Bayesian Multi Plate High Throughput Screening of Compounds
This addresses the need for more robust and flexible statistical tools in drug discovery, particularly for handling large-scale compound screening data, though it is an incremental improvement over existing methods.
The paper tackles the problem of high-throughput screening in drug discovery by introducing a Bayesian nonparametric framework that identifies candidate hits from multiple plates simultaneously, sharing statistical strength among plates. Experiments show significant improvements in hit identification sensitivity and specificity over the standard B-score method.
High throughput screening of compounds (chemicals) is an essential part of drug discovery [7], involving thousands to millions of compounds, with the purpose of identifying candidate hits. Most statistical tools, including the industry standard B-score method, work on individual compound plates and do not exploit cross-plate correlation or statistical strength among plates. We present a new statistical framework for high throughput screening of compounds based on Bayesian nonparametric modeling. The proposed approach is able to identify candidate hits from multiple plates simultaneously, sharing statistical strength among plates and providing more robust estimates of compound activity. It can flexibly accommodate arbitrary distributions of compound activities and is applicable to any plate geometry. The algorithm provides a principled statistical approach for hit identification and false discovery rate control. Experiments demonstrate significant improvements in hit identification sensitivity and specificity over the B-score method, which is highly sensitive to threshold choice. The framework is implemented as an efficient R extension package BHTSpack and is suitable for large scale data sets.