Molecular representation
Targets are encoded from structure, surface electrostatics and known binding motifs. Sequences are embedded with nucleic-acid language models so chemistry and geometry live in the same space.
Platform
A design stack for molecular recognition
Every biosensor is only as good as the molecule that does the recognizing. MOLwise treats that molecule as a design problem — solvable with models, physics and a tight experimental loop.
Generative binder design
Conditioned generative models emit aptamer and peptide candidates for a specific epitope, with motif constraints, secondary-structure control and synthesizability filters applied at generation time.
Physics-based screening
Docking and molecular dynamics stress-test the shortlist: binding pose stability, conformational switching and off-target discrimination against the real sample matrix.
Learned affinity ranking
Surrogate models trained on public and in-house binding data score every candidate, collapsing billions of possibilities to a testable set of tens.
Sensor-aware scoring
We score for device reality — immobilization chemistry, electrochemical signal change, stability and reproducibility — so the winning binder also makes a working sensor.
Closed-loop validation
Binding assays and prototype sensor runs feed measured results back into the models, tightening every subsequent campaign on the same target family.
Classical selection vs. MOLwise
Traditional SELEXMOLwise
Library originRandom poolTarget-conditioned generative design
ScreeningIterative wet-lab roundsIn-silico screening at 10⁹+ scale
TimelineMonths to a yearDays to weeks
Sensor readinessAssessed after selectionScored during design
LearningRestarts each campaignModels improve with every run