Optimizing the performance of in silico ADMET general models according to local requirements: MARS approach. solubility estimations as case study.

The quality of in vitro data used to build in silico absorption, distribution, metabolism, and toxicity (ADMET) models is, in many cases, inconsistent. The paucity of data from single laboratory sources has led to the mixing of data sets with varying experimental conditions and to the coverage of restricted chemical space in models which are purported to be of general applicability. In order to overcome these shortcomings, a method, Metropolis/Monte Carlo adaptive ranking simulation (MARS) has been developed. This aims to estimate "optimal flexible threshold points" in order to achieve better correlation between any in silico ADMET model and any discrete qualitative experimental data. The MARS method covers three key factors: the predictive model, the experimental procedure for the assay, and the chemical series or scaffold. When large and general solubility data sets (>650 compounds) are analyzed against commercially available in silico models, using MARS, an improvement in kappa statistics up to 16.2% is obtained. When particular chemical series are addressed, improvements up to 46.0% are seen on kappa statistics. This coefficient then allows an investigation into the effectiveness of a classifier by assessing the improvement over chance. These improvements in ranking estimations allow more predictive decision-making for virtual libraries.

Data reduction and representation in drug discovery.

Pre-clinical drug discovery relies increasingly on huge volumes of inter-related multivariate data. To make sense of these data and enable quality decision-making based on this plethora of information they must be presented in an interpretable form. Reducing the dimensionality of the data often leaves a data set that is too complex to interpret readily, so intuitive visualization methods are needed. Bioinformatics has provided much of the impetus for visualizing complex data, the cheminformatics community has been aggressive with the data-reduction problem. The increasing appreciation of the inter-related multifactorial nature of pre-clinical drug discovery makes visualization a burgeoning and active field that spans biosciences, mathematics and visual psychology.