Foundation Models for AI-Enabled Biological Design

Asher Moldwin; Amarda Shehu

Foundation Models for AI-Enabled Biological Design

Artificial Intelligence 2025-05-20 v1 Machine Learning Biomolecules Genomics

Authors: Asher Moldwin , Amarda Shehu

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.

Keywords

foundation model clinical decision support artificial intelligence

Cite

@article{arxiv.2505.11610,
  title  = {Foundation Models for AI-Enabled Biological Design},
  author = {Asher Moldwin and Amarda Shehu},
  journal= {arXiv preprint arXiv:2505.11610},
  year   = {2025}
}

Comments

Published as part of the workshop proceedings at AAAI 2025 in the workshop "Foundation Models for Biological Discoveries"

Foundation Models for AI-Enabled Biological Design

Abstract

Keywords

Cite

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