Toxometris.ai offers a cloud‑based SaaS platform that predicts ADMET and toxicity endpoints for any small molecule using ensembles of LLMs, graph neural networks, and boosting models. The service delivers OECD‑compliant quantitative estimates, a unified risk‑score ranking, and expert‑reviewed reports, with secure API integration for high‑throughput compound triage.
Funding
Funding not disclosed
Founders
Product
Problem
Early‑stage drug discovery and chemical safety programs must evaluate large libraries of small molecules for absorption, distribution, metabolism, excretion, and toxicity (ADMET). Traditional experimental assays are costly, time‑consuming, and cannot keep pace with the volume of candidates, creating a bottleneck in safety assessment and decision‑making.
Solution
Toxometris.ai delivers a cloud‑based platform that generates in silico ADMET and toxicity predictions for any small molecule using a hybrid of Large Language Models (LLMs) and Graph Neural Networks (GNNs). The service produces quantitative endpoint estimates that conform to OECD defined‑endpoint criteria, and couples them with expert‑reviewed, actionable reports prepared by medicinal chemists and toxicologists. A proprietary risk‑score aggregates predicted ADMET values, physicochemical attributes, and medicinal‑chemistry metrics into a single ranking, enabling rapid prioritization of thousands of compounds. Consensus ensembles combine multiple molecular representations (SMILES, fingerprints, descriptors, graphs) to improve robustness and reduce model bias. The platform operates under NDA‑protected confidentiality and updates models in real time as new data become available, ensuring predictions remain aligned with the latest scientific standards.
Target Audience
The primary users are medicinal chemists, toxicologists, and regulatory scientists in pharmaceutical, biotech, and contract research organizations who need scalable, high‑confidence ADMET assessments for large compound libraries.
Features
- Ensemble modeling pipeline that integrates Boosting Machines, GNNs, and LLMs across diverse molecular encodings for high‑accuracy ADMET forecasts
- OECD‑compliant endpoint definitions with transparent applicability domains and goodness‑of‑fit metrics
- Risk‑Score ranking engine (0–1 scale) that synthesizes toxicity, drug‑likeness, and physicochemical properties for high‑throughput compound triage
- Mechanistic interpretation layer providing read‑across analogs, structural alerts, and rule‑based explanations within each report
- Secure, cloud‑native SaaS architecture supporting NDA‑level data confidentiality and role‑based access controls
- On‑demand model fine‑tuning for specific chemical families using curated data sets to boost predictive performance
- RESTful API and FHIR‑compatible endpoints for seamless integration with LIMS, ELN, and drug‑discovery workflows
- Automated report generation by domain experts, delivering actionable recommendations alongside raw prediction outputs