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Forecast the ADMET properties of your molecules with our online platform
A list of available ADMET properties can be found here
FREE TRIAL: 10 compounds for each property

Our Platform

Predicting ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties provides valuable insights that contribute to the efficiency, safety, and overall success of drug development efforts.
This proactive approach can save both time and resources by focusing efforts on compounds with higher chances of success and lower risks.

  • Pioneers in the application of Large Language Models in toxicology

  • Models trained on a vast amount of data using extensive computational resources (several hundred GPUs)


  • Ensemble models applied to various types of molecular representations extract the most information about molecule structure and properties


  • Broad coverage of applicability domain


  • Models are developed in agreement with regulatory authorities requirements (ICH M7, REACH, ECHA-16-B-09, OECD 37849783)


  • Reliable databases: manually reviewed by human experts


  • Transparency: QMRFs (harmonized QSAR Model Reporting Format) are available for every model


Acquire regulatory-compliant predictions

Perform predictions

Submit results to relevant regulatory bodies 


QMRF (QSAR Model Reporting Format) is a harmonized template for summarizing and reporting key information on QSAR models, including information on model validity.

Prepared by the model developer and assessed by regulators.

Prepare QPRFs

QPRF (QSAR Prediction Reporting Format) is an extensively updated reporting format that reflects the newly established OECD QSAR Prediction Principles

Prepared by the user and assessed by regulators.

Use CAF Checklist

The QAF (QSAR Assessment Framework) Prediction Checklist verifies whether a (Q)SAR model and its predictions comply with the principles outlined in the OECD guidance on model validation (OECD, 2007).

Prepared by the user or regulators


Models and reports meticulously crafted in compliance with ICH M7 guideline and QSAR Assessment Framework No. 386 based on the OECD principles for the model validation No. 69.


Ensemble models composed of diverse conventional and cutting-edge AI/ML algorithms, including Boosting Machines, Graph Neural Networks, and Large Language Models


A detailed report on the molecule activity, as well as rule-based and read-across data are available in PDF or CSV formats.

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RISK-SCORE: screen and rank compounds
Perform predictions for an unlimited number of compounds and expedite decision-making without the need to delve into numerous individual endpoint prediction values 


The Risk-Score is estimated based on ADMET properties along with physicochemical and medicinal chemistry properties.
This efficient approach facilitates the swift ranking of a substantial pool of compounds, delivering a list of the most and least promising candidates for acceptance as pharmaceuticals.





  • In addition to compiling a comprehensive set of open datasets, we employ a specially trained Large Language Model to discern and filter relevant data for the relevant endpoint from the vast pool of 35 million scientific papers available on PubMed.

  • Thousands of selected scientific papers are manually reviewed and information is extracted to construct a dataset.

  • The final datasets are manually reviewed and normalized by experts, adhering to the Klimisch criteria and falling under the 'reliable without restriction' category.


All-in-one pipeline

An exceptional opportunity to consolidate your predictions onto a single platform, avoiding the use of different software for predicting specific endpoints. 

Model adaptation

We adopt a bespoke approach to cater to your unique requirements. This entails the capacity to refine and retrain our models based on the specifics of your provided compounds, thereby optimizing predictions to align precisely with the characteristics of your distinct set of compounds.

Ranking system

The unique risk-score-based ranking system enables to perform predictions for an unlimited number of compounds in a very short period of time, facilitating decision-making without the need to delve into numerous individual endpoint prediction values.

Online platform

The platform's adaptive nature, distinct from traditional software, operates in real-time adjustments based on emerging data or refined models. This ensures that decision-making is not only rapid but also remains responsive to the latest scientific insights and advancements throughout the subscription period.



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