1 |
李帅.AI赋能药物研发背后的逻辑 [EB/OL]. [2023-05-14]. .
|
|
LI S. Logic behind AI enabling drug development [EB/OL]. [2023-05-14]. .
|
2 |
马璟. 早期毒性优化筛选在药物候选决策中的作用[C]// 中国毒理学会第六届全国毒理学大会论文集. 北京:中国毒理学会,2013:419.
|
|
MA J. The role of early toxicity optimization screening in drug candidate decision-making [C]// Proceedings of the 6th National Toxicology Conference of the Chinese Society of Toxicology. Beijing: Chinese Society of Toxicology, 2013: 419.
|
3 |
YANG K, SWANSON K, JIN W, et al. Analyzing learned molecular representations for property prediction [J]. Journal of Chemical Information and Modeling, 2019, 59(8): 3370-3388.
|
4 |
SHEN W X, ZENG X, ZHU F, et al. Out-of-the-box deep learning prediction of pharmaceutical properties by broadly learned knowledge-based molecular representations [J]. Nature Machine Intelligence, 2021, 3(4): 334-343.
|
5 |
KEARNES S, McCLOSKEY K, BERNDL M, et al. Molecular graph convolutions: moving beyond fingerprints [J]. Journal of Computer-Aided Molecular Design, 2016, 30(8): 595-608.
|
6 |
LI J, CAI D, HE X. Learning graph-level representation for drug discovery [EB/OL]. [2023-02-13]. .
|
7 |
WANG F, YANG J-F, WANG M-Y, et al. Graph attention convolutional neural network model for chemical poisoning of honey bees’ prediction [J]. Science Bulletin, 2020, 65(14): 1184-1191.
|
8 |
YUAN K, GUO S, LIU Z, et al. Incorporating convolution designs into visual Transformers [C]// Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2021: 559-568.
|
9 |
ZHANG Z, LIU Q, WANG H, et al. Motif-based graph self-supervised learning for molecular property prediction [EB/OL]. [2023-02-13]. .
|
10 |
GUO Z, ZHANG C, YU W, et al. Few-shot graph learning for molecular property prediction [C]// Proceedings of the Web Conference 2021. New York: ACM, 2021: 2559-2567.
|
11 |
WANG Y, ABUDUWEILI A, YAO Q, et al. Property-aware relation networks for few-shot molecular property prediction [EB/OL]. [2023-04-14]. .
|
12 |
XU K, HU W, LESKOVEC J, et al. How powerful are graph neural networks? [EB/OL]. [2023-05-19]. .
|
13 |
HAMILTON W L, YING R, LESKOVEC J. Inductive representation learning on large graphs [C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2017: 1025-1035.
|
14 |
DEVLIN J, CHANG M W, LEE K, et al. BERT: pre-training of deep bidirectional Transformers for language understanding [EB/OL]. [2022-12-03]. .
|
15 |
HU W, LIU B, GOMES J, et al. Strategies for pre-training graph neural networks [EB/OL]. [2023-09-13]. .
|
16 |
TORRES L H M, RIBEIRO B, ARRAIS J P. Few-shot learning with Transformers via graph embeddings for molecular property prediction [J]. Expert Systems with Applications, 2023, 225: 120005.
|
17 |
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. Red Hook: Curran Associates Inc., 2017: 6000-6010.
|
18 |
HENDRYCKS D, GIMPEL K. Bridging nonlinearities and stochastic regularizers with Gaussian error linear units [EB/OL]. [2023-03-14]. .
|
19 |
FINN C, ABBEEL P, LEVINE S. Model-agnostic meta-learning for fast adaptation of deep networks [C]// Proceedings of the 34th International Conference on Machine Learning. New York: JMLR.org, 2017: 1126-1135.
|
20 |
HOSPEDALES T, ANTONIOU A, MICAELLI P, et al. Meta-learning in neural networks: a survey [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(9): 5149-5169.
|
21 |
YAO H, LIU Y, WEI Y, et al. Learning from multiple cities: a meta-learning approach for spatial-temporal prediction [C]// Proceedings of the 2019 World Wide Web Conference. New York: ACM, 2019: 2181-2191.
|
22 |
HUANG R, XIA M, D-T NGUYEN, et al. Tox21 Challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs [J]. Frontiers in Environmental Science, 2016, 3: 85.
|
23 |
KUHN M, LETUNIC I, JENSEN L J, et al. The SIDER database of drugs and side effects [J]. Nucleic Acids Research, 2016, 44(D1): D1075-D1079.
|