Most AI drug discovery tools only study successes.

OpenBind: Most datasets ignore the 90% of experiments that don't work, leaving AI models blind to recurring obstacles. We train on the hidden data of what fails.

9 of 10 molecules fail, yet nobody tracks the logic of these losses.

Decision-support for medicinal chemists.

Risk scoring engine

Submit your CSV to see a ranked list of risks. We identify the patterns of failure inherited from history.

Detect similar losses

Examine historical analogs that appeared promising but failed to bind, saving you useless synthesis cycles.

Transparent uncertainty

We define where our data is strong (EGFR) and where we must stay silent (VEGFR2).

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Proven data.

We won't replace the wet-lab. We help you by optimizing your queue by 40%.

0.64 Temporal AUROC

trained 2018, tested 2019-24

0.70 Precision@10

70% accuracy in predicting actual failures

0.78 Scaffold-split AUROC

validates against new molecular forms

0.013 Expected Calibration Error

our probability scores are dependable