Scaling SMILES-Based Chemical Language Models for Therapeutic Peptide Engineering.

ABSTRACT: Therapeutic peptides occupy a unique middle ground in drug discovery, offering the high specificity of protein interactions with the chemical diversity of small molecules, yet they currently fall in a computational blind spot. Existing AI tools cannot handle them effectively: protein models are restricted to natural amino acids, while chemical models struggle to process large, polymer-like sequences. This disconnect has forced the field to rely on static chemical descriptors that fail to capture subtle chemical details. To bridge this gap, we present PeptideCLM-2, a chemical language model trained on over 100 million molecules to natively represent complex peptide chemistry. PeptideCLM-2 consistently outperforms both chemical descriptors and specialized AI models on critical drug developme

SUBMITTER: Feller AL 

PROVIDER: S-EPMC12803269 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature