Start with What the Disease Needs. End with a Candidate.
De novo small molecule discovery grounded in disease pathway biology — from gap analysis to lead compound, fully integrated with the simulation chain that validates it.
Discovery Inverted
Most computational drug discovery starts at the binding site: find a target, generate molecules that bind it, filter for drug-like properties, hand off to medicinal chemistry. The biological context — whether that target actually matters in this patient's disease, at this stage, given what is already being treated — comes later, if at all.
OVIVO inverts this. Discovery begins with the disease simulation: a quantitative map of which biological pathways are covered by the current treatment landscape and which are not. Targets are identified from the gaps. Molecules are generated to fill them. Every candidate is then evaluated against the full PK/PD/disease simulation chain before being reported — not as a binding prediction in isolation, but as a drug effect in the context of the patient's biology.
The Pipeline
A directed chain of twelve stages — each using the outputs of the previous to make better decisions. A compound that fails selectivity gating does not proceed. A compound that passes every filter is validated through the full simulation before leaving the platform.
Disease Analysis
Pathway gap mapping from the full disease simulation. Target identification and druggability scoring from the uncovered mechanisms — not from target lists.
Molecular Design
Protein structure from PDB or AlphaFold. Binding site characterization, pharmacophore modeling, scaffold seeding, and genetic algorithm molecular generation.
Candidate Filtering
Docking fitness scoring, ML affinity prediction, hard selectivity gates against anti-target panels, and ADMET filtering for drug-likeness, metabolic stability, and toxicity flags.
Clinical Validation
SAR analysis and lead optimization, then the full PK/PD/Disease simulation chain. The output is a candidate with a mechanistic context — not a binding affinity number.
Evidence Grading
The platform will not report false precision. Every generated compound carries an explicit evidence grade that communicates exactly how much weight to place on each prediction. Honest uncertainty is a feature — the alternative is wasted synthesis and failed assays.
HYPOTHESIS
Pharmacophore-based prediction only. Directional signal, low confidence. Reported as ~50 nM, not 46.7 nM. A starting point for further investigation, not a result.
CALIBRATED
ML model predictions with R² ≥ 0.3, docking-validated. Meaningful signal, suitable for wet lab prioritization. The level at which a candidate becomes actionable.
VALIDATED
Docking confirmed and experimentally anchored. Actionable for wet lab follow-up. The platform also reports where it is blocked — a genuine selectivity barrier disclosed is more valuable than a false positive delivered.
The platform tells you where it is blocked, not just where it succeeds. A genuine selectivity barrier disclosed is more valuable than a false positive delivered with confidence.
Additional Capabilities
Beyond de novo generation, the discovery infrastructure supports a range of strategies for finding and validating therapeutic opportunities.
Drug Repurposing
Cross-disease efficacy screening identifies approved drugs with pathway overlap in new indications. Dramatically lower regulatory and development cost than de novo compounds — and higher certainty on safety, because the drug has already been in people.
Combination Pharmacology
For complex diseases requiring multi-target coverage, the platform designs compound combinations with modeled synergy, coverage complementarity, and DDI safety assessment built in from the start.
Polypharmacology
Single compounds designed to engage multiple targets are evaluated for their multi-target coverage maps and selectivity profiles across the full anti-target panel — with explicit coverage quantification, not just binding lists.
Retrosynthesis
Synthetic route feasibility assessment for generated compounds — so the candidates that reach prioritization are ones that can actually be made in the lab, not just computational constructs.
Co-Development
For Biotech, Pharma, and CRO partners looking to apply de novo discovery, drug repurposing, or combination strategy to a specific disease or target space.
develop@ovivolabs.comInvestments
The most ambitious capability layer on the platform — and the most defensible moat as candidates continue to validate in assays. Growing evidence base. Clear trajectory.
invest@ovivolabs.comMedia & Research
Press inquiries, academic collaboration, and questions about specific discovery campaigns, methodology, or the platform's evidence grading framework.
media@ovivolabs.com