Each preclinical cycle takes months and millions, yet most candidates still fail later because teams are forced to advance compounds on partial evidence. Structure-based methods can suggest binding, but they do not show how compounds will behave in vivo.
Traditional assays evaluate one phenotype in one cell type. Adverse effects, off-target activity, and context-dependent responses remain hidden until costly later stages.
Previous iterations generate functional data that are rarely reused when a program stalls or pivots. Those datasets may still contain mechanistic insight the next campaign can use.
Binding affinity models cannot capture polypharmacology, pathway crosstalk, or cell-context-specific responses. The gap between target selection and therapeutic effect remains wide.
Our bulk foundation model scores small molecules by predicted functional response across viability, expression, morphology and more. Discovery teams use it to triage leads before committing to follow-up experiments, flag potential risks before in vivo studies, and classify mechanisms of action without target-dependent assays.
Our model predicts phenotypic response across diverse cellular contexts, identifying off-target liabilities and context-dependent effects that single-assay workflows cannot. Teams can assess risk earlier, before committing to secondary assays or additional chemistry.
Our model learns across assays, allowing past screening data to improve predictions on new compounds and functional endpoints. Legacy data and shelved assets become the basis for the next round of candidate ranking.
We use chemical structure to predict functional assay performance, not binding affinity. The output is a confidence-ranked shortlist based on predicted activity, early risk signals, and target-agnostic MoA.
Yes. Our bulk foundation model has already learned shared structure across diverse assay types, including proliferation, morphology, expression, and viability readouts. It transfers knowledge even to assays it has never directly observed. A 100-compound screen with a robust functional readout provides sufficient signal for fine-tuning.
We represent assays as points in a continuous phenotypic space, not as discrete categories. A novel assay is never truly “unseen” by the model. It is positioned relative to assays already in the training corpus, enabling meaningful predictions from day one.
This is the scenario our platform was designed for. Traditional screening evaluates a compound in a single disease-relevant cell type. Our model simultaneously predicts phenotypic response across all cell contexts in our corpus, flagging selectivity issues, off-target toxicity, and context-dependent effects that single-assay workflows cannot detect.
Going back to target validation after a failed clinical result costs tens of millions. Functional screening across cell contexts flags these signals during lead optimization, when course-correction is still possible.
Yes. Our foundation model was pre-trained on over 10 million functional experiments, so even a modest archived screen provides sufficient data for fine-tuning. We can re-score the original library against new phenotypic endpoints, identify structurally novel candidates that may have been deprioritized, and screen unsynthesized analogs virtually.
Most platforms require massive proprietary datasets to deliver results. If you have deep biological expertise but limited screening data, that is precisely where transfer learning from a phenotypically diverse foundation model creates the most value.
Structure-based methods model molecular binding. They do not capture protein expression dynamics, pathway crosstalk, polypharmacology, or cell-context-dependent effects. We model how the cell actually responds to treatment, not what a docking simulation predicts it might do. The two approaches identify complementary candidate sets with minimal overlap.
Our most productive collaborations are with teams that already use structural and target-based tools. Reflector adds the phenotypic layer that structure-based methods cannot provide. The result is compound prioritization that accounts for both binding affinity and functional consequence.
We are scaling phenotypic prediction into an infrastructure layer for drug discovery. If you have built ML systems, understand perturbation biology, or know what it takes to close a partnership deal and you want to do it at the stage where your decisions shape the company, reach out.
We collaborate with academic and industry labs running functional screens. You get predictive analysis and co-publication opportunities. We get the biological diversity that makes our model generalize.