GALAS Modeling Methodology Applications In The Prediction Of Drug Metabolism Related Properties
Poster Feb 21, 2017
Remigijus Didziapetris, Justas Dapkunas, Andrius Sazonovas and Pranas Japertas
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.
A Computational Model of Mood and Future ProspectsPoster
Mood disorders are characterized by changes in reaction to positive events, but studies examining mood in gambling have suggested these changes may not be different from control. Interrogating mood changes with a computational model may give insight into disorders like depression.READ MORE
New Biotransformation Prediction Engine Integrated into a Metabolite Identification SolutionPoster
Here we present a new prediction algorithm that determines the likelihood of biotransformation reactions, and subsequent metabolite identification, within an automated processing routine.READ MORE
A New Method for Analyzing MSe/All Ions Fragmentation in Xenobiotic Metabolism StudiesPoster
During early drug discovery, the study of metabolism plays an essential role in determining which drug candidates move forward into development and later stages. As an alternative to traditional Data Dependent Acquisition (DDA), the use of MSE/All Ions Fragmentation (AIF) has become common in metabolite identification workflows for the analysis of metabolic hot spots. Here we present a solution for analysis of MSE/AlF in metID studies.READ MORE