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UnibindMD: An Integrated AI and Molecular Dynamics Platform for De Novo Peptide Binder Design

Created on 24 Oct 2026

Authors

Wu, X., Huang, A., Fu, C., Wang, Z., Fu, W.

Abstract

UnibindMD is an integrated computational platform that turns a protein target into validated de novo peptide-binder leads by chaining five orthogonal stages: deep-learning binder design (BindCraft), independent structure prediction and confidence assessment (AlphaFold3-class / Boltz-2), binding-affinity prediction (PRODIGY), developability profiling (ADMET-AI), and all-atom molecular-dynamics validation (GROMACS/CHARMM36m). The platform targets a persistent weakness of single-method design, namely that confidence metrics such as ipTM and pLDDT are self-consistent but not independent, so a design predicted to be confident may still be physically unstable. We demonstrate UnibindMD on three Andes virus (ANDV) intervention points, a setting of urgent need given a case fatality rate approaching 40%, a 2026 MV Hondius cruise-ship outbreak (13 cases, 3 deaths), and no approved therapeutic: the Gn-Gc glycoprotein complex (viral entry), the L-protein endonuclease domain (cap-snatching; PDB 6Q99), and the host VEGFR2 kinase (vascular permeability; PDB 2XIR). Across nine BindCraft campaigns (4,475 trajectories) the platform produced 464 accepted binders (acceptance 2.3-67.6%), with top candidates reaching ipTM = 0.94 and pLDDT = 0.94. The three selected leads were predicted by PRODIGY to bind with G = -9.1 to -11.1 kcal mol-1 (Kd 8 nM-0.2 M), and all three remained stably bound over 100 ns of unrestrained molecular dynamics (receptor CRMSD [≤] 2.6 [A], retained peptide fold, [≥] 99.5% of frames preserving more than half of the initial interface contacts, and 75-82% helical content). ADMET-AI flagged the expected poor oral bioavailability of large polar peptides and no PAINS alerts. By converting single-prediction design into a cross-validated, dynamics-tested workflow, UnibindMD provides a rapid, scalable, and generalizable path from any protein target to validated peptide-binder candidates.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 24 Oct 2026.

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