A molecular video-derived foundation model for scientific drug discovery

Hongxin Xiang, Li Zeng, Linlin Hou, Kenli Li, Zhimin Fu, Yunguang Qiu, Ruth Nussinov, Jianying Hu, Michal Rosen-Zvi, Xiangxiang Zeng () and Feixiong Cheng ()
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Hongxin Xiang: Hunan University
Li Zeng: Hunan University
Linlin Hou: Hunan University
Kenli Li: Hunan University
Zhimin Fu: Cleveland Clinic
Yunguang Qiu: Cleveland Clinic
Ruth Nussinov: National Cancer Institute
Jianying Hu: Yorktown Heights
Michal Rosen-Zvi: IBM Research Labs
Xiangxiang Zeng: Hunan University
Feixiong Cheng: Cleveland Clinic

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Accurate molecular representation of compounds is a fundamental challenge for prediction of drug targets and molecular properties. In this study, we present a molecular video-based foundation model, named VideoMol, pretrained on 120 million frames of 2 million unlabeled drug-like and bioactive molecules. VideoMol renders each molecule as a video with 60-frame and designs three self-supervised learning strategies on molecular videos to capture molecular representation. We show high performance of VideoMol in predicting molecular targets and properties across 43 drug discovery benchmark datasets. VideoMol achieves high accuracy in identifying antiviral molecules against common diverse disease-specific drug targets (i.e., BACE1 and EP4). Drugs screened by VideoMol show better binding affinity than molecular docking, revealing the effectiveness in understanding the three-dimensional structure of molecules. We further illustrate interpretability of VideoMol using key chemical substructures.

Date: 2024
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DOI: 10.1038/s41467-024-53742-z

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