Multimodal Foundation Model for Pharmaceutical Knowledge Extraction from Structures, Assays, Patents, and Regulatory Text Download PDF

Pharmaceutical research and development generates diverse knowledge spanning chemical structures, biological assay readouts, patent claims, and regulatory documents, yet these sources are typically curated, searched, and interpreted through separate workflows even when describing the same compound, target, or safety concern. No single system currently provides unified reasoning across structural chemistry, pharmacology, intellectual property, and regulatory evidence, forcing researchers to manually integrate information from multiple databases, document repositories, and expert interpretations. This article proposes a conceptual multimodal foundation model for pharmaceutical knowledge extraction that aligns molecules, assays, patents, and regulatory text within a shared representation space. The system architecture combines molecular encoders, assay-table encoders, document-text encoders, contrastive alignment modules, retrieval-augmented generation, and a conversational interface to enable evidence-grounded question answering across pharmaceutical data modalities. Such a model could assist medicinal chemists, pharmacologists, regulatory scientists, and competitive-intelligence teams in retrieving integrated answers that currently require separate searches, while also supporting drug repurposing, safety signal review, and patent landscape analysis by linking evidence across modalities. By facilitating cross-domain reasoning, a pharmaceutical multimodal foundation model could transform the synthesis of complex evidence into a routine and accessible capability.