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We have developed a deep generative model, generative tensorial reinforcement learning (GENTRL), for de novo small-molecule design. GENTRL optimizes synthetic feasibility, novelty, and biological activity. We used GENTRL to discover potent inhibitors of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, in 21 days. Four compounds were active in biochemical assays, and two were validated in cell-based assays. One lead candidate was tested and demonstrated favorable pharmacokinetics in mice. A machine learning model allows the identification of new small-molecule kinase inhibitors in days.

The possibility of pigment detection and recognition in different environments such as solvents or proteins is a challenging, and at the same time demanding, task. It may be needed in very different situations: from the nondestructive in situ identification of pigments in paintings to the early detection of fungal infection in major agro-industrial crops and products. So, we propose a prototype method, the key feature of which is a procedure analyzing the lineshape of a spectrum. The shape of the absorption spectrum corresponding to this transition strongly depends on the immediate environment of a pigment and can serve as a marker to detect the presence of a particular pigment molecule in a sample. Considering carotenoids as an object of study, we demonstrate that the combined operation of the differential evolution algorithm and semiclassical quantum modeling of the optical response based on a generalized spectral density (the number of vibronic modes is arbitrary) allows us to distinguish quantum models of the pigment for different solvents. Moreover, it is determined that to predict the optical properties of monomeric pigments in protein, it is necessary to create a database containing, for each pigment, in addition to the absorption spectra measured in a predefined set of solvents, the parameters of the quantum model found using differential evolution.

The integration of energy markets (gas and oil) is accompanied with growing imbalance in the economic interests pursued by member states of the Eurasian Economic Union (EAEU). The evaluation was based on price and synergy factors (economies of scale), and the production function was solved by building a linear regression model. The model construction involved calculating the indicators of GDP elasticity that reflect changes in consumption and foreign trade for the period 2007 to 2018 and assessing the effects of the price factor (indices of growth in oil and gas transit prices and oil and gas export prices) on GDP changes when a common gas or oil market is created. Finally, the priority conditions for the integration of EAEU gas and oil markets were ascertained to allow the design of optimal scenarios for the establishment of a shared electricity market. The findings confirmed a synergistic effect, which manifests as an increase in GDP growth rate for each of the EAEU member states. The proposed approach enables a comprehensive evaluation of integration effectiveness on the basis of available relevant data.