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On this page you can use industry proven algorithms to predict physico-chemical properties of chemical structures, such as solubility and lipophilicity.

The predecessor, ALOGPS 2.1, has been widely tested by pharma companies, where it repeatedly demonstrated excellent results (see http://www.vcclab.org). Recently, the ALOGPS program came out as the top quality tool for the prediction of logP values on a set of in-house data from Pfizer & Nycomed - in comparison to 12 commercial and 6 public approaches (Mannhold et al, 2009).
Since then, it has been shown that - on a test set of 95809 Pfizer molecules - application of the self-learning feature of our software can decrease the error further from 1.02 log units to 0.59 log units (Tetko et al, 2009). Moreover, our applicability domain approach allows to identify the 60% of the structures for which the predicition accuray reaches 0.33 log units.

ALOGPS 3.01, available here, was developed using the same approach, Associative Neural Networks, but on a 50% larger dataset of molecules for logP and almost 8 times larger dataset for water solubility. We are currently developing new modules for the prediction of DMSO solubility and pKa prediction, other important parameters in early drug research.

If you need help or have suggestions or other comments, please contact help@eadmet.com.

We offer commercial versions of these tools that allow batch processing, customization, user data integration and much more. If you are interested, feel free to ask for more information at sales@eadmet.com.

Note that our free license only permits non-commercial and evaluation usage.