Advances in Deep Learning Assisted Drug Discovery Methods: A Self-review


Cite item

Full Text

Abstract

:Artificial Intelligence is a field within computer science that endeavors to replicate the intricate structures and operational mechanisms inherent in the human brain. Machine learning is a subfield of artificial intelligence that focuses on developing models by analyzing training data. Deep learning is a distinct subfield within artificial intelligence, characterized by using models that depict geometric transformations across multiple layers. The deep learning has shown significant promise in various domains, including health and life sciences. In recent times, deep learning has demonstrated successful applications in drug discovery. In this self-review, we present recent methods developed with the aid of deep learning. The objective is to give a brief overview of the present cutting-edge advancements in drug discovery from our group. We have systematically discussed experimental evidence and proof of concept examples for the deep learning-based models developed, such as Deep- BindBC, DeepPep, and DeepBindRG. These developments not only shed light on the existing challenges but also emphasize the achievements and prospects for future drug discovery and development progress.

About the authors

Haiping Zhang

Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences

Email: info@benthamscience.net

Konda Saravanan

Department of Biotechnology, Bharath Institute of Higher Education and Research

Author for correspondence.
Email: info@benthamscience.net

References

  1. Zhu H. Big data and artificial intelligence modeling for drug discovery. Annu Rev Pharmacol Toxicol 2020; 60(1): 573-89. doi: 10.1146/annurev-pharmtox-010919-023324 PMID: 31518513
  2. Lin X, Li X, Lin X. A review on applications of computational methods in drug screening and design. Molecules 2020; 25(6): 1375. doi: 10.3390/molecules25061375 PMID: 32197324
  3. Schaduangrat N, Lampa S, Simeon S, Gleeson MP, Spjuth O, Nantasenamat C. Towards reproducible computational drug discovery. J Cheminform 2020; 12(1): 9. doi: 10.1186/s13321-020-0408-x PMID: 33430992
  4. Wang CC, Zhao Y, Chen X. Drug-pathway association prediction: From experimental results to computational models. Brief Bioinform 2021; 22(3): bbaa061. doi: 10.1093/bib/bbaa061 PMID: 32393976
  5. Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: Taxonomy, trends and challenges of computational models. Brief Bioinform 2022; 23(5): bbac358. doi: 10.1093/bib/bbac358 PMID: 36056743
  6. Ghoussaini M, Nelson MR, Dunham I. Future prospects for human genetics and genomics in drug discovery. Curr Opin Struct Biol 2023; 80: 102568. doi: 10.1016/j.sbi.2023.102568 PMID: 36963162
  7. Sabe VT, Ntombela T, Jhamba LA, et al. Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review. Eur J Med Chem 2021; 224: 113705. doi: 10.1016/j.ejmech.2021.113705 PMID: 34303871
  8. Wang W, Ye Z, Gao H, Ouyang D. Computational pharmaceutics - A new paradigm of drug delivery. J Control Release 2021; 338: 119-36. doi: 10.1016/j.jconrel.2021.08.030 PMID: 34418520
  9. Yu W, MacKerell AD. Computer-aided drug design methods BT - antibiotics: Methods and protocols. In: Sass P, Ed. Springer New York. New York, NY 2017; pp. 85-106.
