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

Area of Science:

• Molecular informatics
• Drug discovery
• Artificial intelligence in chemistry

Background:

• Foundation and multimodal models are increasingly vital in molecular informatics.
• These models leverage large-scale pretraining across diverse data types including sequences, graphs, 3D structures, and text.
• Their application is particularly prominent in accelerating drug discovery processes.

Purpose of the Study:

• To provide practical guidance on the effective use of foundation and multimodal models in molecular informatics.
• To clarify the definition of foundation models in chemistry.
• To compare different model architectures and summarize generative modeling techniques.

Main Methods:

• Review and comparison of chemical language models, graph-based architectures, and 3D equivariant networks.
• Exploration of multimodal strategies integrating molecules with proteins, pockets, and natural language.
• Summary of diffusion-based generative modeling techniques.

Main Results:

• Guidance on selecting appropriate representations and data for model training.
• Strategies for designing effective pretraining and adaptation pipelines.
• Emphasis on rigorous evaluation methodologies, including realistic splitting protocols and uncertainty calibration.

Conclusions:

• Foundation and multimodal models offer significant potential for advancing molecular informatics and drug discovery.
• Careful consideration of model choice, data, and evaluation is crucial for successful real-world implementation.
• The review highlights key considerations for practitioners in the field.