Fast Peptide Ensemble Generation via a Hybrid Computational Pipeline: A Baseline Validation on the MDM2-p53 Interface

Peptide drug discovery is often bottlenecked by the immense computational cost of Molecular Dynamics (MD) simulations required to sample flexible conformations. Here, we present an unoptimized hybrid computational pipeline. By integrating a rapid heuristic search strategy in topological state space with a high-fidelity all-atom 3D geometric reconstruction algorithm, our engine generates high-affinity peptide conformers within seconds using only CPU. We demonstrate its baseline performance on the classic MDM2-p53 target (PDB: 1YCR). From a completely random initial state, the best generated conformer achieved a docking score of -20.15 kcal/mol, closely approaching the crystallographic ground truth of -22.92 kcal/mol (a gap of 2.77 kcal/mol). This baseline validation highlights the viability of this pipeline as an ultra-fast pre-filter for peptide drug discovery.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-26
DOI
https://doi.org/10.5281/zenodo.23654447
Primary Topic
Computational Drug Discovery Methods
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Fast Peptide Ensemble Generation via a Hybrid Computational Pipeline: A Baseline Validation on the MDM2-p53 Interface

Baoyuan Zhang
Zenodo (CERN European Organization for Nuclear Research)
Computational Drug Discovery Methods
preprint

Fast Peptide Ensemble Generation via a Hybrid Computational Pipeline: A Baseline Validation on the MDM2-p53 Interface

Baoyuan Zhang
preprint en

Abstract

Peptide drug discovery is often bottlenecked by the immense computational cost of Molecular Dynamics (MD) simulations required to sample flexible conformations. Here, we present an unoptimized hybrid computational pipeline. By integrating a rapid heuristic search strategy in topological state space with a high-fidelity all-atom 3D geometric reconstruction algorithm, our engine generates high-affinity peptide conformers within seconds using only CPU. We demonstrate its baseline performance on the classic MDM2-p53 target (PDB: 1YCR). From a completely random initial state, the best generated conformer achieved a docking score of -20.15 kcal/mol, closely approaching the crystallographic ground truth of -22.92 kcal/mol (a gap of 2.77 kcal/mol). This baseline validation highlights the viability of this pipeline as an ultra-fast pre-filter for peptide drug discovery.

Zenodo (CERN European Organization for Nuclear Research)
Computational Drug Discovery Methods
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon