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BMC Bioinformatics. 2018 Jun 13;19(Suppl 8):207. doi: 10.1186/s12859-018-2194-2.

In silico prediction of potential chemical reactions mediated by human enzymes.

Author information

1
School of Integrative Engineering, Chung-Ang University, Seoul, Republic of Korea.
2
School of Biological Sciences, Chonnam National University, Gwangju, Republic of Korea.
3
Department of Multimedia, Chonnam National University, Yeosu, Republic of Korea.
4
College of Industrial Sciences, Kongju National University, Yesan, Republic of Korea.
5
School of Integrative Engineering, Chung-Ang University, Seoul, Republic of Korea. blisszen@cau.ac.kr.

Abstract

BACKGROUND:

Administered drugs are often converted into an ineffective or activated form by enzymes in our body. Conventional in silico prediction approaches focused on therapeutically important enzymes such as CYP450. However, there are more than thousands of different cellular enzymes that potentially convert administered drug into other forms.

RESULT:

We developed an in silico model to predict which of human enzymes including metabolic enzymes as well as CYP450 family can catalyze a given chemical compound. The prediction is based on the chemical and physical similarity between known enzyme substrates and a query chemical compound. Our in silico model was developed using multiple linear regression and the model showed high performance (AUC = 0.896) despite of the large number of enzymes. When evaluated on a test dataset, it also showed significantly high performance (AUC = 0.746). Interestingly, evaluation with literature data showed that our model can be used to predict not only enzymatic reactions but also drug conversion and enzyme inhibition.

CONCLUSION:

Our model was able to predict enzymatic reactions of a query molecule with a high accuracy. This may foster to discover new metabolic routes and to accelerate the computational development of drug candidates by enabling the prediction of the potential conversion of administered drugs into active or inactive forms.

KEYWORDS:

Drug discovery; Enzyme reaction prediction; In silico model; Machine learning

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