abstract

Artificial Intelligence for Drug Discovery

Authors:

Feixiong ChengAuthors Info & Claims

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Pages 4074 - 4075

https://doi.org/10.1145/3447548.3470796

Published: 14 August 2021 Publication History

Abstract

Drug discovery is a long and costly process, taking on average 10 years and 2.5 billion dollars to develop a new drug. Artificial intelligence has the potential to significantly accelerate the process of drug discovery by analyzing a large amount of data generated in the biomedical domain such as bioassays, chemical experiments, and biomedical literature. Recently, there is a growing interesting in developing AI techniques for drug discovery in many different communities including machine learning, data mining, and biomedical community. In this tutorial, we will provide a detailed introduction to key problems in drug discovery such as molecular property prediction, de novo molecular design and molecular optimization, retrosynthesis reaction and prediction, and drug repurposing and combination, and also key technique advancements with artificial intelligence for these problems. This tutorial can be served as introduction materials for both computer scientist interested in drug discovery as well as drug discovery practitioners for learning the latest AI techniques along this direction.

References

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Feixiong Cheng, Rishi J Desai, Diane E Handy, Ruisheng Wang, Sebastian Schneeweiss, Alberto-László Barabási, and Joseph Loscalzo. 2018. Networkbased approach to prediction and population-based validation of in silico drug repurposing. Nature communications 9, 1 (2018), 1--12.

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Hanjun Dai, Chengtao Li, Connor Coley, Bo Dai, and Le Song. 2019. Retrosynthesis prediction with conditional graph logic network. In Advances in Neural Information Processing Systems. 8872--8882.

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Deisy Morselli Gysi, Ítalo Do Valle, Marinka Zitnik, Asher Ameli, Xiao Gan, Onur Varol, Helia Sanchez, Rebecca Marlene Baron, Dina Ghiassian, Joseph Loscalzo, et al. 2020. Network medicine framework for identifying drug repurposing opportunities for covid-19. arXiv preprint arXiv:2004.07229 (2020).

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Miko?aj Sacha, Miko?aj B?a?, Piotr Byrski, Pawe? W?odarczyk-Pruszy'ski, and Stanis?aw Jastrz?bski. 2020. Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits. arXiv preprint arXiv:2006.15426 (2020).

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Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, and Jian Tang. 2020. A Graph to Graphs Framework for Retrosynthesis Prediction. ICML (2020).

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Digital Library

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Yadi Zhou, Yuan Hou, Jiayu Shen, Asha Kallianpur, Joe Zein, Daniel A Culver, Samar Farha, Suzy Comhair, Claudio Fiocchi, Michaela U Gack, et al. 2020. A Network Medicine Approach to Investigation and Population-based Validation of Disease Manifestations and Drug Repurposing for COVID-19. ChemRxiv (2020).

Cited By

Zhang YWei ZHuang WLi C(2024)HierAffinity: Predicting Protein-Ligand Binding Affinity With Hierarchical ModelingDatabase Systems for Advanced Applications10.1007/978-981-97-5575-2_3(37-52)Online publication date: 2-Sep-2024
https://doi.org/10.1007/978-981-97-5575-2_3
Zhang YZhou GWei ZXu H(2022)Predicting Protein-Ligand Binding Affinity via Joint Global-Local Interaction Modeling2022 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM54844.2022.00175(1323-1328)Online publication date: Nov-2022
https://doi.org/10.1109/ICDM54844.2022.00175

Index Terms

Artificial Intelligence for Drug Discovery
1. Information systems
  1. Information systems applications
    1. Data mining

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Information

Published In

cover image ACM Conferences

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

August 2021

4259 pages

ISBN:9781450383325

DOI:10.1145/3447548

General Chairs:
Feida Zhu
Singapore Management University
,
Beng Chin Ooi
National University of Singapore
,
Chunyan Miao
Nanyang Technology University
,
Program Chairs:
Haixun Wang,
Iryna Skrypnyk,
Wynne Hsu,
Sanjay Chawla

Copyright © 2021 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 August 2021

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Funding Sources

Mila-Samsung Research Contract
NSERC
CIFAR AI Research Chair

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KDD '21

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KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 14 - 18, 2021

Virtual Event, Singapore

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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KDD '25

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sigkdd
sigkdd

The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

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Cited By

Zhang YWei ZHuang WLi C(2024)HierAffinity: Predicting Protein-Ligand Binding Affinity With Hierarchical ModelingDatabase Systems for Advanced Applications10.1007/978-981-97-5575-2_3(37-52)Online publication date: 2-Sep-2024
https://doi.org/10.1007/978-981-97-5575-2_3
Zhang YZhou GWei ZXu H(2022)Predicting Protein-Ligand Binding Affinity via Joint Global-Local Interaction Modeling2022 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM54844.2022.00175(1323-1328)Online publication date: Nov-2022
https://doi.org/10.1109/ICDM54844.2022.00175

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