In the latest years, the application of deep generative models to suggest virtual compounds
is becoming a new and powerful tool in drug discovery projects. The idea behind this review …

The growth of machine learning as a tool for research in computational chemistry is well
documented. For many years, this growth was heavily driven by the paradigms of supervised …

Deep learning shortens the cycle of the drug discovery for its success in extracting features
of molecules and proteins. Generating new molecules with deep learning methods could …

De novo drug design is crucial in advancing drug discovery, which aims to generate new
drugs with specific pharmacological properties. Recently, deep generative models have …

Many deep learning (DL)-based molecular generative models have been proposed to
design novel molecules. These models may perform well on benchmarks, but they usually …

Reinforcement learning (RL) has been applied to various domains in computational
chemistry and has found wide‐spread success. In this review, we first motivate the …

The flourishment of machine learning and deep learning methods has boosted the
development of cheminformatics, especially regarding the application of drug discovery and …

Designing reward functions that guide generative molecular design (GMD) algorithms to
desirable areas of chemical space is of critical importance in AI-driven drug discovery …