Research & Insights
Science, methodology, and industry perspective from the Genolux computational biology team.
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 — and why computational approaches are finally making it tractable.
Binding-Pocket Disruption Scores: How Genolux Quantifies PPI Interface Destabilization
A technical walkthrough of the scoring function Genolux uses to rank small-molecule candidates by their predicted ability to disrupt PPI binding interfaces.
AlphaFold2 Changed Everything — and PPI Modeling Is Still Catching Up
AlphaFold2 solved structure prediction but did not solve interface druggability prediction. This post examines the gap between structure availability and actionable PPI disruption models.
Hot-Spot Residues and the MDM2-p53 Interface: A Case Study in Computational PPI Targeting
The MDM2-p53 interaction remains one of the most-studied PPI targets in oncology. We walk through how hot-spot residue mapping and ΔΔG calculations guide interface disruption design.
Virtual Screening vs. PPI-Focused Docking: Why Standard Libraries Fall Short
Standard virtual screening pipelines optimize for enzyme pockets. PPI interfaces are shallow, extended, and hydrophobic — they require different library design, docking metrics, and scoring criteria.
Using Molecular Dynamics to Validate PPI Interface Stability Predictions
After computational screening identifies candidate disruptors, MD simulations in GROMACS and OpenMM confirm whether predicted interface destabilization holds under physiological conditions.
Compressing the Hit Identification Timeline: From 18 Months to Weeks
Traditional hit identification for PPI targets often runs 12-18 months of iterative synthesis. Computational pre-screening changes the economics — here is the workflow that makes it possible.
ADMET Challenges Specific to PPI Disruptors: Why Standard Filters Miss the Mark
PPI disruptors tend to be larger, more lipophilic, and more conformationally flexible than typical drug candidates. Standard ADMET filters reject them prematurely.
The PDB in 2025: How Structural Coverage of Oncology PPI Complexes Has Expanded
A quantitative look at PDB coverage of oncology-relevant PPI complexes over the past five years — and what gaps remain for computational modeling efforts.
4 more articles
SAR Without Synthesis: Building Structure-Activity Relationships Computationally for PPI Targets
Wnt Pathway PPI Targets: A Computational Assessment of Interface Druggability
Target Selection Criteria for Oncology PPI Programs: Balancing Biology, Chemistry, and Tractability
