We introduce a deep learning architecture for structure-based virtual screening that generates fixed-sized fingerprints of proteins and small molecules by applying learnable atom convolution and softmax operations to each molecule separately. These fingerprints are further non-linearly transformed, their inner product is calculated and used to predict the binding potential. Moreover, we show that widely used benchmark datasets may be insufficient for testing structure-based virtual screening methods that utilize machine learning. Therefore, we introduce a new benchmark dataset, which we constructed based on DUD-E, MUV and PDBBind databases.

Supplementary data are available at Bioinformatics online.

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Within the general conformal transformation method a simplified analytical model is proposed to study the effect of external hydrostatic pressure on low- and high-temperature superconducting systems. A single fluctuation in the density of states, placed away from the Fermi level, as well as external pressure are included in the model to derive equations for the superconducting gap, free energy difference, and specific heat difference. The zero- and sub-critical temperature limits are discussed by the method of successive approximations. The critical temperature is found as a function of high external pressure. It is shown that there are four universal types of the response of the system, in terms of dependence of the critical temperature on increasing external pressure. Some effects, which should be possible to be observed experimentally in s-wave superconductors, the cuprates (i.e. high-Tc superconductors) and other superconducting materials of the new generation such as two-gap superconductors, are revealed and discussed. An equation for the ratio  ≡ 2Δ(0)/Tc, as a function of the introduced parameters, is derived and solved numerically. Analysis of other thermodynamic quantities and the characteristic ratio  ≡ ΔC(Tc)/CN(Tc) is performed numerically, and mutual relations between the discussed quantities are investigated. The simple analytical model presented in the paper may turn out to be helpful in searching for novel superconducting components with higher critical temperatures induced by pressure effects.

Supplementary data are available at Bioinformatics online.

The knowledge extraction is an important element of the e-Health system. In this paper, we introduce a new method for decision rules extraction called Graph-based Rules Inducer to support the medical interview in the diabetes treatment. The emphasis is put on the capability of hidden context change tracking. The context is understood as a set of all factors affecting patient condition. In order to follow context changes, a forgetting mechanism with a forgetting factor is implemented in the proposed algorithm. Moreover, to aggregate data, a graph representation is used and a limitation of the search space is proposed to protect from overfitting. We demonstrate the advantages of our approach in comparison with other methods through an empirical study on the Electricity benchmark data set in the classification task. Subsequently, our method is applied in the diabetes treatment as a tool supporting medical interviews.

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