Driving force for the hydration of the swelling clays: case of montmorillonites saturated with alkaline-earth cations.

Important structural modifications occur in swelling clays upon water adsorption. The multi-scale evolution of the swelling clay structure is usually evidenced by various experimental techniques. However, the driving force behind such phenomena is still not thoroughly understood. It appears strongly dependent on the nature of the interlayer cation. In the case of montmorillonites saturated with alkaline cations, it was inferred that the compensating cation or the layer surface could control the hydration process and thus the opening of the interlayer space, depending on the nature of the interlayer cation. In the present study, emphasis is put on the impact of divalent alkaline-earth cations compensating the layer charge in montmorillonites. Since no experimental technique offers the possibility of directly determining the hydration contributions related to interlayer cations and layer surfaces, an approach based on the combination of electrostatic calculations and immersion data is developed here, as already validated in the case of montmorillonites saturated by alkaline cations. This methodology allows to estimate the hydration energy for divalent interlayer cations and therefore to shed a new light on the driving force for hydration process occurring in montmorillonites saturated with alkaline-earth cations. Firstly, the surface energy values obtained from the electrostatic calculations based on the Electronegativity Equalization Method vary from 450 mJ m(-2) for Mg-montmorillonite to 1100 mJ m(-2) for Ba-montmorillonite. Secondly, considering both the hydration energy for cations and layer surfaces, the driving force for the hydration of alkaline-earth saturated montmorillonites can be attributed to the interlayer cation in the case of Mg-, Ca-, Sr-montmorillonites and to the interlayer surface in the case of Ba-montmorillonites. These results explain the differences in behaviour upon water adsorption as a function of the nature of the interlayer cation, thereby allowing the macroscopic swelling trends to be better understood. The knowledge of hydration processes occurring in homoionic montmorillonites saturated with both the alkaline and the alkaline-earth cations may be of great importance to explain the behaviour of natural clay samples where mixtures of the two types of interlayer cation are present and also provides valuable information on the cation exchange occurring in the swelling clays.

QSAR Modelling of CYP3A4 Inhibition as a Screening Tool in the Context of DrugDrug Interaction Studies.

Drugdrug interaction potential (DDI), especially cytochrome P450 (CYP) 3A4 inhibition potential, is one of the most important parameters to be optimized before preclinical and clinical pharmaceutical development as regard to the number of marketed drug metabolized mainly by this CYP and potentially co-administered with the future drug. The present study aims to develop in silico models for CYP3A4 inhibition prediction to help medicinal chemists during the discovery phase and even before the synthesis of new chemical entities (NCEs), focusing on NCEs devoid of any inhibitory potential toward this CYP. In order to find a relevant relationship between CYP3A4 inhibition and chemical features of the screened compounds, we applied a genetic-algorithm-based QSAR exploratory tool SQS (Stochastic QSAR Sampler) in combination with different description approaches comprising alignment-independent Volsurf descriptors, ISIDA fragments and Topological Fuzzy Pharmacophore Triplets. The experimental data used to build models were extracted from an in-house database. We derived a model with good prediction ability that was confirmed on both newly synthesized compound and public dataset retrieved from Pubchem database. This model is a promising efficient tool for filtering out potentially problematic compounds.

Inductive transfer of knowledge: application of multi-task learning and feature net approaches to model tissue-air partition coefficients.

Two inductive knowledge transfer approaches - multitask learning (MTL) and Feature Net (FN) - have been used to build predictive neural networks (ASNN) and PLS models for 11 types of tissue-air partition coefficients (TAPC). Unlike conventional single-task learning (STL) modeling focused only on a single target property without any relations to other properties, in the framework of inductive transfer approach, the individual models are viewed as nodes in the network of interrelated models built in parallel (MTL) or sequentially (FN). It has been demonstrated that MTL and FN techniques are extremely useful in structure-property modeling on small and structurally diverse data sets, when conventional STL modeling is unable to produce any predictive model. The predictive STL individual models were obtained for 4 out of 11 TAPC, whereas application of inductive knowledge transfer techniques resulted in models for 9 TAPC. Differences in prediction performances of the models as a function of the machine-learning method, and of the number of properties simultaneously involved in the learning, has been discussed.

Stochastic versus stepwise strategies for quantitative structure-activity relationship generation--how much effort may the mining for successful QSAR models take?

Descriptor selection in QSAR typically relies on a set of upfront working hypotheses in order to boil down the initial descriptor set to a tractable size. Stepwise regression, computationally cheap and therefore widely used in spite of its potential caveats, is most aggressive in reducing the effectively explored problem space by adopting a greedy variable pick strategy. This work explores an antipodal approach, incarnated by an original Genetic Algorithm (GA)-based Stochastic QSAR Sampler (SQS) that favors unbiased model search over computational cost. Independent of a priori descriptor filtering and, most important, not limited to linear models only, it was benchmarked against the ISIDA Stepwise Regression (SR) tool. SQS was run under various premises, varying the training/validation set splitting scheme, the nonlinearity policy, and the used descriptors. With the considered three anti-HIV compound sets, repeated SQS runs generate sometimes poorly overlapping but nevertheless equally well validating model sets. Enabling SQS to apply nonlinear descriptor transformations increases the problem space: nevertheless, nonlinear models tend to be more robust validators. Model validation benchmarking showed SQS to match the performance of SR or outperform it in cases when the upfront simplifications of SR "backfire", even though the robust SR got trapped in local minima only once in six cases. Consensus models from large SQS model sets validate well--but not outstandingly better than SR consensus equations. SQS is thus a robust QSAR building tool according to standard validation tests against external sets of compounds (of same families as used for training), but many of its benefits/drawbacks may yet not be revealed by such tests. SQS results are a challenge to the traditional way to interpret and exploit QSAR: how to deal with thousands of well validating models, nonetheless providing potentially diverging applicability ranges and predicted values for external compounds. SR does not impose such burden on the user, but is "betting" on a single equation or a narrow consensus model to behave properly in virtual screening a sound strategy? By posing these questions, this article will hopefully act as an incentive for the long-haul studies needed to get them answered.