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Machine Learning for New Drug Development

We use machine learning to predict toxicity and ADME (absorption, distribution, metabolism, and excretion). After a lead has been designed, our model could offer in-silico assessment about ADME and toxicity base on auto feature extration AI.

Our Services

ADMET Prediction

Our ADMET Prediction Service contains about 30 endpoints, such as ADME (e.g. Caco-2 permeability, Cyp family inhibition), toxicity(e.g. hERG inhibition, mutagenicity), pharmacokinetics(e.g. volume of distribution, clearance) etc., helping you find druggable compounds instantly.

Lead Optimization

With huge compound database, our Lead Optimization Service could optimize compounds automatically and suggest the possible compounds, just setting your main structure you want and conditions you need, for example, “no hERG toxicity.” If you are not sure what 's the next, we also provide consulting services to help you.

Customized AI

Our Prediction Service is highly-performed in general cases. However, if you have some in-vivo/in-vitro data of similar structures, we could combine your data with ours to generate even more highly-performed prediction service for sets of the structure. Customized services will be charged based on the scope of your projects.

Benefits

Reliable prediction

With our powerful Auto-Feature-Extraction Algorithms, we could find more information from structures than traditional Fingerprints did, and the accuracy rate of our prediction is much better than other solutions.

Time-Saving Solutions

Using our reliable Prediction Services at the Early Stage will let you know which compound may fail at Pre-clinical Stage. You can then optimize it or give it up early to avoid unnecessary time cost.

Reasonable Suggestion

Different from other ligand-based methods, our Explained AI model will tell you why the compounds will fail in the future Pre-clinical Tests, offering reference to your final decisions.


Contact Us

Let's make a try

contact@virtualman.ai
17F., No. 3, Park St., Nangang Dist.,
Taipei City 115, Taiwan (R.O.C.)




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