MOLECULAR INTERACTIONS
4× FASTER4× faster molecular design
A reproducible drug–protein binding infrastructure that predicts binding affinity reliably, before committing a single compound to synthesis.
- 01Protein + ligand set
- 02MD simulation
- 03Free-energy scoring
- 04Ranked leads
METHODS
- Molecular dynamics
- OpenMM
- MDTraj
- Statistical analysis
CHALLENGE
Non-scalable, non-reproducible affinity prediction
A major pharma company wanted to improve its drug–protein binding affinity predictions, a critical step in small molecule development. The better the prediction, the fewer compounds have to be synthesized and tested in the lab. They were already using an open-source free energy affinity tool built collaboratively by several large pharma companies, but two problems blocked it at scale:
- No standardized validation framework to judge prediction accuracy across different protein families.
- Poor reproducibility, which prevented systematic comparison of results across drug candidates.
SOLUTION
Drug–protein simulation infrastructure
LUCAI built unified, reproducible computational infrastructure that integrates structural analysis, molecular dynamics simulation, and drug–protein interaction scoring into automated workflows. The system runs standardized validation protocols across protein targets, so binding affinity predictions can be benchmarked consistently. Any researcher can now deploy and run the simulations without specialized computational expertise.
- Molecular dynamics
- OpenMM
- Statistical analysis
- MDTraj
IMPACT
Production-ready affinity prediction
The validated infrastructure is now deployable across any small molecule discovery project, regardless of protein target class. It delivers rapid, reliable affinity predictions before wet-lab synthesis, cutting molecular design cycles from roughly 8 months to 2.
- Reproducible binding predictions
- Computational standardization
- Automated validation framework
Talk to the team building your models.
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