We present an overview of the most important databases with 2-dimensional and 3-dimensional structural information about drugs and drug candidates, and of databases with relevant properties. Access to experimental data and numerical methods for selecting and utilizing these data is crucial for developing accurate predictive in silico models. Many interesting predictive methods for classifying the suitability of chemical compounds as potential drugs, as well as for predicting their physico-chemical and ADMET properties have been proposed in recent years. These methods are discussed, and some possible future directions in this rapidly developing field are described.

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The B-RAF kinase plays an important role both in tumor induction and maintenance in several cancers. The molecular basis of the inactive B-RAF(WT) and B-RAF(V600E) inhibition and selectivity of a series of inhibitors was examined with a combination of molecular dynamics (MD), free energy MM-PBSA and local-binding energy (LBE) approaches. The conformational stability of the unbounded kinases and in particular the processes of the B-RAF (V600E) mutant activation were analyzed. A unique salt bridge network formed mainly by the catalytic residues was identified in the unbounded B-RAFs. The reorganization of this network and the restriction of the active segment flexibility upon ligand binding inhibit both B-RAF(WT) and B-RAF (V600E), thus appearing as an important factor for ligand selectivity. A significant correlation between the binding energies of the compounds in B-RAF(WT) and their inhibition effects on B-RAF (V600E) was revealed, which can explain the low mutant selectivity observed for numerous inhibitors. Our results suggest that the interactions between the activation segment and the alpha C-helix, as well as between the residues in the salt bridge network, are the major mechanism of the B-RAF (V600E) activation. Overall data revealed the important role of Lys601 for ligand activity, selectivity and protein stabilization, proposing an explanation of the observed strong kinase activation in the K601E mutated form.

In the present work, the Henderson-Hasselbalch (HH) equation has been employed for the development of a tool for the prediction of pH-dependent aqueous solubility of drugs and drug candidates. A new prediction method for the intrinsic solubility was developed, based on artificial neural networks that have been trained on a druglike PHYSPROP subset of 4548 compounds. For the prediction of acid/base dissociation coefficients, the commercial tool Marvin has been used, following validation on a data set of 467 molecules from the PHYSPROP database. The best performing network for intrinsic solubility predictions has a cross-validated root mean square error (RMSE) of 0.70 log S-units, while the Marvin pKa plug-in has an RMSE of 0.71 pH-units. A data set of 27 drugs with experimentally determined pH-solubility curves was assembled from the literature for the validation of the combined pH-dependent model, giving a mean RMSE of 0.79 log S-units. Finally, the combined model has been applied on profiling the solubility space at low pH of five large vendor libraries.

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