generation of dual-target compounds using artificial intelligence.

Drugs that interact with multiple therapeutic targets are potential high-value products in polypharmacology-based drug discovery, but the rational design remains a formidable challenge. Here, we present artificial intelligence (AI)-based methods to design the chemical structures of compounds that interact with multiple therapeutic target proteins. The molecular structure generation is performed by a fragment-based approach using a genetic algorithm with chemical substructures and a deep learning approach using reinforcement learning with stochastic policy gradients in the framework of generative adversarial networks.

Density functional study of the phase stability and Raman spectra of YbO, YbSiO and YbSiO under pressure.

The phase stability and Raman spectra of Yb2O3, Yb2SiO5 and Yb2Si2O7 under hydrostatic pressure are investigated using density functional theory calculations. The calculated energies of polymorphs of each compound show that the stable phases at zero pressure, viz., C-type Yb2O3, X2-Yb2SiO5 and β-Yb2Si2O7, exhibit a pressure-induced phase transition as compressive pressure increases, which is consistent with available experimental data.