  10. Li J, Fu A, Zhang L. An overview of scoring functions used for protein-ligand interactions in molecular docking. Interdiscip Sci 2019; 11(2): 320-8. doi: 10.1007/s12539-019-00327-w PMID: 30877639
  11. Adelusi TI, Oyedele AQK, Boyenle ID, et al. Molecular modeling in drug discovery. Inform Med Unlocked 2022; 29: 100880. doi: 10.1016/j.imu.2022.100880
  12. Giordano D, Biancaniello C, Argenio MA, Facchiano A. Drug design by pharmacophore and virtual screening approach. Pharmaceuticals 2022; 15(5): 646. doi: 10.3390/ph15050646 PMID: 35631472
  13. Schneider P, Walters WP, Plowright AT, et al. Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov 2020; 19(5): 353-64. doi: 10.1038/s41573-019-0050-3 PMID: 31801986
  14. Lavecchia A. Deep learning in drug discovery: Opportunities, challenges and future prospects. Drug Discov Today 2019; 24(10): 2017-32. doi: 10.1016/j.drudis.2019.07.006 PMID: 31377227
  15. Shaker B, Ahmad S, Lee J, Jung C, Na D. In silico methods and tools for drug discovery. Comput Biol Med 2021; 137: 104851. doi: 10.1016/j.compbiomed.2021.104851 PMID: 34520990
  16. Zafar I, Anwar S. kanwal F, et al. Reviewing methods of deep learning for intelligent healthcare systems in genomics and biomedicine. Biomed Signal Process Control 2023; 86: 105263. doi: 10.1016/j.bspc.2023.105263
  17. Sreeraman S, Kannan MP, Singh Kushwah RB, et al. Drug design and disease diagnosis: The potential of deep learning models in biology. Curr Bioinform 2023; 18(3): 208-20. doi: 10.2174/1574893618666230227105703
  18. Terranova N, Venkatakrishnan K, Benincosa LJ. Application of machine learning in translational medicine: Current status and future opportunities. AAPS J 2021; 23(4): 74. doi: 10.1208/s12248-021-00593-x PMID: 34008139
  19. Hernández Medina R, Kutuzova S, Nielsen KN, et al. Machine learning and deep learning applications in microbiome research. ISME Communications 2022; 2(1): 98. doi: 10.1038/s43705-022-00182-9 PMID: 37938690
  20. Jiang Y, Luo J, Huang D, Liu Y, Li D. Machine learning advances in microbiology: A review of methods and applications. Front Microbiol 2022; 13: 925454. doi: 10.3389/fmicb.2022.925454 PMID: 35711777
  21. Ahmed SF, Alam MSB, Hassan M, et al. Deep learning modelling techniques: Current progress, applications, advantages, and challenges. Artif Intell Rev 2023; 56(11): 13521-617. doi: 10.1007/s10462-023-10466-8
  22. Selvaraj C, Chandra I, Singh SK. Artificial intelligence and machine learning approaches for drug design: Challenges and opportunities for the pharmaceutical industries. Mol Divers 2022; 26(3): 1893-913. doi: 10.1007/s11030-021-10326-z PMID: 34686947
  23. Miethke M, Pieroni M, Weber T, et al. Towards the sustainable discovery and development of new antibiotics. Nat Rev Chem 2021; 5(10): 726-49. doi: 10.1038/s41570-021-00313-1
  24. Dara S, Dhamercherla S, Jadav SS, Babu CHM, Ahsan MJ. Machine learning in drug discovery: A review. Artif Intell Rev 2022; 55(3): 1947-99. doi: 10.1007/s10462-021-10058-4 PMID: 34393317
  25. Vamathevan J, Clark D, Czodrowski P, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 2019; 18(6): 463-77. doi: 10.1038/s41573-019-0024-5 PMID: 30976107
  26. Carracedo-Reboredo P, Liñares-Blanco J, Rodríguez-Fernández N, et al. A review on machine learning approaches and trends in drug discovery. Comput Struct Biotechnol J 2021; 19: 4538-58. doi: 10.1016/j.csbj.2021.08.011 PMID: 34471498
  27. Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug Discov Today 2018; 23(6): 1241-50. doi: 10.1016/j.drudis.2018.01.039 PMID: 29366762
  28. Sarkar C, Das B, Rawat VS, et al. Artificial intelligence and machine learning technology driven modern drug discovery and development. Int J Mol Sci 2023; 24(3): 2026. doi: 10.3390/ijms24032026 PMID: 36768346
  29. Odell SG, Lazo GR, Woodhouse MR, Hane DL, Sen TZ. The art of curation at a biological database: Principles and application. Curr Plant Biol 2017; 11-12: 2-11. doi: 10.1016/j.cpb.2017.11.001
  30. Torne L, Binns R. Drug development and therapeutic solutions in the digital age. Drug Discov Today 2018; 23(12): 1922-4. doi: 10.1016/j.drudis.2018.09.005 PMID: 30227241
  31. Goecks J, Jalili V, Heiser LM, Gray JW. How machine learning will transform biomedicine. Cell 2020; 181(1): 92-101. doi: 10.1016/j.cell.2020.03.022 PMID: 32243801
  32. Ching T, Himmelstein DS, Beaulieu-Jones BK, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface 2018; 15(141): 20170387. doi: 10.1098/rsif.2017.0387 PMID: 29618526
  33. Cao C, Liu F, Tan H, et al. Deep learning and its applications in biomedicine. Genom Proteom Bioinform 2018; 16(1): 17-32. doi: 10.1016/j.gpb.2017.07.003 PMID: 29522900
  34. Zemouri R, Zerhouni N, Racoceanu D. Deep learning in the biomedical applications: Recent and future status. Appl Sci 2019; 9(8): 1526. doi: 10.3390/app9081526
  35. Baldi P. Deep learning in biomedical data science. Annu Rev Biomed Data Sci 2018; 1(1): 181-205. doi: 10.1146/annurev-biodatasci-080917-013343
  36. Yang S, Zhu F, Ling X, Liu Q, Zhao P. Intelligent health care: Applications of deep learning in computational medicine. Front Genet 2021; 12: 607471. doi: 10.3389/fgene.2021.607471 PMID: 33912213
  37. Matsuzaka Y, Yashiro R. Applications of deep learning for drug discovery systems with big data. BioMedInformatics 2022; 2(4): 603-24. doi: 10.3390/biomedinformatics2040039
  38. Jiang D, Wu Z, Hsieh CY, et al. Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models. J Cheminform 2021; 13(1): 12. doi: 10.1186/s13321-020-00479-8 PMID: 33597034
  39. Nag S, Baidya ATK, Mandal A, et al. Deep learning tools for advancing drug discovery and development. 3 Biotech 2022; 12: 110.
  40. Runcie NT, Mey ASJS. SILVR: Guided diffusion for molecule generation. J Chem Inf Model 2023; 63(19): 5996-6005. doi: 10.1021/acs.jcim.3c00667 PMID: 37724771
  41. Watson JL, Juergens D, Bennett NR, et al. De novo design of protein structure and function with RFdiffusion. Nature 2023; 620(7976): 1089-100. doi: 10.1038/s41586-023-06415-8 PMID: 37433327
  42. Khakzad H, Igashov I, Schneuing A, Goverde C, Bronstein M, Correia B. A new age in protein design empowered by deep learning. Cell Syst 2023; 14(11): 925-39. doi: 10.1016/j.cels.2023.10.006 PMID: 37972559
  43. Niranjan V, Uttarkar A, Ramakrishnan A, et al. De novo design of anti-covid drugs using machine learning-based equivariant diffusion model targeting the spike protein. Curr Issues Mol Biol 2023; 45(5): 4261-84. doi: 10.3390/cimb45050271 PMID: 37232740
  44. Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021; 596(7873): 583-9. doi: 10.1038/s41586-021-03819-2 PMID: 34265844
  45. Zhang H, Saravanan KM, Yang Y, Wei Y, Yi P, Zhang JZH. Generating and screening de novo compounds against given targets using ultrafast deep learning models as core components. Brief Bioinform 2022; 23(4): bbac226. doi: 10.1093/bib/bbac226 PMID: 35724626
  46. Meyers J, Fabian B, Brown N. De novo molecular design and generative models. Drug Discov Today 2021; 26(11): 2707-15. doi: 10.1016/j.drudis.2021.05.019 PMID: 34082136
  47. Lu F, Li M, Min X, Li C, Zeng X. De novo generation of dual-target ligands using adversarial training and reinforcement learning. Brief Bioinform 2021; 22(6): bbab333. doi: 10.1093/bib/bbab333 PMID: 34410338
  48. Zhavoronkov A, Ivanenkov YA, Aliper A, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol 2019; 37(9): 1038-40. doi: 10.1038/s41587-019-0224-x PMID: 31477924
  49. Li Y, Hu J, Wang Y, Zhou J, Zhang L, Liu Z. DeepScaffold: A comprehensive tool for scaffold-based de novo drug discovery using deep learning. J Chem Inf Model 2020; 60(1): 77-91. doi: 10.1021/acs.jcim.9b00727 PMID: 31809029
  50. Li Y, Pei J, Lai L. Structure-based de novo drug design using 3D deep generative models. Chem Sci 2021; 12(41): 13664-75. doi: 10.1039/D1SC04444C PMID: 34760151
  51. Zhang H, Saravanan KM, Yang Y, et al. Deep learning based drug screening for novel coronavirus 2019-nCov. Interdiscip Sci 2020; 12(3): 368-76. doi: 10.1007/s12539-020-00376-6 PMID: 32488835
  52. Bento AP, Hersey A, Félix E, et al. An open source chemical structure curation pipeline using RDKit. J Cheminform 2020; 12(1): 51. doi: 10.1186/s13321-020-00456-1 PMID: 33431044
  53. Akbarian M, Khani A, Eghbalpour S, Uversky VN. Bioactive peptides: Synthesis, sources, applications, and proposed mechanisms of action. Int J Mol Sci 2022; 23(3): 1445. doi: 10.3390/ijms23031445 PMID: 35163367
  54. Wang L, Wang N, Zhang W, et al. Therapeutic peptides: Current applications and future directions. Signal Transduct Target Ther 2022; 7(1): 48. doi: 10.1038/s41392-022-00904-4 PMID: 35165272
  55. Anjum K, Abbas SQ, Akhter N, Shagufta BI, Shah SAA, Hassan SS. Emerging biopharmaceuticals from bioactive peptides derived from marine organisms. Chem Biol Drug Des 2017; 90(1): 12-30. doi: 10.1111/cbdd.12925 PMID: 28004491
  56. Wan F, Kontogiorgos-Heintz D, de la Fuente-Nunez C. Deep generative models for peptide design. Digital Discovery 2022; 1(3): 195-208. doi: 10.1039/D1DD00024A PMID: 35769205
  57. Zhang H, Saravanan KM, Wei Y, et al. Deep learning-based bioactive therapeutic peptide generation and screening. J Chem Inf Model 2023; 63(3): 835-45. doi: 10.1021/acs.jcim.2c01485 PMID: 36724090
  58. Zheng D, Liwinski T, Elinav E. Interaction between microbiota and immunity in health and disease. Cell Res 2020; 30(6): 492-506. doi: 10.1038/s41422-020-0332-7 PMID: 32433595
  59. Theillet FX, Binolfi A, Frembgen-Kesner T, et al. Physicochemical properties of cells and their effects on intrinsically disordered proteins (IDPs). Chem Rev 2014; 114(13): 6661-714. doi: 10.1021/cr400695p PMID: 24901537
  60. Díaz-Villanueva J, Díaz-Molina R, García-González V. Protein folding and mechanisms of proteostasis. Int J Mol Sci 2015; 16(8): 17193-230. doi: 10.3390/ijms160817193 PMID: 26225966
  61. Mutharasu G, Murugesan A, Kondamani S, Thiyagarajan R, Yli-Harja O, Kandhavelu M. Signaling landscape of mitochondrial non-coding RNAs. J Biomol Struct Dyn 2023; 41(21): 12016-25. doi: 10.1080/07391102.2022.2164520 PMID: 36617957
  62. Kannan MP, Sreeraman S, Somala CS, et al. Advancement of targeted protein degradation strategies as therapeutics for undruggable disease targets. Future Med Chem 2023; 15(10): 867-83. doi: 10.4155/fmc-2023-0072 PMID: 37254917
  63. Saravanan KM, Ponnuraj K. Sequence and structural analysis of fibronectin‐binding protein reveals importance of multiple intrinsic disordered tandem repeats. J Mol Recognit 2019; 32(4): e2768. doi: 10.1002/jmr.2768 PMID: 30397967
  64. Manoharan P, Saravanan KM. Computational profiling of pore properties of outer membrane proteins. J Biomol Struct Dyn 2017; 35(11): 2372-81. doi: 10.1080/07391102.2016.1220329 PMID: 27494049
