New avenues in artificial-intelligence- assisted drug discovery

Over the past decade, the amount of biomedical data available has grown at unprecedented rates. Increased automation tech-nology and larger data volumes have encouraged the use of machine learning (ML) or artificial intelligence (AI) techniques for mining such data and extracting useful patterns. Because the identification of chemical entities with desired biological activ-ity is a crucial task in drug discovery, AI technologies have the potential to accelerate this process and support decision making. In addition, the advent of deep learning (DL) has shown great promise in addressing diverse problems in drug discovery, such as de novo molecular design. Herein, we will appraise the current state-of-the-art in AI-assisted drug discovery, discussing the recent applications covering generative models for chemical structure generation, scoring functions to improve binding affinity and pose prediction, and molecular dynamics to assist in the parametrization, featurization and generalization tasks. Finally, we will discuss current hurdles and the strategies to overcome them, as well as potential future directions.

Automated summary

In the past decade, the availability of biomedical data has grown rapidly. Machine learning and artificial intelligence techniques have been widely used to mine these data and extract useful patterns. These technologies have the potential to accelerate drug discovery and support decision making.