Multi-view biomedical foundation models for molecule-target and property prediction
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914353181097984 |
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| author | Suryanarayanan, Parthasarathy Qiu, Yunguang Sethi, Shreyans Mahajan, Diwakar Li, Hongyang Yang, Yuxin Eyigoz, Elif Saenz, Aldo Guzman Platt, Daniel E. Rumbell, Timothy H. Ng, Kenney Dey, Sanjoy Burch, Myson Kwon, Bum Chul Meyer, Pablo Cheng, Feixiong Hu, Jianying Morrone, Joseph A. |
| author_facet | Suryanarayanan, Parthasarathy Qiu, Yunguang Sethi, Shreyans Mahajan, Diwakar Li, Hongyang Yang, Yuxin Eyigoz, Elif Saenz, Aldo Guzman Platt, Daniel E. Rumbell, Timothy H. Ng, Kenney Dey, Sanjoy Burch, Myson Kwon, Bum Chul Meyer, Pablo Cheng, Feixiong Hu, Jianying Morrone, Joseph A. |
| contents | Quality molecular representations are key to foundation model development in bio-medical research. Previous efforts have typically focused on a single representation or molecular view, which may have strengths or weaknesses on a given task. We develop Multi-view Molecular Embedding with Late Fusion (MMELON), an approach that integrates graph, image and text views in a foundation model setting and may be readily extended to additional representations. Single-view foundation models are each pre-trained on a dataset of up to 200M molecules. The multi-view model performs robustly, matching the performance of the highest-ranked single-view. It is validated on over 120 tasks, including molecular solubility, ADME properties, and activity against G Protein-Coupled receptors (GPCRs). We identify 33 GPCRs that are related to Alzheimer's disease and employ the multi-view model to select strong binders from a compound screen. Predictions are validated through structure-based modeling and identification of key binding motifs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19704 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-view biomedical foundation models for molecule-target and property prediction Suryanarayanan, Parthasarathy Qiu, Yunguang Sethi, Shreyans Mahajan, Diwakar Li, Hongyang Yang, Yuxin Eyigoz, Elif Saenz, Aldo Guzman Platt, Daniel E. Rumbell, Timothy H. Ng, Kenney Dey, Sanjoy Burch, Myson Kwon, Bum Chul Meyer, Pablo Cheng, Feixiong Hu, Jianying Morrone, Joseph A. Biomolecules Artificial Intelligence Machine Learning Quality molecular representations are key to foundation model development in bio-medical research. Previous efforts have typically focused on a single representation or molecular view, which may have strengths or weaknesses on a given task. We develop Multi-view Molecular Embedding with Late Fusion (MMELON), an approach that integrates graph, image and text views in a foundation model setting and may be readily extended to additional representations. Single-view foundation models are each pre-trained on a dataset of up to 200M molecules. The multi-view model performs robustly, matching the performance of the highest-ranked single-view. It is validated on over 120 tasks, including molecular solubility, ADME properties, and activity against G Protein-Coupled receptors (GPCRs). We identify 33 GPCRs that are related to Alzheimer's disease and employ the multi-view model to select strong binders from a compound screen. Predictions are validated through structure-based modeling and identification of key binding motifs. |
| title | Multi-view biomedical foundation models for molecule-target and property prediction |
| topic | Biomolecules Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2410.19704 |