrams CA, Wolohan PRN, Abrahamian E, Willett P, Clark RD. GALAHAD: 1. Pharmacophore identification by hypermolecular alignment of ligands in 3D. J Comput Aided Mol Des. 2006;20(9):567–87. DOI: https://doi.org/10.1007/s10822-006-9082-y.
32. 32. Patel Y, Gillet VJ, Bravi G, Leach AR. A comparison of the pharmacophore identification programs: Catalyst, DISCO and GASP. J Comput Aided Mol Des. 2002;16(8–9):653–81. DOI: https://doi.org/10.1023/A:1021954728347.
33. 33. Schneidman-Duhovny D, Dror O, Inbar Y, Nussinov R, Wolfson HJ. PharmaGist: a webserver for ligand-based pharmacophore detection. Nucleic Acids Res. 2008;36 (Web Server issue):223–8. DOI: https://doi.org/10.1093/nar/gkn187.
34. 34. Koes, DR, Camacho CJ. Pharmer: Efficient and exact pharmacophore search. J Chem Inf Model. 2011;51(6):1307–14. DOI: https://doi.org/10.1021/ci200097m.
35. 35. Liu X, Ouyang S, Yu B, Liu Y, Huang K, Gong J, et al. PharmMapper server: A web server for potential drug target identification using pharmacophore mapping approach. Nucleic Acids Res. 2010;38(SUPPL. 2):5–7. DOI: https://doi.org/10.1093/nar/gkq300.
36. 36. Dixon SL, Smondyrev AM, Rao SN. PHASE: A novel approach to pharmacophore modeling and 3D database searching. Chem Biol Drug Des. 2006;67(5):370–2. DOI: https://doi.org/10.1111/j.1747-0285.2006.00384.x.
37. 37. Qing X, Lee XY, De Raeymaeker J, Tame JR, Zhang KY, De Maeyer M, et al. Pharmacophore modeling: Advances, limitations, and current utility in drug discovery. J Receptor Ligand Channel Res. 2014;7:81–92. DOI: https://doi.org/10.2147/JRLCR.S46843.
38. 38. Pauli I, Dos Santos RN, Rostirolla DC, Martinelli LK, Ducati RG, Timmers LFSM, et al. Discovery of new inhibitors of mycobacterium tuberculosis InhA enzyme using virtual screening and a 3D-pharmacophore-based approach. J Chem Inf Model. 2013;53(9):2390–401. DOI: https://doi.org/10.1021/ci400202t.
39. 39. Rampogu S, Lee KW. Pharmacophore Modelling-Based Drug Repurposing Approaches for SARS-CoV-2 Therapeutics. Front Chem. 2021;9(May):1–10. DOI: https://dx.doi.org/10.3389%2Ffchem.2021.636362.
40. 40. Medina-Franco JL. Advances in computational approaches for drug discovery based on natural products. Rev Latinoam Quim. 2013;41(2):95–110. URL: http://www.scielo.org.mx/scielo.php?pid=S0370-59432013000200003&script=sci_arttext&tlng=en.
41. 41. Koutsoukas A, Simms B, Kirchmair J, Bond PJ, Whitmore A V., Zimmer S, et al. From in silico target prediction to multi-target drug design: Current databases, methods and applications. J Proteomics. 2011;74(12):2554–74. DOI: https://doi.org/10.1016/j.jprot.2011.05.011.
42. 42. Rollinger JM, Schuster D, Danzl B, Schwaiger S, Markt P, Schmidtke M, et al. In silico target fishing for rationalized ligand discovery exemplified on constituents of Ruta graveolens. Planta Med. 2009;75(3):195–204. DOI: https://doi.org/10.1055%2Fs-0028-1088397.
43. 43. Rognan D. Structure-based approaches to target fishing and ligand profiling. Mol Inform. 2010;29(3):176–87. DOI: https://doi.org/10.1002/minf.200900081.
44. 44. Rella M, Rushworth CA, Guy JL, Turner AJ, Langer T, Jackson RM. Structure-based pharmacophore design and virtual screening for novel Angiotensin Converting Enzyme 2 inhibitors. J Chem Inf Model. 2006;46(2):708–16. DOI: https://doi.org/10.1021/ci0503614.
