Predict PPI Disruption
Before You Synthesize
Genolux models protein-protein interaction surfaces computationally — delivering binding-pocket disruption scores for small-molecule candidates months before your first synthesis run.
PPI targets are biologically validated but computationally underserved. Standard docking pipelines don't model interface topologies. The result: discovery teams synthesize dozens of candidates to find one hit. Genolux changes that ratio.
Computational PPI Modeling — End to End
Interface Surface Analysis
Hot-spot residue identification, buried surface area calculation, and electrostatic potential mapping across known and predicted PPI complexes.
Disruption Score Prediction
Proprietary scoring function ranks small-molecule candidates by predicted binding-pocket disruption — before any chemistry is attempted.
Virtual Library Screening
PPI-optimized fragment and lead-like libraries screened computationally. Candidates prioritized by interface complementarity and synthetic accessibility.
From PPI Complex to Ranked Hit List
Submit Target Complex
Upload PDB coordinates or provide UniProt IDs. Genolux retrieves or predicts the complex structure.
Interface Characterization
Automated hot-spot identification, ΔΔG estimation per residue, binding pocket geometry extraction.
Candidate Scoring
Your SMILES library or our PPI-curated fragment set scored against the characterized interface.
Ranked Output
Disruption score ranked hit list with structural rationale and SAR vectors — delivered in days, not months.
Built for Computational Chemistry Teams in Oncology
Computational Chemists
- PPI-optimized scoring functions, not adapted enzyme-pocket metrics
- SMILES input, SDF output — integrates with your existing pipeline
- Benchmarked against experimental ΔΔG datasets
Drug Discovery Leadership
- Compress hit identification from 18 months to weeks
- De-risk synthesis investment before committing resources
- Focused oncology PPI target coverage — MDM2/p53, BCL-2 family, Wnt pathway
From the Research Blog
Protein-Protein Interaction Surfaces: The New Frontier in Oncology Drug Discovery
Why disrupting protein-protein interactions rather than targeting enzyme active sites opens a wider therapeutic window in oncology.
Hot-Spot Residues and the MDM2-p53 Interface: A Case Study in Computational PPI Targeting
How hot-spot residue mapping and ΔΔG calculations guide interface disruption design for one of oncology's most-studied targets.
Compressing the Hit Identification Timeline: From 18 Months to Weeks
Traditional hit identification for PPI targets often runs 12-18 months. Computational pre-screening changes the economics.
Ready to Screen Your PPI Targets?
Contact the Genolux research team to discuss a pilot program for your oncology discovery pipeline.
Contact Research Team