Designing novel peptides with amyloid-<i>β</i> binding and clearance potential using BiLSTM and molecular dynamics.

ABSTRACT: Generative artificial intelligence is transforming de novo biomolecular design, yet developing models that reliably generate functional, target-specific peptides remains a significant challenge. Here, we introduce and validate a novel two-stage Bidirectional Long Short-Term Memory (BiLSTM) framework for the generative design of short, functional peptides. Our AI pipeline is trained on full-length proteins annotated with specific Gene Ontology (GO) terms related to amyloid-β (Aβ) interaction and is fine-tuned on experimentally validated peptide fragments to capture local functional motifs within a global protein context. As a proof-of-concept, we applied this framework to generate peptides targeting Aβ42, a key pathological agent in Alzheimer's disease. From 1,000 AI-generated sequen

SUBMITTER: Yata VK 

PROVIDER: S-EPMC12673400 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature