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Related Experiment Video

Updated: Feb 28, 2026

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A Machine Learning-Enabled Venom Peptide Platform for Rapid Drug Discovery.

Fei Cai1, Lijuan Zhou1, Bryce Delgado2

  • 1Department of Biological Chemistry, Genentech, 1 DNA Way, South San Francisco, CA 94080, USA.

Pharmaceuticals (Basel, Switzerland)
|February 27, 2026
PubMed
Summary

A novel venom peptide discovery platform, guided by machine learning, successfully identified potent binders for challenging therapeutic targets. This approach accelerates the development of small, antibody-like peptide therapeutics.

Keywords:
machine learningphage displayvenom peptide library

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Area of Science:

  • Biotechnology
  • Drug Discovery
  • Protein Engineering

Background:

  • Nature provides diverse venom peptides targeting complex membrane proteins.
  • Disulfide-rich venom peptides offer structural stability and pharmacological potential.

Purpose of the Study:

  • To develop a robust venom peptide therapeutics discovery system.
  • To leverage machine learning for designing peptide libraries with enhanced stability and diversity.

Main Methods:

  • Constructed a phage display library of ~482 venom-derived scaffolds.
  • Utilized a machine learning model to predict mutation-tolerant residues for optimal foldability.
  • Screened the library against CD47, DLL3, IL33, and P2X7R targets.

Main Results:

  • Achieved 100% success rate, identifying strong binders for all four targets.
  • Rapidly identified potential DLL3 binder leads using high-throughput recombinant expression and ML-assisted affinity maturation.
  • Developed an efficient venom-based discovery platform.

Conclusions:

  • The venom-based platform offers superior functionality and developability over conventional methods.
  • Combines natural peptide diversity, ML-guided design, and recombinant expression for efficient binder identification.
  • Enables development of next-generation peptide therapeutics targeting complex proteins.