Login Register Cart 0
Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

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

Author(s): Haiping Zhang and Konda Mani Saravanan*

Volume 19, Issue 10, 2024

Published on: 29 January, 2024

Page: [891 - 907] Pages: 17

DOI: 10.2174/0115748936285690240101041704

Price: $65

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.

Next »
[1]
Zhu H. Big data and artificial intelligence modeling for drug discovery. Annu Rev Pharmacol Toxicol 2020; 60(1): 573-89.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/10.3390/app9081526]
[35]
Baldi P. Deep learning in biomedical data science. Annu Rev Biomed Data Sci 2018; 1(1): 181-205.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/10.1002/prot.26535] [PMID: 37283068]
[87]
Sadybekov AV, Katritch V. Computational approaches streamlining drug discovery. Nature 2023; 616(7958): 673-85.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/10.1021/jm300687e] [PMID: 22716043]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy