BM_2024v15n1

Bioscience Method 2024, Vol.15, No.1, 28-36 http://bioscipublisher.com/index.php/bm 36 Popova M., Isayev O., and Tropsha A., 2017, Deep reinforcement learning for de novo drug design, Science Advances, 4(7): e7885. https://doi.org/10.1126/sciadv.aap7885 PMid:30050984 PMCid:PMC6059760 Schneider G., 2017, Automating drug discovery, Nature Reviews Drug Discovery, 17: 97-113. https://doi.org/10.1038/nrd.2017.232 PMid:29242609 Tang B., Kramer S., Fang M., Qiu Y., Wu Z., and Xu D., 2020, A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility, Journal of Cheminformatics, 12: 15. https://doi.org/10.1186/s13321-020-0414-z PMid:33431047 PMCid:PMC7035778 Tran T., Tayara H., and Chong K., 2023, Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives, Pharmaceutics, 15(4): 1260. https://doi.org/10.3390/pharmaceutics15041260 PMid:37111744 PMCid:PMC10143484 Walters W.P., and Barzilay R., 2021, Applications of Deep Learning in Molecule Generation and Molecular Property Prediction, Acc. Chem. Res.,54(2): 263-270. https://doi.org/10.1021/acs.accounts.0c00699 PMid:33370107 Wang Z., Mi J., Lu S., and He J., 2023, MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures, ArXiv, abs/2311.16666. Wieder O., Kuenemann M., Wieder M., Seidel T., Meyer C., Bryant S., and Langer T., 2021, Improved Lipophilicity and Aqueous Solubility Prediction with Composite Graph Neural Networks, Molecules, 26(20): 6185. https://doi.org/10.3390/molecules26206185 PMid:34684766 PMCid:PMC8539502 Xiong J., Xiong Z., Chen K., Jiang H., and Zheng M., 2021, Graph neural networks for automated de novo drug design, Drug Discovery Today, 26(6): 1382-1393. https://doi.org/10.1016/j.drudis.2021.02.011 PMid:33609779 Yaseen B., 2023, Drug Target Interaction Prediction Using Convolutional Neural Network (CNN), 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 2(2): 1-5. https://doi.org/10.1109/HORA58378.2023.10156717 Yu H., and Welch J., 2021, MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks, Genome Biology, 22: 158. https://doi.org/10.1186/s13059-021-02373-4 PMid:34016135 PMCid:PMC8139054 Zeng X., Xiang H., Yu L., Wang J., Li K., Nussinov R., and Cheng F., 2022, Accurate prediction of molecular targets using a self-supervised image representation learning framework, Research Square, 4: 1004-1016. https://doi.org/10.1038/s42256-022-00557-6 Zhang Q., Shen Z., and Huang D., 2019, Predicting in-vitro transcription factor binding sites using DNA Sequence+Shape, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(2): 667-676. https://doi.org/10.1109/TCBB.2019.2947461 PMid:31634140

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