  65. Zhang H, Yang Y, Li J, et al. A novel virtual screening procedure identifies Pralatrexate as inhibitor of SARS-CoV-2 RdRp and it reduces viral replication in vitro. PLOS Comput Biol 2020; 16(12): e1008489. doi: 10.1371/journal.pcbi.1008489 PMID: 33382685
  66. Saravanan KM, Zhang H, Hossain MT, Reza MS, Wei Y. Deep learning-based drug screening for covid-19 and case studies. In: Methods in Pharmacology and Toxicology;. 2021; pp. 631-60. doi: 10.1007/7653_2020_58
  67. Yu H, Li C, Wang X, et al. Techniques and strategies for potential protein target discovery and active pharmaceutical molecule screening in a pandemic. J Proteome Res 2020; 19(11): 4242-58. doi: 10.1021/acs.jproteome.0c00372 PMID: 32957788
  68. Zhang H, Li J, Saravanan KM, et al. An integrated deep learning and molecular dynamics simulation-based screening pipeline identifies inhibitors of a new cancer drug target TIPE2. Front Pharmacol 2021; 12: 772296. doi: 10.3389/fphar.2021.772296 PMID: 34887765
  69. Saravanan KM, Kannan M, Meera P, Bharathkumar N, Anand T. E3 ligases: A potential multi-drug target for different types of cancers and neurological disorders. Future Med Chem 2022; 14(3): 187-201. doi: 10.4155/fmc-2021-0157 PMID: 35100004
  70. Raslan MA, Raslan SA, Shehata EM, Mahmoud AS, Sabri NA. Advances in the applications of bioinformatics and chemoinformatics. Pharmaceuticals 2023; 16(7): 1050. doi: 10.3390/ph16071050 PMID: 37513961
  71. Noor F, Asif M, Ashfaq UA, Qasim M. Tahir ul Qamar M. Machine learning for synergistic network pharmacology: A comprehensive overview. Brief Bioinform 2023; 24(3): bbad120. doi: 10.1093/bib/bbad120 PMID: 37031957
  72. Zhao L, Zhang H, Li N, et al. Network pharmacology, a promising approach to reveal the pharmacology mechanism of Chinese medicine formula. J Ethnopharmacol 2023; 309: 116306. doi: 10.1016/j.jep.2023.116306 PMID: 36858276
  73. Löscher W. Single-target versus multi-target drugs versus combinations of drugs with multiple targets: Preclinical and clinical evidence for the treatment or prevention of epilepsy. Front Pharmacol 2021; 12: 730257. doi: 10.3389/fphar.2021.730257 PMID: 34776956
  74. Premkumar T, Sajitha Lulu S. Molecular mechanisms of emerging therapeutic targets in alzheimer’s disease: A systematic review. Neurochem J 2022; 16(4): 443-55. doi: 10.1134/S1819712422040183
  75. Unni PA, Pillai GG, Sajithalulu S. Biological processes and key druggable targets involved in age-associated memory loss: A systematic review. Life Sci 2021; 270: 119079. doi: 10.1016/j.lfs.2021.119079 PMID: 33460668
  76. Isert C, Atz K, Schneider G. Structure-based drug design with geometric deep learning. Curr Opin Struct Biol 2023; 79: 102548. doi: 10.1016/j.sbi.2023.102548 PMID: 36842415
  77. Grinter SZ, Liang Y, Huang SY, Hyder SM, Zou X. An inverse docking approach for identifying new potential anti-cancer targets. J Mol Graph Model 2011; 29(6): 795-9. doi: 10.1016/j.jmgm.2011.01.002 PMID: 21315634
  78. Xu X, Huang M, Zou X. Docking-based inverse virtual screening: methods, applications, and challenges. Biophys Rep 2018; 4(1): 1-16. doi: 10.1007/s41048-017-0045-8 PMID: 29577065
  79. Zhang H, Liao L, Cai Y, Hu Y, Wang H. IVS2vec: A tool of Inverse Virtual Screening based on word2vec and deep learning techniques. Methods 2019; 166: 57-65. doi: 10.1016/j.ymeth.2019.03.012 PMID: 30910562
  80. Jaeger S, Fulle S, Turk S. Mol2vec: Unsupervised machine learning approach with chemical intuition. J Chem Inf Model 2018; 58(1): 27-35. doi: 10.1021/acs.jcim.7b00616 PMID: 29268609
