Pharmix Corporation

Numerate, Inc.

Guido Lanza

Jessen Yu

John H Griffin

Nigel Duffy

Paul Boardman

Method, apparatus, and article of manufacture for modeling the biological effects of a molecule are disclosed. A machine learning application may be configured to process a set of training examples regarding a biological property of interest. Once trained, the machine learning application may be configured to generate a prediction regarding a property of interest for a test molecule. Embodiments of the invention provide a meta-model configured to generate a biological effect prediction for a molecule based on the predictions generated for the test molecule by a plurality of molecular property models.

Modeling biological effects of molecules using molecular property models

The present invention provides compositions of matter, kits and methods for their use in the treatment of MAP kinase-related conditions and/or HMG-CoA reductase-related conditions. In particular, the invention provides compositions for treating inflammatory and/or cardiovascular conditions in an animal subject by inhibiting p38&#x3b1; MAP kinase and/or HMG-CoA reductase, as well as providing formulations and modes of administering such compositions. The invention further provides methods for the rational design of inhibitors of MAP kinase, HMG-CoA reductase, or both for use in the practice of the present invention.

Compositions and treatments for inhibiting kinase and/or HMG-CoA reductase

Embodiments of the invention provide methods for evaluating the accuracy of a molecular model properties model (or predictions generated using a molecular properties model). The accuracy of a molecular properties model may be evaluated using three general approaches, (i) by using the same data set to both train the model and to estimate the accuracy of the model, (ii) by using distinct data sets to train and subsequently test a model, and (iii) by using multiple models (or sets of predictions).

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San Francisco, CA, United States of America

Guido Lanza