Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal Learning Article Swipe
Yifei Wang
,
Yingying Li
,
Lin Liu
,
Pengyu Hong
,
Hao Xu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1021/acs.jcim.5c00430
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1021/acs.jcim.5c00430
The versatility of multimodal deep learning holds tremendous promise for advancing scientific research and practical applications. As this field continues to evolve, the collective power of cross-modal analysis promises to drive transformative innovations, opening new frontiers in chemical understanding and drug discovery. Hence, we introduce asymmetric contrastive multimodal learning (ACML), a specifically designed approach to enhance molecular understanding and accelerate advancements in drug discovery. ACML harnesses the power of effective asymmetric contrastive learning to seamlessly transfer information from various chemical modalities to molecular graph representations. By combining pretrained chemical unimodal encoders and a shallow-designed graph encoder with 5 layers, ACML facilitates the assimilation of coordinated chemical semantics from different modalities, leading to comprehensive representation learning with efficient training. We demonstrate the effectiveness of this framework through large-scale cross-modality retrieval and isomer discrimination tasks. Additionally, ACML enhances interpretability by revealing chemical semantics in graph presentations and bolsters the expressive power of graph neural networks, as evidenced by improved performance in molecular property prediction tasks from MoleculeNet and Therapeutics Data Commons (TDC). Ultimately, ACML exemplifies its potential to revolutionize molecular representational learning, offering deeper insights into the chemical semantics of diverse modalities and paving the way for groundbreaking advancements in chemical research and drug discovery.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1021/acs.jcim.5c00430
- OA Status
- hybrid
- Cited By
- 2
- References
- 52
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4411565709Canonical identifier for this work in OpenAlex
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https://doi.org/10.1021/acs.jcim.5c00430Digital Object Identifier
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Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-23Full publication date if available
- Authors
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Yifei Wang, Yingying Li, Lin Liu, Pengyu Hong, Hao XuList of authors in order
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https://doi.org/10.1021/acs.jcim.5c00430Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://doi.org/10.1021/acs.jcim.5c00430Direct OA link when available
- Concepts
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Drug discovery, Computer science, Drug, Natural language processing, Artificial intelligence, Data science, Psychology, Medicine, Pharmacology, Bioinformatics, BiologyTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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