chemical informatics.

CULGI has an extensive database of pre-calculated molecules. The database serves as input for industrial projects. We also use the database to calibrate our chemical informatics algorithms. In addition, has a comprehensive set of all useful descriptors known to date. Third party software can further extend the set of descriptors, through CULGI’s open data structure and Python interface.

CULGI also includes a range of statistical calculation methods, such as linear and non-linear regression, and genetic algorithms.

One of many ways one could extend the modeling capabilities is the interfacing of CULGI to Machine Learning software, for instance, TensorFlow (from Google).  By importing both CULGI and TensorFlow in a Python script, one enables the combination of physics-modeling with data-driven modeling.

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