Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship

TLDR

Neural networks, long known but recently accelerated by increased computing power, now benefit from deep learning techniques that enable complex, human‑like associations between seemingly distant phenomena. The paper provides a comprehensive overview of using AI systems in drug design. The authors employ neural networks trained on chemical compound data—sourced from experiments or quantum models—to identify medically relevant chemical structures. Studies demonstrate that neural networks can generalize from relatively narrow training data to predict chemical structure–biological activity relationships. The article is categorized under Computer and Information Science > Chemoinformatics.

Abstract

Abstract The paper presents a comprehensive overview of the use of artificial intelligence (AI) systems in drug design. Neural networks, which are one of the systems employed in AI, are used to identify chemical structures that can have medical relevance. Successful training of neural networks must be preceded by the acquisition of relevant information about chemical compounds, functional groups, and their possible biological activity. In general, a neural network requires a large set of training data, which must contain information about the chemical structure–biological activity relationship. The data can come from experimental measurements, but can also be generated using appropriate quantum models. In many of the studies presented below, authors showed a significant potential of neural networks to produce generalizations based on even relatively narrow training data. Despite the fact that neural network systems have been known for more than 40 years, it is only recently that they have seen rapid development due to the wider availability of computing power. In recent years, there has been a growing interest in deep learning techniques, bringing network modeling to a new level of abstraction. Deep learning allows combining what seems to be causally distant phenomena and effects, and to associate facts in a way resembling the human mind. This article is categorized under: Computer and Information Science > Chemoinformatics