  81. Fu Y, Zhao J, Chen Z. Insights into the molecular mechanisms of protein-ligand interactions by molecular docking and molecular dynamics simulation: A case of oligopeptide binding protein. Comput Math Methods Med 2018; 2018: 1-12. doi: 10.1155/2018/3502514 PMID: 30627209
  82. Knutson C, Bontha M, Bilbrey JA, Kumar N. Decoding the protein-ligand interactions using parallel graph neural networks. Sci Rep 2022; 12(1): 7624. doi: 10.1038/s41598-022-10418-2 PMID: 35538084
  83. Davis FP, Sali A. The overlap of small molecule and protein binding sites within families of protein structures. PLOS Comput Biol 2010; 6(2): e1000668. doi: 10.1371/journal.pcbi.1000668 PMID: 20140189
  84. Ayaz P, Lyczek A, Paung Y, et al. Structural mechanism of a drug-binding process involving a large conformational change of the protein target. Nat Commun 2023; 14(1): 1885. doi: 10.1038/s41467-023-36956-5 PMID: 37019905
  85. Ge Y, Ganamet K. Using sitemap to aid in the identification of cryptic binding pockets. Biophys J 2023; 122(3): 142a. doi: 10.1016/j.bpj.2022.11.927
  86. Xu X, Duan R, Zou X. Template‐guided method for protein-ligand complex structure prediction: Application to CASP15 protein-ligand studies. Proteins 2023; 91(12): 1829-36. doi: 10.1002/prot.26535 PMID: 37283068
  87. Sadybekov AV, Katritch V. Computational approaches streamlining drug discovery. Nature 2023; 616(7958): 673-85. doi: 10.1038/s41586-023-05905-z PMID: 37100941
  88. Borkakoti N, Thornton JM. AlphaFold2 protein structure prediction: Implications for drug discovery. Curr Opin Struct Biol 2023; 78: 102526. doi: 10.1016/j.sbi.2022.102526 PMID: 36621153
  89. Wu K, Karapetyan E, Schloss J, Vadgama J, Wu Y. Advancements in small molecule drug design: A structural perspective. Drug Discov Today 2023; 28(10): 103730. doi: 10.1016/j.drudis.2023.103730 PMID: 37536390
  90. Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE. A geometric approach to macromolecule-ligand interactions. J Mol Biol 1982; 161(2): 269-88. doi: 10.1016/0022-2836(82)90153-X PMID: 7154081
  91. Laskowski RA. SURFNET: A program for visualizing molecular surfaces, cavities, and intermolecular interactions. J Mol Graph 1995; 13(5): 323-330, 307-308. doi: 10.1016/0263-7855(95)00073-9 PMID: 8603061
  92. Hendlich M, Rippmann F, Barnickel G. LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. J Mol Graph Model 1997; 15(6): 359-363, 389. doi: 10.1016/S1093-3263(98)00002-3 PMID: 9704298
  93. Weisel M, Proschak E, Schneider G. PocketPicker: Analysis of ligand binding-sites with shape descriptors. Chem Cent J 2007; 1(1): 7. doi: 10.1186/1752-153X-1-7 PMID: 17880740
  94. Schelling M, Hopf TA, Rost B. Evolutionary couplings and sequence variation effect predict protein binding sites. Proteins 2018; 86(10): 1064-74. doi: 10.1002/prot.25585 PMID: 30020551
  95. Capra JA, Laskowski RA, Thornton JM, Singh M, Funkhouser TA. Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure. PLOS Comput Biol 2009; 5(12): e1000585. doi: 10.1371/journal.pcbi.1000585 PMID: 19997483
  96. Le Guilloux V, Schmidtke P, Tuffery P. Fpocket: An open source platform for ligand pocket detection. BMC Bioinformatics 2009; 10(1): 168. doi: 10.1186/1471-2105-10-168 PMID: 19486540
  97. Tian W, Chen C, Lei X, Zhao J, Liang J. CASTp 3.0: Computed atlas of surface topography of proteins. Nucleic Acids Res 2018; 46(W1): W363-7. doi: 10.1093/nar/gky473 PMID: 29860391
  98. Krivák R, Hoksza D. P2Rank: Machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure. J Cheminform 2018; 10(1): 39. doi: 10.1186/s13321-018-0285-8 PMID: 30109435
