ZeroGEN: leveraging language models for zero-shot ligand design from protein sequences.

ABSTRACT:

Motivation

Deep generative methods based on language models have the capability to generate new data that resemble a given distribution and have begun to gain traction in ligand design. However, existing models face significant challenges when it comes to generating ligands for unseen targets, a scenario known as zero-shot learning. The ability to effectively generate ligands for novel targets is crucial for accelerating drug discovery and expanding the applicability of ligand design. Therefore, there is a pressing need to develop robust deep generative frameworks that can operate efficiently in zero-shot scenarios.

Results

In this study, we introduce ZeroGEN, a novel zero-shot deep generative framework based on protein sequences. ZeroGEN analyzes extensive data on protein-l

SUBMITTER: Chen Y 

PROVIDER: S-EPMC12596140 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature