| [1] |
Gupta R, Srivastava D, Sahu M, et al. Artificial intelligence to deep learning: machine intelligence approach for drug discovery[J]. Mol Divers, 2021, 25(3): 1315-1360.
|
| [2] |
Jayatunga MKP, Xie W, Ruder L, et al. AI in small-molecule drug discovery: a coming wave[J]? Nat Rev Drug Discov, 2022, 21(3): 175-176.
|
| [3] |
Koromina M, Pandi MT, Patrinos GP. Rethinking drug repositioning and development with artificial intelligence, machine learning, and omics[J]. OMICS, 2019, 23(11): 539-548.
|
| [4] |
Yang X, Wang YF, Byrne R, et al. Concepts of artificial intelligence for computer-assisted drug discovery[J]. Chem Rev, 2019, 119(18): 10520-10594.
|
| [5] |
Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators[J]. Neural Netw, 1989, 2(5): 359-366.
|
| [6] |
LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
|
| [7] |
Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives[J]. IEEE Trans Pattern Anal Mach Intell, 2013, 35(8): 1798-1828.
|
| [8] |
Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks[J]. Commun ACM, 2020, 63(11): 139-144.
|
| [9] |
?ztürk H, ?zgür A, Schwaller P, et al. Exploring chemical space using natural language processing methodologies for drug discovery[J]. Drug Discov Today, 2020, 25(4): 689-705.
|
| [10] |
Kriegeskorte N, Golan T. Neural network models and deep learning[J]. Curr Biol, 2019, 29(7): R231-R236.
|
| [11] |
Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. New York: ACM, 2017: 6000-6010.
|
| [12] |
Bagal V, Aggarwal R, Vinod PK, et al. MolGPT: molecular generation using a transformer-decoder model[J]. J Chem Inf Model, 2022, 62(9): 2064-2076.
|
| [13] |
Hu J, Gao J, Fang XM, et al. DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations[J]. Brief Bioinform, 2022, 23(5): bbac302.
|
| [14] |
Zaikis D, Vlahavas I. TP-DDI: transformer-based pipeline for the extraction of drug-drug interactions[J]. Artif Intell Med, 2021, 119: 102153.
|
| [15] |
Jiang LK, Jiang CZ, Yu XY, et al. DeepTTA: a transformer-based model for predicting cancer drug response[J]. Brief Bioinform, 2022, 23(3): bbac100.
|
| [16] |
Zhang ZH, Chen LF, Zhong FS, et al. Graph neural network approaches for drug-target interactions[J]. Curr Opin Struct Biol, 2022, 73: 102327.
|
| [17] |
Yang ZD, Zhong WH, Zhao L, et al. MGraphDTA: deep multiscale graph neural network for explainable drug-target binding affinity prediction[J]. Chem Sci, 2022, 13(3): 816-833.
|
| [18] |
Jorgensen WL. Challenges for academic drug discovery[J]. Angew Chem Int Ed Engl, 2012, 51(47): 11680-11684.
|
| [19] |
Yan JL, Bhadra P, Li A, et al. Deep-AmPEP30: improve short antimicrobial peptides prediction with deep learning[J]. Mol Ther Nucleic Acids, 2020, 20: 882-894.
|
| [20] |
Zhavoronkov A, Ivanenkov YA, Aliper A, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors[J]. Nat Biotechnol, 2019, 37(9): 1038-1040.
|
| [21] |
Yoshimori A, Bajorath J. Deep SAR matrix: SAR matrix expansion for advanced analog design using deep learning architectures[J]. Future Drug Discov, 2020. doi:10.4155/fdd-2020-0005.
|
| [22] |
Yoshimori A, Miljkovi? F, Bajorath J. Approach for the design of covalent protein kinase inhibitors via focused deep generative modeling[J]. Molecules, 2022, 27(2): 570.
|
| [23] |
Kennedy K, Cal R, Casey R, et al. The anti-ageing effects of a natural peptide discovered by artificial intelligence[J]. Int J Cosmet Sci, 2020, 42(4): 388-398.
