Gene expression based inference of cancer drug sensitivity
Predictive modeling approach to infer treatment response in cancers.
A new decision-support tool for oncologists and hospitals. A molecular replica of each patient’s tumor simulating how it responds, resists, and evolves.
Nearly 1 in 3 patients endure an ineffective first therapy.
Most cancer deaths trace back to drug resistance.
No oncologist can keep pace with the literature.
Each generates data. None tells you what to do with it.
Oncology AI agents designed for for every step of the Patient journey
AI-native clinical EHR. Six specialized agents. Any test ingested.
The patient’s AI co-pilot. Genomic data in plain language.
Molecular cancer therapy simulation. Every treatment ranked.
Cancer sequencing panels, built for the Digital Twin.
One secure, governed platform beneath every point of care.
Simulate response before the first dose rather than after several lines of therapy.
Models molecular changes that may signal emerging resistance.
Pharmacogenomic flags surface risk before it reaches the patient.
Next-line options modeled now, ready the moment they're needed.
GAP tags surface what to order before it’s needed, UG- T1A1 before sacituzumab, HbA1c before alpelisib, ranked.
Patient #4471
● Resistance model flagged and deprioritized
Accelerated Drug Validation ›››Continuous Evidence
GeneSilico closes the gap between AI drug discovery and human proof, generating efficacy evidence continuously across an oncology hospital and CRO network for AI-native biotech and pharma.
An AI-discovered candidate, ready for human proof.
Response modeled patient by patient, before a dose.
Multicentre study across the hospital and CRO network.
Structured, auditable evidence to sponsors and regulators.
Predictive modeling approach to infer treatment response in cancers.
Prestigious international research grant recognizing GeneSilico's contribution to AI-guided oncology therapy and drug resistance modelling.
Live with hundreds of oncologist. Ready for your institution.