45. 45. Caporuscio F, Tafi A. Pharmacophore Modelling: A Forty Year Old Approach and its Modern Synergies. Curr Med Chem. 2011;18(17):2543–53. DOI: https://doi.org/10.2174/092986711795933669.
46. 46. Paliwal S, Mittal A, Sharma M, Pandey A, Singh A, Paliwal S. Pharmacophore and molecular docking based identification of novel structurally diverse PDE-5 inhibitors. Med Chem Res. 2015;24(2):576–87. DOI: https://doi.org/10.1007/s00044-014-1144-4.
47. 47. Peach ML, Nicklaus MC. Combining docking with pharmacophore filtering for improved virtual screening. J Cheminform. 2009;1(1):1–15. DOI: https://doi.org/10.1186/1758-2946-1-6.
48. 48. Hindle SA, Rarey M, Buning C, Lengauer T. Flexible docking under pharmacophore type constraints. J Comput Aided Mol Des. 2002;16(2):129–49. DOI: https://doi.org/10.1023/A:1016399411208.
49. 49. Mobley DL, Lim NM, Wymer KL. Blind prediction of HIV integrase binding from the SAMPL4 challenge. J Comput Aided Mol Des. 2014;28(4):327–45. DOI: https://doi.org/10.1007/s10822-014-9723-5.
50. 50. Lyne PD, Kenny PW, Cosgrove DA, Deng C, Zabludoff S, Wendoloski JJ, et al. Identification of Compounds with Nanomolar Binding Affinity for Checkpoint Kinase-1 Using Knowledge-Based Virtual Screening. J Med Chem. 2004;47(8):1962–8. DOI: https://doi.org/10.1021/jm030504i.
51. 51. Wang M, Hou S, Wei Y, Li D, Lin J. Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking. PLoS Comput Biol. 2021;17(3):1–23. DOI: https://doi.org/10.1371/journal.pcbi.1008821.
52. 52. Alavijeh MS, Palmer AM. The pivotal role of drug metabolism and pharmacokinetics in the discovery and development of new medicines. Curr Opin Investig drugs J. 2004;7(8):755–63.
53. 53. Guner O, Bowen J. Pharmacophore modeling for ADME. Curr Top Med Chem. 2013;13(11):1327–42. URL: https://www.ingentaconnect.com/content/ben/ctmc/2013/00000013/00000011/art00007.
54. 54. Mohan CG. Structural Bioinformatics: Applications in Preclinical Drug Discovery Process. Challenges and Advances in Computational Chemistry and Physics. Springer Nature; 2019. 25–55 p. ISBN: 978-3-030-05281-2.
55. 55. De Groot MJ, Ekins S. Pharmacophore modeling of cytochromes P450. Adv Drug Deliv Rev. 2002;54(3):367–83. DOI: https://doi.org/10.1016/S0169-409X(02)00009-1.
56. 56. Sorich MJ, Miners JO, McKinnon RA, Smith PA. Multiple pharmacophores for the investigation of human UDP-glucuronosyltransferase isoform substrate selectivity. Mol Pharmacol. 2004;65(2):301–8. DOI: https://doi.org/10.1124/mol.65.2.301.
57. 57. Hassan Baig M, Ahmad K, Roy S, Mohammad Ashraf J, Adil M, Haris Siddiqui M, et al. Computer Aided Drug Design: Success and Limitations. Curr Pharm Des. 2016;22(5):572–81. URL: https://www.ingentaconnect.com/content/ben/cpd/2016/00000022/00000005/art00008.
58. 58. Hamza A, Wei N-N, Zhan C-G. Ligand-Based Virtual Screening Approach Using a New Scoring Function. J Chem Inf Model. 2012;52(4):963–74. DOI: https://doi.org/10.1021/ci200617d.
59. 59. Scior T, Bender A, Tresadern G, Medina-Franco JL, Martínez-Mayorga K, Langer T, et al. Recognizing pitfalls in virtual screening: A critical review. J Chem Inf Model. 2012;52(4):867–81. DOI: https://doi.org/10.1021/ci200528d.
