Analysis of the GC-MS of volatile compounds and the phytochemical profile and antioxidant activities of some Bulgarian medicinal plants.

The study's objective was to investigate the volatile compounds, assess the total phenolic content and phenolic acids profile, determine the antioxidant capacity and evaluate the anthocyanin and flavonoid contents in stinging nettle (Urtica dioica L.), tansy (Tanacetum vulgare L.), bladder campion (Silene vulgaris (Moench) Garcke) and rosehip fruit (Rosa canina L.

Carotenoids, tocopherols, organic acids, charbohydrate and mineral content in different medicinal plant extracts.

Nettle (Urtica dioica L.), tansy (Tanacetum vulgare L.), bladder campion (Silene vulgaris (Moench) Garcke, waterpepper (Polygonum hydropiper L.

Deep reinforcement learning for de novo drug design.

We have devised and implemented a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). On the basis of deep and reinforcement learning (RL) approaches, ReLeaSE integrates two deep neural networks-generative and predictive-that are trained separately but are used jointly to generate novel targeted chemical libraries. ReLeaSE uses simple representation of molecules by their simplified molecular-input line-entry system (SMILES) strings only.