Foundation Models for AI-Enabled Biological Design

Asher Moldwin, Amarda Shehu
5/16/2025
cs.AIcs.LGq-bio.BM

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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.

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Cite this paper

@article{moldwin2025foundation,
  title  = {Foundation Models for AI-Enabled Biological Design},
  author = {Asher Moldwin and Amarda Shehu},
  year   = {2025},
  eprint = {2505.11610},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2505.11610v1}
}

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