A molecular video-derived foundation model for scientific drug discovery

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.

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Publication Details

Journal
Nature Communications
Published
2024-11-08
DOI
https://doi.org/10.1038/s41467-024-53742-z
Citations
28
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
10.12

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article

A molecular video-derived foundation model for scientific drug discovery

Hongxin Xiang, Michal Rosen‐Zvi, Linlin Hou, Feixiong Cheng et al.
28 citations
Nature Communications
Computational Drug Discovery Methods
10.12
article

A molecular video-derived foundation model for scientific drug discovery

Hongxin Xiang, Michal Rosen‐Zvi, Linlin Hou, Feixiong Cheng, Ruth Nussinov, Xiangxiang Zeng, Li Zeng, Jianying Hu, Yunguang Qiu, Zhimin Fu, Kenli Li
article en
28 citations

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.

Nature CommunicationsVol. 15(1)
Cleveland Clinic (US), Tel Aviv University (IL), Hunan University (CN), Hebrew University of Jerusalem (IL), Cleveland Clinic Lerner College of Medicine (US), IBM Research - Thomas J. Watson Research Center (US), Frederick National Laboratory for Cancer Research (US), National Cancer Institute (US), Cleveland Clinic Akron General (US), IBM Research - Haifa (IL), Case Western Reserve University (US), Northeast Ohio Medical University (US)
U.S. Department of Health and Human Services, National Institutes of Health, National Cancer Institute
Good health and well-being
Openalex Percentile: Top 1%
Computational Drug Discovery Methods
10.12
DOIOpen Access PDFOpenAlex
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