Johannes H. Voigt

Comparison of random forest and Pipeline Pilot Naïve Bayes in prospective QSAR predictions.

Random forest is currently considered one of the best QSAR methods available in terms of accuracy of prediction. However, it is computationally intensive. Naïve Bayes is a simple, robust classification method. The Laplacian-modified Naïve Bayes implementation is the preferred QSAR method in the widely used commercial chemoinformatics platform Pipeline Pilot. We made a comparison of the ability of Pipeline Pilot Naïve Bayes (PLPNB) and random forest to make accurate predictions on 18 large, diverse in-house QSAR data sets. These include on-target and ADME-related activities. These data sets were set up as classification problems with either binary or multicategory activities. We used a time-split method of dividing training and test sets, as we feel this is a realistic way of simulating prospective prediction. PLPNB is computationally efficient. However, random forest predictions are at least as good and in many cases significantly better than those of PLPNB on our data sets. PLPNB performs better with ECFP4 and ECFP6 descriptors, which are native to Pipeline Pilot, and more poorly with other descriptors we tried.

Cross-Docking of Inhibitors into CDK2 Structures. 1

Predicting protein/ligand binding affinity is one of the most challenging computational chemistry tasks. Numerous methods have been developed to address this challenge, but they all have limitations. Failure to account for protein flexibility has been a shortcoming of many methods. In this cross-docking study the data set comprised 150 inhibitor complexes of the protein kinase CDK2. Gold and Glide performed well in terms of docking accuracy. The chance of cross-docking a ligand within a 2 A RMSD of its experimental pose was found to be 50%. Relative binding potency was not properly predicted from scoring functions, even though cross-docking of each inhibitor into each protein structure was performed and only scores of correctly docked ligands were considered. An accompanying paper (Voigt, J. H.; Elkin, C.; Madison, V. S. Duca, J. S. J. Chem. Inf. Model. 2008, 48, 669-678) covers cross-docking and docking accuracy from the perspective of using multiple protein structures.

eCounterscreening: Using QSAR Predictions to Prioritize Testing for Off-Target Activities and Setting the Balance between Benefit and Risk

During drug development, compounds are tested against counterscreens, a panel of off-target activities that would be undesirable for a drug to have. Testing every compound against every counterscreen is generally too costly in terms of time and money, and we need to find a rational way of prioritizing counterscreen testing. Here we present the eCounterscreening paradigm, wherein predictions from QSAR models for counterscreen activity are used to generate a recommendation as to whether a specific compound in a specific project should be tested against a specific counterscreen. The rules behind the recommendations, which can be summarized in a risk-benefit plot specific for a counterscreen/project combination, are based on a previously assembled database of prospective QSAR predictions. The recommendations require two user-defined cutoffs: the level of activity in a specific counterscreen that is considered undesirable and the level of risk the chemist is willing to accept that an undesired counterscreen activity will go undetected. We demonstrate in a simulated prospective experiment that eCounterscreening can be used to postpone a large fraction of counterscreen testing and still have an acceptably low risk of undetected counterscreen activity.