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ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics.

Jiangming Sun¹, Nina Jeliazkova², Vladimir Chupakin³

¹Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&D Gothenburg, 43183 Mölndal, Sweden.

Journal of Cheminformatics

|March 21, 2017

Summary

This study compiles a large chemogenomics dataset from public databases, containing over 70 million structure-activity relationship data points. This resource supports building accurate in silico target prediction and polypharmacology models.

Keywords:

Big Data Bioactivity Chemical structure Chemogenomics Molecular fingerprints QSAR Search engine

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Area of Science:

Computational chemistry and cheminformatics
Drug discovery and development
Bioinformatics and data science

Background:

Chemogenomics data is crucial for developing in silico target prediction models.
The growing volume of chemogenomics data presents opportunities for Big Data approaches.
High-quality datasets are essential for effective model building.

Purpose of the Study:

To compile a comprehensive, high-quality chemogenomics dataset.
To create an industry-scale resource for computational drug discovery.
To facilitate the development and validation of in silico prediction models.

Main Methods:

Integrated data from public databases (PubChem, ChEMBL).
Collected over 70 million structure-activity relationship (SAR) data points.
Included chemical structures, target information, and activity annotations.

Main Results:

A large-scale chemogenomics dataset with over 70 million SAR data points.
A valuable resource for in silico polypharmacology and off-target effect prediction.
A foundation for validating cheminformatics approaches.

Conclusions:

The compiled dataset is a significant resource for advancing computational drug discovery.
Enables the development of more robust predictive models.
Supports the validation of various cheminformatics methodologies.