|
| [24] |
Rein D, Ternes P, Demin R, et al. Artificial intelligence identified peptides modulate inflammation in healthy adults[J]. Food Funct, 2019, 10(9): 6030-6041.
|
| [25] |
Casey R, Adelfio A, Connolly M, et al. Discovery through machine learning and preclinical validation of novel anti-diabetic peptides[J]. Biomedicines, 2021, 9(3): 276.
|
| [26] |
Al-Khdhairawi A, Sanuri D, Akbar R, et al. Machine learning and molecular simulation ascertain antimicrobial peptide against Klebsiella pneumoniae from public database[J]. Comput Biol Chem, 2023, 102: 107800.
|
| [27] |
Yuan QT, Chen KY, Yu YM, et al. Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding[J]. Brief Bioinform, 2023, 24(1): bbac630.
|
| [28] |
Huang JJ, Xu YC, Xue YF, et al. Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences[J]. Nat Biomed Eng, 2023.doi:10.1038/s41551-022-00991-2.
|
| [29] |
Krishnan K, Kassab R, Agajanian S, et al. Interpretable machine learning models for molecular design of tyrosine kinase inhibitors using variational autoencoders and perturbation-based approach of chemical space exploration[J]. Int J Mol Sci, 2022, 23(19): 11262.
|
| [30] |
Kleandrova VV, Scotti MT, Scotti L, et al. Multi-target drug discovery via PTML modeling: applications to the design of virtual dual inhibitors of CDK4 and HER2[J]. Curr Top Med Chem, 2021, 21(7): 661-675.
|
| [31] |
Xing GM, Liang L, Deng CL, et al. Activity prediction of small molecule inhibitors for antirheumatoid arthritis targets based on artificial intelligence[J]. ACS Comb Sci, 2020, 22(12): 873-886.
|
| [32] |
Lien ST, Lin TE, Hsieh JH, et al. Establishment of extensive artificial intelligence models for kinase inhibitor prediction: identification of novel PDGFRB inhibitors[J]. Comput Biol Med, 2023, 156: 106722.
|
| [33] |
Goh GB, Hodas NO, Vishnu A. Deep learning for computational chemistry[J]. J Comput Chem, 2017, 38(16): 1291-1307.
|
| [34] |
Arul Murugan N, Ruba Priya G, Narahari Sastry G, et al. Artificial intelligence in virtual screening: models versus experiments[J]. Drug Discov Today, 2022, 27(7): 1913-1923.
|
| [35] |
Serafim MSM, Kronenberger T, Oliveira PR, et al. The application of machine learning techniques to innovative antibacterial discovery and development[J]. Expert Opin Drug Discov, 2020, 15(10): 1165-1180.
|
| [36] |
Arcon JP, Modenutti CP, Avenda?o D, et al. AutoDock Bias: improving binding mode prediction and virtual screening using known protein-ligand interactions[J]. Bioinformatics, 2019, 35(19): 3836-3838.
|
| [37] |
Arcon JP, Defelipe LA, Lopez ED, et al. Cosolvent-based protein pharmacophore for ligand enrichment in virtual screening[J]. J Chem Inf Model, 2019, 59(8): 3572-3583.
|
| [38] |
Zhang HP, Liao LB, Cai YT, et al. IVS2vec: a tool of Inverse Virtual Screening based on word2vec and deep learning techniques[J]. Methods, 2019, 166: 57-65.
|
| [39] |
Gong JN, Zhao L, Chen GX, et al. A novel artificial intelligence protocol to investigate potential leads for diabetes mellitus[J]. Mol Divers, 2021, 25(3): 1375-1393.
|
| [40] |
Beck BR, Shin B, Choi Y, et al. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model[J]. Comput Struct Biotechnol J, 2020, 18: 784-790.
|
| [41] |
Lamontagne F, Agarwal A, Rochwerg B, et al. A living WHO guideline on drugs for covid-19[J]. BMJ, 2020, 370: m3379.
