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Updated: Jul 4, 2026
Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
Published on: December 1, 2020
poolHiTS: a shifted transversal design based pooling strategy for high-throughput drug screening
Raghunandan M Kainkaryam1, Peter J Woolf
1Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA. raghu@umich.edu
Background:
A key goal of drug discovery is to increase the throughput of small molecule screens without sacrificing screening accuracy. High-throughput screening (HTS) in drug discovery involves testing a large number of compounds in a biological assay to identify active compounds. Normally, molecules from a large compound library are tested individually to identify the activity of each molecule. Usually a small number of compounds are found to be active, however the presence of false positive and negative testing errors suggests that this one-drug one-assay screening strategy can be significantly improved. Pooling designs are testing schemes that test mixtures of compounds in each assay, thereby generating a screen of the whole compound library in fewer tests. By repeatedly testing compounds in different combinations, pooling designs also allow for error-correction. These pooled designs, for specific experiment parameters, can be simply and efficiently created using the Shifted Transversal Design (STD) pooling algorithm. However, drug screening contains a number of key constraints that require specific modifications if this pooling approach is to be useful for practical screen designs.
Results:
In this paper, we introduce a pooling strategy called poolHiTS (Pooled High-Throughput Screening) which is based on the STD algorithm. In poolHiTS, we implement a limit on the number of compounds that can be mixed in a single assay. In addition, we show that the STD-based pooling strategy is limited in the error-correction that it can achieve. Due to the mixing constraint, we show that it is more efficient to split a large library into smaller blocks of compounds, which are then tested using an optimized strategy repeated for each block. We package the optimal block selection algorithm into poolHiTS. The MATLAB codes for the poolHiTS algorithm and the corresponding decoding strategy are also provided.
Conclusion:
We have produced a practical version of STD algorithm for pooled drug screens. This pooling strategy provides both assay compression and error-correction capabilities that can both accelerate and reduce the overall cost of HTS in drug discovery.
