Deep Learning Methods for Small Molecule Drug Discovery: A Survey
It addresses the need for an integrated overview in the drug discovery community, but it is incremental as it synthesizes existing work without introducing new methods.
This paper provides a comprehensive survey of deep learning applications in small molecule drug discovery, covering molecule generation, property prediction, retrosynthesis, and reaction prediction, and discusses relationships among these tasks while reviewing literature, benchmarks, and performance comparisons.
With the development of computer-assisted techniques, research communities including biochemistry and deep learning have been devoted into the drug discovery field for over a decade. Various applications of deep learning have drawn great attention in drug discovery, such as molecule generation, molecular property prediction, retrosynthesis prediction, and reaction prediction. While most existing surveys only focus on one of the applications, limiting the view of researchers in the community. In this paper, we present a comprehensive review on the aforementioned four aspects, and discuss the relationships among different applications. The latest literature and classical benchmarks are presented for better understanding the development of variety of approaches. We commence by summarizing the molecule representation format in these works, followed by an introduction of recent proposed approaches for each of the four tasks. Furthermore, we review a variety of commonly used datasets and evaluation metrics and compare the performance of deep learning-based models. Finally, we conclude by identifying remaining challenges and discussing the future trend for deep learning methods in drug discovery.