|
| [42] |
Roessler HI, Knoers NVAM, van Haelst MM, et al. Drug repurposing for rare diseases[J]. Trends Pharmacol Sci, 2021, 42(4): 255-267.
|
| [43] |
Brasil S, Allocca M, Magrinho SCM, et al. Systematic review: drug repositioning for congenital disorders of glycosylation (CDG)[J]. Int J Mol Sci, 2022, 23(15): 8725.
|
| [44] |
Ancuceanu R, Hovanet MV, Anghel AI, et al. Computational models using multiple machine learning algorithms for predicting drug hepatotoxicity with the DILIrank dataset[J]. Int J Mol Sci, 2020, 21(6): 2114.
|
| [45] |
Chen MM, Yang ZY, Gao YX, et al. Fast identification of adverse drug reactions (ADRs) of digestive and nervous systems of organic drugs by in silico models[J]. Molecules, 2021, 26(4): 930.
|
| [46] |
Gong XJ, Hu M, Liu JZ, et al. Decoding kinase-adverse event associations for small molecule kinase inhibitors[J]. Nat Commun, 2022, 13(1): 4349.
|
| [47] |
Nguyen M, Long SW, McDermott PF, et al. Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella[J]. J Clin Microbiol, 2019, 57(2): e01260-e01218.
|
| [48] |
Weis C, Cuénod A, Rieck B, et al. Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning[J]. Nat Med, 2022, 28(1): 164-174.
|
| [49] |
Yang ZY, Ye ZF, Xiao YJ, et al. SPLDExtraTrees: robust machine learning approach for predicting kinase inhibitor resistance[J]. Brief Bioinform, 2022, 23(3): bbac050.
|
| [50] |
Sui QH, Chen ZC, Hu ZY, et al. Cisplatin resistance-related multi-omics differences and the establishment of machine learning models[J]. J Transl Med, 2022, 20(1): 171.
|
| [51] |
Yu T, Fu Y, He JT, et al. Identification of antibiotic resistance in ESKAPE pathogens through plasmonic nanosensors and machine learning[J]. ACS Nano, 2023, 17(5): 4551-4563.
|
| [52] |
Chandrasekaran S, Cokol-Cakmak M, Sahin N, et al. Chemogenomics and orthology-based design of antibiotic combination therapies[J]. Mol Syst Biol, 2016, 12(5): 872.
|
| [53] |
Ma SY, Jaipalli S, Larkins-Ford J, et al. Transcriptomic signatures predict regulators of drug synergy and clinical regimen efficacy against tuberculosis[J]. mBio, 2019, 10(6): e02627-e02619.
|
| [54] |
Li XB, Dowling EK, Yan GH, et al. Precision combination therapies based on recurrent oncogenic coalterations[J]. Cancer Discov, 2022, 12(6): 1542-1559.
|
| [55] |
Zhang TY, Zhang LW, Payne PRO, et al. Synergistic drug combination prediction by integrating multiomics data in deep learning models[J]. Methods Mol Biol, 2021, 2194: 223-238.
|
| [56] |
Bate A. Bayesian confidence propagation neural network[J]. Drug Saf, 2007, 30(7): 623-625.
|
| [57] |
Wu ZX, Zhu MF, Kang Y, et al. Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets[J]. Brief Bioinform, 2021, 22(4): bbaa321.
|
| [58] |
Lugagne JB, Dunlop MJ. Anticipating antibiotic resistance[J]. Science, 2022, 375(6583): 818-819.
|
| [59] |
Li S, Zhang B. Traditional Chinese medicine network pharmacology: theory, methodology and application[J]. Chin J Nat Med, 2013, 11(2): 110-120.
|
| [60] |
Ren SJ, Wu SL, Weng QH. Physics-informed machine learning methods for biomass gasification modeling by considering monotonic relationships[J]. Bioresour Technol, 2023, 369: 128472.
|
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