  99. Saberi Fathi S, Tuszynski JA. A simple method for finding a protein’s ligand-binding pockets. BMC Struct Biol 2014; 14(1): 18. doi: 10.1186/1472-6807-14-18 PMID: 25038637
  100. 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: 10.1093/bioinformatics/btx350 PMID: 28575181
  101. Pu L, Govindaraj RG, Lemoine JM, Wu HC, Brylinski M. DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network. PLOS Comput Biol 2019; 15(2): e1006718. doi: 10.1371/journal.pcbi.1006718 PMID: 30716081
  102. Ursenbach J, O’Connell ME, Neiser J, et al. Scoring algorithms for a computer-based cognitive screening tool: An illustrative example of overfitting machine learning approaches and the impact on estimates of classification accuracy. Psychol Assess 2019; 31(11): 1377-82. doi: 10.1037/pas0000764 PMID: 31414853
  103. Ragoza M, Hochuli J, Idrobo E, Sunseri J, Koes DR. Protein-ligand scoring with convolutional neural networks. J Chem Inf Model 2017; 57(4): 942-57. doi: 10.1021/acs.jcim.6b00740 PMID: 28368587
  104. Zhang H, Saravanan KM, Lin J, et al. DeepBindPoc: A deep learning method to rank ligand binding pockets using molecular vector representation. PeerJ 2020; 8: e8864. doi: 10.7717/peerj.8864 PMID: 32292649
  105. Zhang H, Zhang T, Saravanan KM, et al. DeepBindBC: A practical deep learning method for identifying native-like protein-ligand complexes in virtual screening. Methods 2022; 205: 247-62. doi: 10.1016/j.ymeth.2022.07.009 PMID: 35878751
  106. Feng Y, Cheng X, Wu S, Mani Saravanan K, Liu W. Hybrid drug-screening strategy identifies potential SARS-CoV-2 cell-entry inhibitors targeting human transmembrane serine protease. Struct Chem 2022; 33(5): 1503-15. doi: 10.1007/s11224-022-01960-w PMID: 35571866
  107. Jones D, Kim H, Zhang X, et al. Improved protein-ligand binding affinity prediction with structure-based deep fusion inference. J Chem Inf Model 2021; 61(4): 1583-92. doi: 10.1021/acs.jcim.0c01306 PMID: 33754707
  108. Alzubaidi L, Zhang J, Humaidi AJ, et al. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J Big Data 2021; 8(1): 53. doi: 10.1186/s40537-021-00444-8 PMID: 33816053
  109. Mamdouh Farghaly H, Abd El-Hafeez T. A high-quality feature selection method based on frequent and correlated items for text classification. Soft Comput 2023; 27(16): 11259-74. doi: 10.1007/s00500-023-08587-x
  110. Taye MM. Understanding of machine learning with deep learning: Architectures, workflow, applications and future directions. Computers 2023; 12(5): 91. doi: 10.3390/computers12050091
  111. Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK, Binding DB. BindingDB: A web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res 2007; 35(Database): D198-201. doi: 10.1093/nar/gkl999 PMID: 17145705
  112. Chai J, Zeng H, Li A, Ngai EWT. Deep learning in computer vision: A critical review of emerging techniques and application scenarios. Machine Learning Appl 2021; 6: 100134. doi: 10.1016/j.mlwa.2021.100134
  113. Li H, Tian S, Li Y, et al. Modern deep learning in bioinformatics. J Mol Cell Biol 2021; 12(11): 823-7. doi: 10.1093/jmcb/mjaa030 PMID: 32573721
  114. Reddy AS, Amarnath HSD, Bapi RS, Sastry GM, Sastry GN. Protein ligand interaction database (PLID). Comput Biol Chem 2008; 32(5): 387-90. doi: 10.1016/j.compbiolchem.2008.03.017 PMID: 18514578
  115. Stepniewska-Dziubinska MM, Zielenkiewicz P, Siedlecki P. Development and evaluation of a deep learning model for protein-ligand binding affinity prediction. Bioinformatics 2018; 34(21): 3666-74. doi: 10.1093/bioinformatics/bty374 PMID: 29757353
  116. Kanakala GC, Aggarwal R, Nayar D, Priyakumar UD. Latent biases in machine learning models for predicting binding affinities using popular data sets. ACS Omega 2023; 8(2): 2389-97. doi: 10.1021/acsomega.2c06781 PMID: 36687059
  117. Jiang X, Yan J, Zhao Y, et al. Characterizing functional brain networks via spatio-temporal attention 4D convolutional neural networks (STA-4DCNNs). Neural Netw 2023; 158: 99-110. doi: 10.1016/j.neunet.2022.11.004 PMID: 36446159
  118. Zhang H, Liao L, Saravanan KM, Yin P, Wei Y. DeepBindRG: A deep learning based method for estimating effective protein-ligand affinity. PeerJ 2019; 7: e7362. doi: 10.7717/peerj.7362 PMID: 31380152
  119. Wang S, Liu D, Ding M, et al. SE-OnionNet: A convolution neural network for protein-ligand binding affinity prediction. Front Genet 2021; 11: 607824. doi: 10.3389/fgene.2020.607824 PMID: 33737946
  120. Zhang H, Zhang T, Saravanan KM, et al. A novel virtual drug screening pipeline with deep-leaning as core component identifies inhibitor of pancreatic alpha-amylase Proceedings of the Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine BIBM 2021; 104-11. doi: 10.1109/BIBM52615.2021.9669306
  121. Kojima R, Ishida S, Ohta M, Iwata H, Honma T, Okuno Y. kGCN: A graph-based deep learning framework for chemical structures. J Cheminform 2020; 12(1): 32. doi: 10.1186/s13321-020-00435-6 PMID: 33430993
  122. Temml V, Kutil Z. Structure-based molecular modeling in SAR analysis and lead optimization. Comput Struct Biotechnol J 2021; 19: 1431-44. doi: 10.1016/j.csbj.2021.02.018 PMID: 33777339
  123. Rensi S, Altman RB. Flexible analog search with kernel PCA embedded molecule vectors. Comput Struct Biotechnol J 2017; 15: 320-7. doi: 10.1016/j.csbj.2017.03.003 PMID: 28458783
  124. Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database 2020; 2020: baaa010. doi: 10.1093/database/baaa010 PMID: 32185396
  125. Nguyen T, Le H, Quinn TP, Nguyen T, Le TD, Venkatesh S. GraphDTA: predicting drug-target binding affinity with graph neural networks. Bioinformatics 2021; 37(8): 1140-7. doi: 10.1093/bioinformatics/btaa921 PMID: 33119053
  126. Moesser MA, Klein D, Boyles F, Deane CM, Baxter A, Morris GM. Protein-ligand interaction graphs: Learning from ligand-shaped 3D interaction graphs to improve binding affinity prediction. BioRxiv 2022; 2022.03.04.483012. doi: 10.1101/2022.03.04.483012
  127. Zhang H, Saravanan KM, Zhang JZH. DeepBindGCN: Integrating molecular vector representation with graph convolutional neural networks for protein-ligand interaction prediction. Molecules 2023; 28(12): 4691. doi: 10.3390/molecules28124691
  128. Baranwal M, Magner A, Saldinger J, et al. Struct2Graph: A graph attention network for structure based predictions of protein-protein interactions. BMC Bioinformatics 2022; 23(1): 370. doi: 10.1186/s12859-022-04910-9 PMID: 36088285
  129. Wang R, Fang X, Lu Y, Yang CY, Wang S. The PDBbind database: Methodologies and updates. J Med Chem 2005; 48(12): 4111-9. doi: 10.1021/jm048957q PMID: 15943484
  130. Liu Z, Li Y, Han L, et al. PDB-wide collection of binding data: Current status of the PDBbind database. Bioinformatics 2015; 31(3): 405-12. doi: 10.1093/bioinformatics/btu626 PMID: 25301850
  131. Yang C, Chen EA, Zhang Y. Protein-ligand docking in the machine-learning era. Molecules 2022; 27(14): 4568. doi: 10.3390/molecules27144568 PMID: 35889440
  132. Mysinger MM, Carchia M, Irwin JJ, Shoichet BK. Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking. J Med Chem 2012; 55(14): 6582-94. doi: 10.1021/jm300687e PMID: 22716043

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2024 Bentham Science Publishers