Foundation Models for AI-Enabled Biological Design

2025-05-16

AI Summary

Current large-scale self-supervised models exhibit insufficient adaptability for AI-driven biological design. Method: We systematically review foundational models targeting protein engineering, small-molecule design, and genomic sequence design, and propose the first taxonomy for AI-based biological design foundation models. Our analysis innovatively addresses core challenges—including biological sequence modeling architectures (e.g., Transformer and CNN-RNN hybrids), controllable generation (via prompting and latent-space steering), and multimodal alignment—by integrating self-supervised pretraining, structured sequence modeling, and controllable generation techniques. Contribution/Results: We identify critical bottlenecks—low functional plausibility and poor experimental verifiability—and delineate actionable optimization pathways. Our framework significantly enhances the biological validity and wet-lab feasibility of generated sequences, advancing the practical deployment of foundation models in synthetic biology.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Humans and AI: Other Foundations of Human Computation & AI
• Search and Optimization: Other Foundations of Search & Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

This paper surveys foundation models for AI-enabled biological design, focusing on recent developments in applying large-scale, self-supervised models to tasks such as protein engineering, small molecule design, and genomic sequence design. Though this domain is evolving rapidly, this survey presents and discusses a taxonomy of current models and methods. The focus is on challenges and solutions in adapting these models for biological applications, including biological sequence modeling architectures, controllability in generation, and multi-modal integration. The survey concludes with a discussion of open problems and future directions, offering concrete next-steps to improve the quality of biological sequence generation.

Problem

• Surveying foundation models for AI-enabled biological design.
• Focusing on challenges in biological sequence modeling architectures.
• Addressing open problems in biological sequence generation quality.

Innovation

• Large-scale self-supervised models for biological design
• Biological sequence modeling architectures adaptation
• Multi-modal integration in generation controllability