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

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

Automated summary

The paper provides a comprehensive overview of the use of artificial intelligence systems, particularly neural networks, in drug design to identify chemical structures with medical relevance. Successful training of neural networks requires a large set of training data related to the chemical structure-biological activity relationship, which can be obtained from experimental measurements or appropriate quantum models. Recent advancements in computing power have led to rapid development in neural network systems and a growing interest in deep learning techniques, allowing for a new level of abstraction in network modeling.