Bioscience Method 2024, Vol.15, No.1, 28-36 http://bioscipublisher.com/index.php/bm 35 Cai C., Wang S., Xu Y., Zhang W., Tang K., Qi O., Lai L., and Pei J., 2020, Transfer learning for drug discovery, Journal of Medicinal Chemistry, 63(16): 8683-8694. https://doi.org/10.1021/acs.jmedchem.9b02147 PMid:32672961 Chen D., Zheng J., Wei G., and Pan F., 2021, Extracting predictive representations from hundreds of millions of molecules, The Journal of Physical Chemistry Letters, 12(44): 10793-10801. https://doi.org/10.1021/acs.jpclett.1c03058 PMid:34723543 PMCid:PMC9358546 Öztürk H., Özgür A., and Ozkirimli E., 2018, DeepDTA: deep drug-target binding affinity prediction, Bioinformatics, 34(17): i821-i829. https://doi.org/10.1093/bioinformatics/bty593 PMid:30423097 PMCid:PMC6129291 Hentabli H., Bengherbia B., Saeed F., Salim N., Nafea I., Toubal A., and Nasser M., 2022, Convolutional Neural Network Model Based on 2D Fingerprint for Bioactivity Prediction, International Journal of Molecular Sciences, 23(21): 13230. https://doi.org/10.3390/ijms232113230 PMid:36362018 PMCid:PMC9657591 Jiang J., Wang R., and Wei G., 2021, GGL-Tox: Geometric Graph Learning for Toxicity Prediction, Journal of Chemical Information and Modeling, 61(4): 1691-1700. https://doi.org/10.1021/acs.jcim.0c01294 PMid:33719422 PMCid:PMC8155789 Jimenez-Carretero D., Abrishami V., Fernandez-de-Manuel L., Palacios I., Quílez-Álvarez A., Díez-Sánchez A., Pozo M., and Montoya M., 2018, Tox_(R)CNN: Deep learning-based nuclei profiling tool for drug toxicity screening, PLoS Computational Biology, 14(11): e1006238. https://doi.org/10.1371/journal.pcbi.1006238 PMid:30500821 PMCid:PMC6291153 Jiménez-Luna J., Grisoni F., and Schneider G., 2020, Drug discovery with explainable artificial intelligence, Nat. Mach. Intell., (2): 573-584. https://doi.org/10.1038/s42256-020-00236-4 Jones D., Kim H., Zhang X., Zemla A., Stevenson G., Bennett W., Kirshner D., Wong S., Lightstone F., and Allen J., 2020, Improved Protein-ligand binding affinity prediction with structure-based deep fusion inference, Journal of Chemical Information and Modeling, 61(4): 1583-1592. https://doi.org/10.1021/acs.jcim.0c01306 PMid:33754707 Kadurin A., Nikolenko S., Khrabrov K., Aliper A., and Zhavoronkov A., 2017, druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico, Molecular Pharmaceutics, 14(9): 3098-3104. https://doi.org/10.1021/acs.molpharmaceut.7b00346 PMid:28703000 Karimic M., Wu D., Wang Z.Y., and Shen Y., 2018, DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks, bioRxiv, 35(18): 3329-3338. https://doi.org/10.1093/bioinformatics/btz111 PMid:30768156 PMCid:PMC6748780 Kayama K., Kanno M., Chisaki N., Tanaka M., Yao R., Hanazono K., Camer G., and Endoh D., 2021, Prediction of PCR amplification from primer and template sequences using recurrent neural network, Scientific Reports, 11: 7493. https://doi.org/10.1038/s41598-021-86357-1 PMid:33820936 PMCid:PMC8021588 Li X.H., Xiong H.Y., Li X.J., Wu X.Y., Zhang X., Liu J., Bian J., and Dou D.J., 2021, Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond, Knowledge and Information Systems, 64: 3197-3234. https://doi.org/10.1007/s10115-022-01756-8 Liu J., Yang Y.H., Lv S., Wang J., and Chen H., 2019, Attention-based BiGRU-CNN for Chinese question classification, Journal of Ambient Intelligence and Humanized Computing, 10:1-12. https://doi.org/10.1007/s12652-019-01344-9 Low K., Coote M., and Izgorodina E., 2022, Explainable Solvation Free Energy Prediction Combining Graph Neural Networks with Chemical Intuition, Journal of Chemical Information and Modeling, 62(22): 5457-5470. https://doi.org/10.1021/acs.jcim.2c01013 PMid:36317829 Mayr A., Klambauer G., Unterthiner T., and Hochreiter S., 2016, DeepTox: Toxicity Prediction using Deep Learning, Frontiers in Environmental Science, 3(8): 1-8. https://doi.org/10.3389/fenvs.2015.00080 Noe M., and Peakman M., 2017, Drug Discovery Technologies, Current and Future Trends, 2: 1-32. https://doi.org/10.1016/B978-0-12-409547-2.12312-1 PMid:28289437 PMCid:PMC5310094
RkJQdWJsaXNoZXIy MjQ4ODYzMg==