ORIGINAL RESEARCH article
Front. Bioinform.
Sec. Protein Bioinformatics
Establishing Standardized Evaluation Protocols for Peptide-Target Interaction Prediction: The PTI-TAPE Consensus Framework eDelphi
1. Imperial College London, London, United Kingdom
2. Nebraska Medicine, Omaha, United States
3. University of Pennsylvania, Philadelphia, United States
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Abstract
Computational peptide-target interaction (PTI) prediction has advanced rapidly as a tool for drug discovery, yet the field lacks agreed evaluation standards. Discrepancies in metrics, negative sampling strategies, and dataset construction across publications currently render cross-study comparisons methodologically intractable, ultimately impeding the clinical translation of therapeutic peptides. To resolve this, we present PTI-TAPE (Peptide-Target Interaction — Tasks Assessing Peptide Engagement), a standardised benchmarking framework developed via a structured three-round eDelphi consensus study with 15 international experts spanning computational biology, machine learning, and regulatory science. Operating under a robust consensus threshold (>80% agreement), the panel established 26 definitive standards across 8 domains, governed by the new STRIDE (Standardisation, Transparency, Representativeness, Integration, Discovery, Evidence) framework. Key outcomes include mandating specific primary metrics for affinity and structure, enforcing a tiered negative sampling hierarchy that prioritizes experimental non-binders, and requiring mandatory temporal dataset splits. By providing a unified reporting checklist, benchmark specifications, and a biennial governance structure, PTI-TAPE ensures reproducible evaluation and fair method comparison, bridging the gap between computational PTI predictions and genuine therapeutic benefit.
Summary
Keywords
Benchmarking, eDelphi consensus, Evaluation standards, machine learning, peptide-target interaction, PTI-TAPE
Received
29 May 2026
Accepted
07 August 2026
Copyright
© 2026 Waldock, Wang, Darzi, de la Fuente-Nunez and Ashrafian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: William Waldock
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.