Deep Learning Based Drug Metabolites Prediction.

Disha Wang,Zihao Shen,Lei Jiang,Shiliang Li,Honglin Li,Jie Wang,Wenjun Liu

doi:10.3389/fphar.2019.01586

Abstract

Drug metabolism research plays a key role in the discovery and development of drugs. Based on the discovery of drug metabolites, new chemical entities can be identified and potential safety hazards caused by reactive or toxic metabolites can be minimized. Nowadays, computational methods are usually complementary tools for experiments. However, current metabolites prediction methods tend to have high false positive rates with low accuracy and are usually only used for specific enzyme systems. In order to overcome this difficulty, a method was developed in this paper by first establishing a database with broad coverage of SMARTS-coded metabolic reaction rule, and then extracting the molecular fingerprints of compounds to construct a classification model based on deep learning algorithms. The metabolic reaction rule database we built can supplement chemically reasonable negative reaction examples. Based on deep learning algorithms, the model could determine which reaction types are more likely to occur than the others. In the test set, our method can achieve the accuracy of 70% (Top-10), which is significantly higher than that of random guess and the rule-based method SyGMa. The results demonstrated that our method has a certain predictive ability and application value.

Highlights

The discovery of small molecule drugs is time-consuming, expensive and labor-intensive. (Dickson and Gagnon, 2004; Paul et al, 2010; Dimasi et al, 2015) It is resource intensive, and involves typical timelines of 10–20 years and costs that range from US$0.5 billion to US$2.6 billion (Paul et al, 2010; Avorn, 2015)
In addition to economic and technical reasons, the main reason is that almost half of the candidate drugs failed in clinical trials
Natural logarithm of probability allocated to real metabolites

Summary

Introduction

The discovery of small molecule drugs is time-consuming, expensive and labor-intensive. (Dickson and Gagnon, 2004; Paul et al, 2010; Dimasi et al, 2015) It is resource intensive, and involves typical timelines of 10–20 years and costs that range from US$0.5 billion to US$2.6 billion (Paul et al, 2010; Avorn, 2015). The discovery of small molecule drugs is time-consuming, expensive and labor-intensive. (Dickson and Gagnon, 2004; Paul et al, 2010; Dimasi et al, 2015) It is resource intensive, and involves typical timelines of 10–20 years and costs that range from US$0.5 billion to US$2.6 billion (Paul et al, 2010; Avorn, 2015). Up to 25% of compounds were withdrawn due to metabolic, pharmacokinetic, or toxic problems (Hwang et al, 2016). Drug metabolism can produce metabolites with physicochemical and pharmacological properties, which are significantly different from the physical and pharmacological properties of parent drugs (Kirchmair et al, 2013). Allogeneic and metabolites are removed from liver and intestinal

Methods

Results

Conclusion