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【導讀】國際人工智能會議AAAI 2021論文將在全程線上舉辦，時間在 2 月 2 日-2 月 9 日，本屆大會也將是第 35 屆 AAAI 大會。大會涵蓋了眾多最近研究進展報告，來自MIla唐建博士、FeiWang博士、Feixiong Cheng共同做了關于人工智能藥物發現的進展報告，非常值得關注!

藥物發現是一個漫長而昂貴的過程，平均需要10年時間和25億美元來開發一種新藥。人工智能有潛力通過分析生物醫學領域產生的大量數據，如生物測定、化學實驗和生物醫學文獻，來顯著加速藥物發現的進程。最近，在許多不同的領域，包括機器學習、數據挖掘和生物醫學領域，人們對開發人工智能技術用于藥物發現越來越感興趣。在本教程中，我們將詳細介紹藥物發現中的關鍵問題，如分子性質預測、新生分子設計和分子優化、反合成反應和預測、藥物再利用和組合，以及針對這些問題的人工智能關鍵技術進展。本教程可以作為對藥物發現感興趣的計算機科學家和藥物發現從業者的入門材料，以便沿著這個方向學習最新的人工智能技術。

AAAI 2021：//deepgraphlearning.github.io/DrugTutorial_AAAI2021/ KDD 2021：

目錄：

• 藥物概述 Drug Discovery Overview [20 min, presented by Feixiong]

• 深度學習，傳統基于網絡方法，圖表示學習 Deep Learning, Traditional Network-based Methods, Graph Representation Learning [15 min, presented by Fei]

  • 卷積神經網絡與循環神經網路絡 Convolutional Neural Networks and Recurrent Neural Networks
  • 圖卷積網絡 Graph Convolutional Networks [GCN (Kipf & Welling, 2016), MPNN (Gilmer et al., 2017), GIN (Xu et al., 2018)]

• 分子性質預測 Molecule Properties Prediction [30 min, presented by Jian]

  • Supervised [MPNN (Gilmer et al., 2017)]
  • Self-supervised [ContextPred (Hu et al., 2019), InfoGraph (Sun et al., 2020)]
  • Semi-supervised [InfoGraph (Sun et al., 2020)]

• 新生分子的生成和優化，De novo Molecule Generation and Optimization [40 min, presented by Jian and Fei]

  • Variational autoencoder-based approach [JTVAE (Jin et al., 2018)]
  • Autoregressive methods [GCPN (You et al., 2018)]
  • Normalizing Flow-based approaches [GraphAF (Shi et al., 2020), MoFlow (Zang & Wang, 2020)]

• 反應預測與反合成，Reaction Prediction and Retrosynthesis [30 min, presented by Jian]

• Reaction prediction [(Jin et al., 2017), (Schwaller et al., 2019), (Sacha et al., 2020)]

• Retrosynthesis [(Dai et al., 2019), (Shi et al., 2020), (Sacha et al., 2020)]

• 基于多組學和臨床數據的藥物再利用，Multiomics and Clinical Data-based Drug Repurposing [45 min, presented by Feixiong and Fei]

• Network-based approach [(Cheng et al., 2018)]

• Graph Neural Network-based approach [(Gysi et al., 2020) ]

• Case Study on COVID-19 [(Zhou et al., 2020), (Gysi et al., 2020)]

• 其他話題，Other Topics [15 min, presented by Fei]

• 結論與未來方向，Conclusion and Future Directions [15 min, presented by Fei]

講者：

唐建，加拿大蒙特利爾學習算法研究院 (MILA) 以及加拿大蒙特利爾大學商學院助理教授、博士生導師。主要研究方向為：圖表示學習、圖研究網絡、知識圖譜、藥物發現。曾獲得機器學習頂級會議ICML2014的最佳論文以及數據挖掘頂級會議WWW‘16的最佳論文提名。發表了一系列在圖表示學習領域的經典論文包括LINE、LargeVis、RotatE以及最近的Graph Markov Neural Networks (GMNN)。

