The Science Behind MolForge

Layer 1 — Uncertainty Quantification

Every Prediction Comes With a Confidence Interval

Most AI drug discovery platforms give you a point estimate. hERG: 0.3. But is that 0.3 reliable? It could be 0.1 or 0.7. MolForge applies Conformal Prediction to every ADMET prediction, providing 90% confidence intervals that tell you exactly how much to trust each number.

Layer 2 — Pareto Failure Boundary Mapping

We Map Where Molecules Go Wrong

Rather than only predicting good molecules, MolForge systematically maps the parameter space where candidates fail. Using ROBOGATE adaptive sampling combined with Multi-Objective Pareto optimization, we generate a Failure Boundary Heatmap for every target — showing exactly which structural modifications lead to cardiac toxicity, mutagenicity, or poor bioavailability.

Layer 3 — Certified Compound Tiers

Not All Predictions Are Equal

MolForge assigns every compound a certification tier based on the level of evidence supporting it. Pharmaceutical R&D teams can immediately understand what level of validation they're working with.

Current TYK2 pipeline: 5 Tier 2 compounds confirmed (ligand_ptm avg 0.883)

Layer 4 — Continuous Feedback Loop

The Model Gets Smarter With Every Experiment

Every CRO assay result — whether a hit or a miss — feeds back into MolForge's models. Negative data (failed experiments) are treated as first-class assets, continuously refining our Conformal Prediction calibration and Pareto boundary maps. This is the moat that takes time to build.