Numerion Labs offers an AI-driven platform that maps and screens an ultra‑large chemical space to identify drug‑like small molecules for pharmaceutical and biotech R&D. The system combines the COSMOS foundation model for activity prediction, the APEX enumerator that evaluates billions of virtual compounds in seconds, and EXPO optimization algorithms that fine‑tune models without extensive training data, accelerating hit identification and lead optimization.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional small‑molecule discovery relies on incremental exploration of known chemical space, which is time‑consuming, costly, and often fails to generate structurally novel candidates for challenging therapeutic areas such as immune and inflammatory diseases.
Solution
Numerion Labs delivers an AI‑driven drug‑hunting superplatform that systematically maps and screens an ultra‑large chemical universe to uncover previously inaccessible, drug‑like molecules. The platform combines a universal chemistry foundation model (COSMOS) that predicts functional activity directly from molecular structure with a hyper‑scalable enumerator (APEX) capable of evaluating billions of virtual compounds in seconds. Project‑specific expert optimization algorithms (EXPO) allow rapid model fine‑tuning without the need for extensive training datasets, accelerating hit identification and lead optimization. By operating in a chemistry domain 10,000 × larger than conventional tools and delivering screening efficiencies up to 10 billion‑fold higher, the system increases the probability of delivering first‑in‑class or best‑in‑class candidates while reducing experimental cycles and cost. The output integrates with standard cheminformatics pipelines and secure cloud analytics, enabling seamless handoff to downstream medicinal chemistry and preclinical programs.
Target Audience
Primary customers are pharmaceutical and biotech R&D organizations that run small‑molecule programs, particularly those targeting immune modulation and inflammatory disease pathways.
Features
- COSMOS universal chemistry foundation model that infers biological activity from raw molecular graphs with high predictive fidelity.
- APEX hyper‑scalable enumerator that exhaustively samples up to 10 billion virtual compounds and ranks them in under 30 seconds.
- EXPO expert optimization algorithms that customize predictive models for a target project without large labeled datasets, cutting model‑training time by 100×.
- Operates across a chemical operating domain 10,000 × larger than legacy virtual libraries, unlocking novel scaffolds.
- 10‑billion‑fold increase in virtual screening throughput compared with traditional docking or QSAR pipelines.
- Integrated cloud analytics suite with API access for downstream data export, model versioning, and secure collaboration.
- Built‑in compliance with industry data‑security standards (encryption at rest, role‑based access controls).