Fei Wang，美國康奈爾大學威爾醫學院副教授，主要研究方向為數據挖掘及其在健康數據科學中的應用。他在人工智能和醫學領域發表了250多篇論文，被引用超過1.27萬次，H指數是56。他的論文在數據挖掘和醫學信息學的頂級國際會議上獲得了8項最佳論文獎。

Feixiong Cheng博士，克利夫蘭診所基因組醫學研究所的首席研究員。鄭博士致力于開發計算和實驗網絡醫學技術，以促進疾病異質性的表征，從而接近協調的、以患者為中心的戰略的目標，以創新診斷和治療的發展。

參考文獻：

Sun, F.-Y., Hoffmann, J., Verma, V., & Tang, J. (2020). Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. ICLR.

Shi, C., Xu, M., Zhu, Z., Zhang, W., Zhang, M., & Tang, J. (2020). GraphAF: a flow-based autoregressive model for molecular graph generation. ICLR.

Shi, C., Xu, M., Guo, H., Zhang, M., & Tang, J. (2020). A Graph to Graphs Framework for Retrosynthesis Prediction. ICML.

Gottipati, S. K., Sattarov, B., Niu, S., Pathak, Y., Wei, H., Liu, S., Thomas, K. M. J., Blackburn, S., Coley, C. W., Tang, J., & others. (2020). Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning. ICML.

Jin, W., Barzilay, R., & Jaakkola, T. (2018). Junction tree variational autoencoder for molecular graph generation. ICML.

You, J., Liu, B., Ying, Z., Pande, V., & Leskovec, J. (2018). Graph convolutional policy network for goal-directed molecular graph generation. Advances in Neural Information Processing Systems, 6410–6421.

Zang, C., & Wang, F. (2020). MoFlow: An Invertible Flow Model for Generating Molecular Graphs. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 617–626.

Sun, M., Zhao, S., Gilvary, C., Elemento, O., Zhou, J., & Wang, F. (2020). Graph convolutional networks for computational drug development and discovery. Briefings in Bioinformatics, 21(3), 919–935.

Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., & Leskovec, J. (2019). Strategies for Pre-training Graph Neural Networks. ArXiv Preprint ArXiv:1905.12265.

Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017). Neural message passing for quantum chemistry. ArXiv Preprint ArXiv:1704.01212.

Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. ArXiv Preprint ArXiv:1609.02907.

Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2018). How powerful are graph neural networks? ArXiv Preprint ArXiv:1810.00826.

Jin, W., Coley, C., Barzilay, R., & Jaakkola, T. (2017). Predicting organic reaction outcomes with weisfeiler-lehman network. Advances in Neural Information Processing Systems, 2607–2616.

Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C. A., Bekas, C., & Lee, A. A. (2019). Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS Central Science, 5(9), 1572–1583.

Sacha, M., B?a?, M., Byrski, P., W?odarczyk-Pruszyński, P., & Jastrz?bski, S. (2020). Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits. ArXiv Preprint ArXiv:2006.15426.

Dai, H., Li, C., Coley, C., Dai, B., & Song, L. (2019). Retrosynthesis prediction with conditional graph logic network. Advances in Neural Information Processing Systems, 8872–8882.

Zhou, Y., Hou, Y., Shen, J., Huang, Y., Martin, W., & Cheng, F. (2020). Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2. Cell Discovery, 6(1), 1–18.

Zeng, X., Zhu, S., Liu, X., Zhou, Y., Nussinov, R., & Cheng, F. (2019). deepDR: a network-based deep learning approach to in silico drug repositioning. Bioinformatics, 35(24), 5191–5198.

Zhou, Y., Wang, F., Jian, T., R., N., & Cheng, F. (2020). Artificial Intelligence in Drug Repurposing. The Lancet Digital Health.

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Gysi, D. M., Valle, í. D., Zitnik, M., Ameli, A., Gan, X., Varol, O., Sanchez, H., Baron, R. M., Ghiassian, D., Loscalzo, J., & others. (2020). Network medicine framework for identifying drug repurposing opportunities for covid-19. ArXiv Preprint ArXiv:2004.07229.

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