  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.
