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Year : 2018  |  Volume : 50  |  Issue : 4  |  Page : 169-176

A big data approach with artificial neural network and molecular similarity for chemical data mining and endocrine disruption prediction

1 Research and Development Centre, Bharathiar University, Coimbatore, Tamil Nadu, India
2 Center for Research and PG Studies in Botany and Biotechnology, Thiagarajar College (Autonomous), Madurai, Tamil Nadu, India
3 Department of Biotechnology, Liatris Biosciences LLP, Kottayam, Kerala, India

Correspondence Address:
Mr. Renjith Paulose
Research and Development Centre, Bharathiar University, Coimbatore - 641 046, Tamil Nadu
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijp.IJP_304_17

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CONTEXT: Chemical toxicity prediction at early stage drug discovery phase has been researched for years, and newest methods are always investigated. Research data comprising chemical physicochemical properties, toxicity, assay, and activity details create massive data which are becoming difficult to manage. Identifying the desired featured chemical with the desired biological activity from millions of chemicals is a challenging task. AIMS: In this study, we investigate and explore big data technologies and machine learning approaches to do an efficient chemical data mining for endocrine receptor disruption prediction and virtual compound screening. The power of artificial neural network (ANN) in predicting chemicals' activity toward androgen receptor (AR) and estrogen receptor (ER) and thereby classifying into human endocrine disruptor or nondisruptor is investigated. SUBJECTS AND METHODS: Molecules are collected along with their Inhibitory Concentration (IC50) values toward AR and ER. Training and test datasets are created with active and inactive classes of molecules. Molecular fingerprints of Electro Topological State (E-State) are generated for describing every compound. ANN machine learning model is created using Apache Spark and implemented in Hadoop big data environment. Test chemical's structural similarity toward active class of training compounds is estimated and combined with ANN model for improving prediction accuracy. RESULTS: AR and ER predictive models applied on corresponding test datasets gave 86.31% and 89.57% accuracies, respectively, in correctly classifying molecules as disruptor or nondisruptor. Molecular fragments and functional groups are ranked based on their importance in forming ANN model and influence toward the AR and ER disruption behavior. Training molecules that are specific to the test molecules' endocrine disruption prediction are retrieved based on the structural similarity values. CONCLUSIONS: The current study demonstrates a new approach of chemical endocrine receptor disruption prediction combining ANN machine learning method and molecular similarity in a big data environment. This method of predictive modeling can be further tested with more receptors and hormones and predictive power can be examined.


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