Bayesian Multi Plate High Throughput Screening of Compounds
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Drug DiscoverySpecificityVocal Bursts Intensity PredictionSimilar Papers 제목 키워드 기반
Accelerating high-throughput virtual screening through molecular pool-based active learning
Structure-based virtual screening is an important tool in early stage drug discovery that scores the interactions between a target protein and candidate ligands. As virtual libraries continue to grow (in excess of $10^8$…
Active LearningBayesian OptimizationDrug DiscoveryVocal Bursts Intensity PredictionUsing Bayesian Optimization to Accelerate Virtual Screening for the Discovery of Therapeutics Appropriate for Repurposing for COVID-19
The novel Wuhan coronavirus known as SARS-CoV-2 has brought almost unprecedented effects for a non-wartime setting, hitting social, economic and health systems hard.~ Being able to bring to bear pharmaceutical interventi…
Bayesian OptimizationHigh Throughput Screening of Expression Constructs using Microcapillary Arrays
Gene expression is a complex phenomenon involving numerous interlinked variables, and studying these variables to control expression is essential in bioengineering and biomanufacturing. While cloning techniques for achie…
A Mathematical Model of Platelet Aggregation in an Extravascular Injury Under Flow
We present the first mathematical model of flow-mediated primary hemostasis in an extravascular injury, which can track the process from initial deposition to occlusion. The model consists of a system of ordinary differe…
Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction
The characterization of drug-protein interactions is crucial in the high-throughput screening for drug discovery. The deep learning-based approaches have attracted attention because they can predict drug-protein interact…
Bayesian InferenceDeep LearningDrug DiscoveryTransfer Learning