Every clinical trial should be simulated first
Mechanistic modeling, Digital Twins, and virtual populations, built by scientists and accelerated by AI. Better trials. Faster insights.
Trusted by leading research organizations
Why Nova?
Biology you can simulate. Predictions you can defend
Expert QSP modeling
Our scientists build mechanistic, biology-grounded models of your drug and disease. Every component traces to published biology.
AI-accelerated platform
Jinkō compresses months of modeling into days. AI does the heavy lifting. Your experts stay in control of every assumption.
Data sovereignty
Your data stays yours, isolated and never used to train shared models.
The expertise of Nova Services. The power of Jinkō
Two ways to work with us, one modeling engine behind both.
Modeling, done for you
Our QSP scientists build a custom mechanistic model of your compound and indication, generate a virtual population and patient Digital Twins, and run your trial In Silico to answer your questions on dose selection, trial design, subgroup response, and efficacy vs. toxicity.
Best when you want the answer, not the software.
Your In Silico trial platform
The same engine, in your hands. Build models, generate virtual populations, and simulate trial scenarios yourself, with AI assistance and full traceability at every step.
Best when your team wants to model at scale.
Agentic Digital Twin
Each virtual patient is a mechanistic Digital Twin: target, PK/PD, and disease biology linked end to end, so simulated outcomes reflect why, not just what.
Predictive validation
We predicted the MARIPOSA and CORALreef Phase III readouts before readout. Blinded and prospective, then checked against the result.
AI-grounded in biology
Every prediction traces back to published mechanism, so you can take a regulator through it step by step. AI reads the literature, generates and imports models, and calibrates at scale, with full traceability.
Proven predictions
Blinded and prospective, our simulations matched the trial before its readout
NSCLC
FLAURA2 / MARIPOSA
Independent prospective predictions from a mechanistic QSP model accurately forecasted PFS outcomes for the Phase III FLAURA2 and MARIPOSA trials in EGFR-mutated NSCLC. The framework was subsequently applied to simulate pemetrexed maintenance de-escalation strategies in FLAURA2.
Cardiometabolic
ASCVD / CORALreef
A mechanistic QSP model of atherosclerosis prospectively predicted LDL-C outcomes of the Phase III CORALreef Lipids trial, building on prior validation of the SIRIUS In Silico cardiovascular outcomes model.
Model Library
The most comprehensive disease model library
NSCLC: EGFR-mutated
Nova's mechanistic model of EGFR-mutant non-small-cell lung cancer (NSCLC) links tumor biology, drug pharmacology, and treatment response. It has been peer-reviewed and used to prospectively predict Phase III combination-therapy outcomes ahead of readout.
Learn moreT-cell engager (TCE) platform
A mechanistic pharmacodynamic model of T-cell–engaging bispecific antibodies in hematological malignancies. It captures the T-cell/tumor-cell engagement that drives efficacy, supporting translational dose selection and trial design.
Learn moreAntibody-drug conjugate (ADC) platform
A modeling platform for antibody-drug conjugates that captures payload delivery, target dynamics, and tumor response, built to guide ADC design and dose optimization across oncology programs.
Learn moreAtherosclerotic cardiovascular disease (ASCVD)
Nova's atherosclerotic cardiovascular disease (ASCVD) model is a mechanistic QSP model predicting LDL-C and cardiovascular outcomes under lipid-lowering therapy. It powers the SIRIUS In Silico trials and has been credibility-assessed in a peer-reviewed publication (npj Digital Medicine).
Learn moreAtopic dermatitis
Nova's atopic dermatitis (AD) model captures the interplay between the skin barrier and the immune system, describing the key pathways and biomarkers that drive disease course, supporting clinical trial design and biomarker stratification.
Learn moreInfluenza
Nova's influenza model is a multi-strain model of natural and vaccine-induced immunization, describing the pathways and biomarkers that drive vaccine efficacy across populations and age groups.
Learn moreRun your first virtual trial in under 5 minutes
Jinkō predicts clinical outcomes before trials in human subjects.