Leveraging HTS Data using Drug Profiling

Traditionally, model building, prediction and virtual screening has been an expert-only field, limiting the application of such techniques. Information and knowledge transfer between therapeutic groups has been limited and the value of such models consequently reduced. We present a model building system that maximises the return on the investment made to build and refine QSAR models. Designed to support rapid creation of high quality QSAR models using a variety of algorithms, the system supports creation, validation and annotation of models. The models have application in virtual screening and property prediction of compound libraries, complementing the skills and knowledge of research scientists in designing new candidates. This platform provides a systemic approach to drug design providing the ability to build an in silico drug profile for as many measurable responses that a system makes to a chemical stimulus. The system promotes QSAR modelling as a front line tool to aid drug discovery scientists.