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Combinatorial Chemistry & High Throughput Screening

Editor-in-Chief

ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

Research Article

Molecular Docking for Prediction and Interpretation of Adverse Drug Reactions

Author(s): Heng Luo, Achille Fokoue-Nkoutche, Nalini Singh, Lun Yang, Jianying Hu and Ping Zhang*

Volume 21, Issue 5, 2018

Page: [314 - 322] Pages: 9

DOI: 10.2174/1386207321666180524110013

Price: $65

Abstract

Aim and Objective: Adverse drug reactions (ADRs) present a major burden for patients and the healthcare industry. Various computational methods have been developed to predict ADRs for drug molecules. However, many of these methods require experimental or surveillance data and cannot be used when only structural information is available.

Materials and Methods: We collected 1,231 small molecule drugs and 600 human proteins and utilized molecular docking to generate binding features among them. We developed machine learning models that use these docking features to make predictions for 1,533 ADRs.

Results: These models obtain an overall area under the receiver operating characteristic curve (AUROC) of 0.843 and an overall area under the precision-recall curve (AUPR) of 0.395, outperforming seven structural fingerprint-based prediction models. Using the method, we predicted skin striae for fluticasone propionate, dermatitis acneiform for mometasone, and decreased libido for irinotecan, as demonstrations. Furthermore, we analyzed the top binding proteins associated with some of the ADRs, which can help to understand and/or generate hypotheses for underlying mechanisms of ADRs.

Conclusion: Machine learning combined with molecular docking can help to predict ADRs for drug molecules and provide possible explanations for the ADR mechanisms.

Keywords: Molecular docking, chemical-protein interactome, machine learning, prediction, side effects, adverse drug reactions.


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