GALAS Modeling Methodology Applications in the Prediction of the Drug Safety Related Properties
Poster Dec 07, 2010
Andrius Sazonovas, Remigijus Didziapetris, Justas Dapkunas, Liutauras Juska, Pranas Japertas
Yet, 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.
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