A Review of the Application of Multimodal Fusion in Artificial Intelligence-Driven Pharmaceutical Industry

Corresponding author: wang li, wangli@ntu.edu.cn

DOI: 10.12201/bmr.202512.00054

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  Abstract: Purpose/Significance This study sorts out the application status and technical paths of multimodal fusion in the core links of artificial intelligence (AI) in the pharmaceutical industry, clarifies its key value in breaking data barriers and shortening the R&D cycle, and provides theoretical and practical references for the innovative development of the pharmaceutical field. Method/Process By using the literature review method, relevant literatures were retrieved from CNKI, PubMed and Web of Science. The research progress and application practice of multimodal fusion technology in the field of AI pharmaceuticals were systematically summarized. Result/Conclusion Relying on the advantage of cross-modal information synergy, multimodal fusion technology has achieved remarkable results in core links of AI-driven pharmaceutical industry such as target identification and de novo drug design. However, it still faces challenges including insufficient modal alignment accuracy and weak model interpretability. In the future, it is necessary to move towards the direction of constructing efficient model architectures and deep integration with practical application scenarios.

  Key words: artificial intelligence; multimodal fusion; pharmaceutical industry; target identification; de novo drug design

  Submit time: 18 December 2025

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2025-09-30	10.12201/bmr.202512.00054V1	Download

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