GALAS Modeling Methodology Applications In The Prediction Of Drug Metabolism Related Properties

Every model, no matter what data, descriptors, or modeling techniques used to build it, has a certain applicability domain, beyond which the quality of predictions becomes highly questionable. This reality is one of the fundamental issues concerning the effective use of third-party predictive algorithms in industry. The simple reason for this is that literature based training sets rarely cover the specific part of the chemical space that ‘in-house’ projects are focused on. Discrepancies between ‘in-house’ experimental protocols and methods used to measure properties for compounds in publicly available sources further affect the quality of resulting in silico predictions. Therefore the need has long existed for a method that would allow any company to effectively assess the Applicability Domain of any third-party model and to tailor it to its specific needs using proprietary ‘in-house’ data.   

Addressing the aforementioned issue, a GALAS (Global, Adjusted Locally According to Similarity) model concept has been developed providing a novel solution to this problem.