Pharmacophore-Inspired Virtual Receptor: Generating Realistic Binding Affinity Data for Machine Learning

Kilian Conde-Frieboes, Carsten Stahlhut

Keywords: Simulation, Receptor, binding, Data Generation

TL;DR: A tool to generate receptor-peptide binding data based on a pharmacophore model

Abstract:

VReceptor is a virtual receptor simulation framework that generates realistic, sequence-dependent binding affinity data for peptide-based therapeutics. It combines pharmacophore-inspired weighting schemes with amino acid similarity metrics to evaluate peptide design strategies in a controlled environment. Validation using the Prolactin-Releasing Peptide (PrRP) dataset confirms VReceptor’s ability
to approximate binding behaviors and optimize experimental planning. This framework empowers researchers in computational peptide design and active learning workflows while highlighting areas for further refinement.

Release To Public: Yes, please release this paper to the public

Submission Number: 26