The screening of compounds for ADME-Tox targets plays an important role in drug design. QSPR models can increase the speed of these specific tasks, although the performance of the models highly depends on several factors, such as the applied molecular descriptors. In this study, a detailed comparison of the most popular descriptor groups has been carried out for six main ADME-Tox classification targets: Ames mutagenicity, P-glycoprotein inhibition, hERG inhibition, hepatotoxicity, blood–brain-barrier permeability, and cytochrome P450 2C9 inhibition. The literature-based, medium-sized binary classification datasets (all above 1,000 molecules) were used for the model building by two common algorithms, XGBoost and the RPropMLP neural network. Five molecular representation sets were compared along with their joint applications: Morgan, Atompairs, and MACCS fingerprints, and the traditional 1D and 2D molecular descriptors, as well as 3D molecular descriptors, separately. The statistical evaluation of the model performances was based on 18 different performance parameters. Although all the developed models were close to the usual performance of QSPR models for each specific ADME-Tox target, the results clearly showed the superiority of the traditional 1D, 2D, and 3D descriptors in the case of the XGBoost algorithm. It is worth trying the classical tools in single model building because the use of 2D descriptors can produce even better models for almost every dataset than the combination of all the examined descriptor sets.

Diastereomeric salt crystallization is a classical, widely applicable chiral resolution technique, which enables the separation of the enantiomers of both racemate and conglomerate-forming compounds. A resolution method for racemic pregabalin with l-tartaric acid was developed to obtain pure (S)-pregabalin l-tartrate monohydrate crystals with the yield ranging from 43 to 50%. A series of designed resolution experiments were executed at different cooling rates and temperature end points to estimate the crystallization kinetics using population balance modeling. Inline ATR-FTIR measurements of these experiments were used to calculate concentrations in the crystallization phase and to collect solid–liquid equilibrium data by the solubility trace method after product sampling. Since diastereomeric salts are dissociable ionic compounds, solubility product expressions were used in the model to express the relative supersaturation as the driving force of crystallization. As a result, a population balance model with secondary nucleation, growth, and agglomeration mechanisms was identified, and the simulated supersaturation profiles and product size distributions well reproduced the measured data.

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