Foundation and Multimodal Models for Drug Discovery in Molecular Informatics: Principles, Evaluation, and Practical Guidance

Foundation and multimodal models are rapidly becoming a core methodology in molecular informatics, particularly for drug discovery, by leveraging large-scale pretraining across sequences, graphs, 3D structures, and text. This mini-review provides practical guidance on when these models help, how to choose representations and data, and how to design pretraining and adaptation pipelines for real-world use. We clarify what qualifies as a foundation model in chemistry; compare chemical language models, graph-based architectures, and 3D equivariant networks; review multimodal strategies that connect molecules with proteins, pockets, and natural language; and summarize diffusion-based generative modeling. We also emphasize rigorous evaluation, discussing realistic splitting protocols, distribution shift, activity cliffs, uncertainty calibration, and conformal prediction in the context of widely used benchmarks.