60. 60. Chandrasekaran B, Agrawal N, Kaushik S. Pharmacore development. In: Encyclopedia of bioinformatics and computational biology: ABC of bioinformatics. Elsevier; 2019. p. 677–87.
61. 61. Wolber G, Seidel T, Bendix F, Langer T. Molecule-pharmacophore superpositioning and pattern matching in computational drug design. Drug Discov Today. 2008;13(1–2):23–9. DOI: https://doi.org/10.1016/j.drudis.2007.09.007.
62. 62. Gimeno A, Ojeda-Montes MJ, Tomás-Hernández S, Cereto-Massagué A, Beltrán-Debón R, Mulero M, et al. The light and dark sides of virtual screening: What is there to know? Int J Mol Sci. 2019;20(6):1375–99. DOI: https://doi.org/10.3390/ijms20061375.
63. 63. Kirchmair J, Wolber G, Laggner C, Langer T. Comparative performance assessment of the conformational model generators omega and catalyst: A large-scale survey on the retrieval of protein-bound ligand conformations. J Chem Inf Model. 2006;46(4):1848–61. DOI: https://doi.org/10.1021/ci060084g.
64. 64. Drwal MN, Griffith R. Combination of ligand- and structure-based methods in virtual screening. Drug Discov Today Technol. 2013;10(3):e395–401. DOI: https://doi.org/10.1016/j.ddtec.2013.02.002.
65. 65. Vancraenenbroeck R, De Raeymaecker J, Lobbestael E, Gao F, De Maeyer M, Voet A, et al. Insilico, in vitro and cellular analysis with a kinome-wide inhibitor panel correlates cellular LRRK2 dephosphorylation to inhibitor activity on LRRK2. Front Mol Neurosci. 2014;7:1–19. DOI: https://doi.org/10.3389/fnmol.2014.00051.
66. 66. Dror O, Shulman-Peleg A, Nussinov R, Wolfson HJ. Predicting molecular interactions in silico: I. A guide to pharmacophore identification and its applications to drug design. Curr Med Chem. 2004;11:71–90. DOI: https://doi.org/10.2174/0929867043456287.
67. 67. Damm KL, Carlson HA. Exploring experimental sources of multiple protein conformations in structure-based drug design. J Am Chem Soc. 2007;129(26):8225–35. DOI: https://doi.org/10.1021/ja0709728.
68. 68. Hu B, Lill MA. Protein pharmacophore selection using hydration-site analysis. J Chem Inf Model. 2012;52(4):1046–60. DOI: https://doi.org/10.1021/ci200620h.
69. 69. Yu W, Lakkaraju SK, Raman EP, MacKerell AD. Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling. J Comput Aided Mol Des. 2014;28(5):491–507. DOI: https://doi.org/10.1007/s10822-014-9728-0.
70. 70. Sydow D. Dynophores: Novel Dynamic Pharmacophores. [Berlin]: Humboldt-Universität zu Berlin; 2015.
71. 71. Arba M, Nur-Hidayat A, Surantaadmaja SI, Tjahjono DH. Pharmacophore-based virtual screening for identifying β5 subunit inhibitor of 20S proteasome. Comput Biol Chem. 2018;77(August):64–71. DOI: https://doi.org/10.1016/j.compbiolchem.2018.08.009.
72. 72. Saxena S, Abdullah M, Sriram D, Guruprasad L. Discovery of novel inhibitors of mycobacterium tuberculosis murg: Homology modelling, structure based pharmacophore, molecular docking, and molecular dynamics simulations. J Biomol Struct Dyn. 2018;36(12):3184–98. DOI: https://doi.org/10.1080/07391102.2017.1384398.
73. 73. James N, Ramanathan K. Ligand-Based Pharmacophore Screening Strategy: a Pragmatic Approach for Targeting HER Proteins. Appl Biochem Biotechnol. 2018;186(1):85–108. DOI: https://doi.org/10.1007/s12010-018-2724-4.
