Bridge the gap between computational predictions and lab reality. Combine AI-powered variant design with developability screening and transparent, physics-grounded explanations.
A complete workflow that accounts for binding affinity AND real-world manufacturability.
Ranked mutation recommendations with ΔΔG binding affinity, developability scores, and expression success rates from fine-tuned AlphaFold/ESM models.
Interactive molecular viewer with constraint editing. Click residues to set disulfide bonds, hydrogen bond distances, and surface charge constraints.
Comprehensive screening for aggregation propensity, isoelectric point, surface hydrophobicity, thermal stability, and host-specific expression warnings.
Understand WHY mutations work through energy contribution maps, new hydrogen bond visualizations, steric clash analysis, and before/after 3D comparisons.
Shared project workspaces with commenting, peer review workflows, experimental outcome logging, and wet-lab validation tracking.
Automatic codon optimization for E. coli and CHO cells, glycosylation site warnings, signal peptide recommendations, and one-click sequence export.
Three steps from sequence to validated therapeutic candidate.
Upload FASTA sequences or PDB structures. Specify your target protein, expression host, and set physical constraints on residues.
Our AI engine analyzes mutations for binding affinity, developability, and expression viability — then explains every recommendation.
Take confident go/no-go decisions. Log experimental results and build a validated track record of prediction accuracy.
Every metric at a glance. No black boxes — just transparent, actionable predictions.
Reduce experimental failure rates and accelerate your drug discovery pipeline with transparent, physics-grounded AI predictions.