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Prediction of biological activity for high-throughput screening using binary kernel discrimination.

G Harper¹, J Bradshaw, J C Gittins

¹GlaxoSmithKline Research and Development, Medicines Research Centre, Gunnels Wood Road, Stevenage SG1 2NY, U.K. gh75680@ggr.co.uk

Journal of Chemical Information and Computer Sciences

|October 18, 2001

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Binary kernel discrimination offers a robust quantitative method for analyzing high-throughput screening data in drug discovery. This approach effectively predicts compound activity, even with limited or noisy training data.

Area of Science:

Computational chemistry
Cheminformatics
Drug discovery

Background:

High-throughput screening (HTS) is crucial for drug discovery but requires quantitative analysis methods.
Predicting compound activity from screening data remains a challenge.

Purpose of the Study:

Introduce and evaluate binary kernel discrimination (BKD) as a quantitative method for HTS data analysis.
Compare BKD performance against existing methods like merged similarity search and neural networks.

Main Methods:

Binary kernel discrimination (BKD) algorithm.
Application to two datasets: monoamine oxidase inhibitors and an in-house enzyme assay.
Comparative analysis with merged similarity search and feedforward neural networks.

Related Experiment Videos

Main Results:

BKD demonstrates robust performance across varying training data quantities.
The method remains effective even when dealing with noisy screening data.
BKD shows competitive or superior performance compared to baseline methods.

Conclusions:

Binary kernel discrimination is a valuable tool for quantitative analysis of HTS results.
The technique aids in predicting compound activity and optimizing compound selection.
Pattern recognition methods are essential for advancing drug discovery efficiency.