74. 74. Patel S, Modi P, Chhabria M. Rational approach to identify newer caspase-1 inhibitors using pharmacophore based virtual screening, docking and molecular dynamic simulation studies. J Mol Graph Model. 2018;81:106–15. DOI: https://doi.org/10.1016/j.jmgm.2018.02.017.
75. 75. Saddala MS, Huang H. Identification of novel inhibitors for TNFα, TNFR1 and TNFα-TNFR1 complex using pharmacophore-based approaches. J Transl Med [Internet]. 2019;17(1):1–16. DOI: https://doi.org/10.1186/s12967-019-1965-5.
76. 76. Kashyap A, Singh PK, Satpati S, Verma H, Silakari O. Pharmacophore modeling and molecular dynamics approach to identify putative DNA Gyrase B inhibitors for resistant tuberculosis. J Cell Biochem. 2019;120(3):3149–59. DOI: https://doi.org/10.1002/jcb.27579.
77. 77. KB S, Kumari A, Shetty D, Fernandes E, DV C, Jays J, et al. Structure based pharmacophore modelling approach for the design of azaindole derivatives as DprE1 inhibitors for tuberculosis. J Mol Graph Model. 2020;101:107718. DOI: https://doi.org/10.1016/j.jmgm.2020.107718.
78. 78. Yoshino R, Yasuo N, Sekijima M. Identification of key interactions between SARS-CoV-2 main protease and inhibitor drug candidates. Sci Rep. 2020;10(1):1–8. DOI: https://doi.org/10.1038/s41598-020-69337-9.
79. 79. Shehroz M, Zaheer T, Hussain T. Computer-aided drug design against spike glycoprotein of SARS-CoV-2 to aid COVID-19 treatment. Heliyon. 2020;6(10):e05278. DOI: https://doi.org/10.1016/j.heliyon.2020.e05278.
80. 80. Battisti V, Wieder O, Garon A, Seidel T, Urban E, Langer T. A Computational Approach to Identify Potential Novel Inhibitors against the Coronavirus SARS-CoV-2. Mol Inform. 2020;39(10):1–8. DOI: https://doi.org/10.1002/minf.202000090.
81. 81. Prabhu SV, Singh SK. Energetically optimized pharmacophore modeling to identify dual negative allosteric modulators against group I mGluRs in neurodegenerative diseases. J Biomol Struct Dyn. 2020;38(8):2326–37. DOI: https://doi.org/10.1080/07391102.2019.1640794.
82. 82. Jade DD, Pandey R, Kumar R, Gupta D. Ligand-based pharmacophore modeling of TNF-α to design novel inhibitors using virtual screening and molecular dynamics. J Biomol Struct Dyn. 2020;0(0):1–17. DOI: https://doi.org/10.1080/07391102.2020.1831962.
83. 83. Bolelli K, Ertan-Bolelli T. Pharmacophore-based virtual screening of novel GSTP1-1 inhibitors. J Turkish Chem Soc Sect A Chem. 2018;5(3):1279–86. DOI: https://doi.org/10.18596/jotcsa.466458.
84. 84. Ain QU, Aleksandrova A, Roessler FD, Ballester PJ. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening. Wiley Interdiscip Rev Comput Mol Sci. 2015;5(6):405–24. DOI: https://doi.org/10.1002/wcms.1225.
85. 85. Barillari C, Marcou G, Rognan D. Hot-spots-guided receptor-based pharmacophores (HS-pharm): A knowledge-based approach to identify ligand-anchoring atoms in protein cavities and prioritize structure-based pharmacophores. J Chem Inf Model. 2008;48(7):1396–410. DOI: https://doi.org/10.1021/ci800064z.
86. 86. Sato T, Honma T, Yokoyama S. Combining machine learning and pharmacophore-based interaction fingerprint for in silico screening. J Chem Inf Model. 2010;50(1):170–85. DOI: https://doi.org/10.1021/ci900382e.
87. 87. Jiménez J, Doerr S, Martínez-Rosell G, Rose AS, De Fabritiis G. DeepSite: Protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics. 2017;33(19):3036–42. DOI: https://doi.org/10.1093/bioinformatics/btx350.