Accurate Hit Estimation for Iterative Screening Using Venn-ABERS Predictors
| Autor: | Thierry Kogej, Henrik Linusson, Lars Carlsson, Ola Engkvist, Ernst Ahlberg, Ulf Johansson, Ruben Buendia, Paolo Toccaceli |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
General Chemical Engineering Drug Evaluation Preclinical Quantitative Structure-Activity Relationship Library and Information Sciences Machine learning computer.software_genre 01 natural sciences law.invention Machine Learning law 0103 physical sciences Estimation 010304 chemical physics Drug discovery business.industry Computational Biology General Chemistry 0104 chemical sciences Computer Science Applications High-Throughput Screening Assays 010404 medicinal & biomolecular chemistry Venn diagram Artificial intelligence business computer |
| Zdroj: | Journal of chemical information and modeling. 59(3) |
| ISSN: | 1549-960X |
| Popis: | Iterative screening has emerged as a promising approach to increase the efficiency of high-throughput screening (HTS) campaigns in drug discovery. By learning from a subset of the compound library, inferences on what compounds to screen next can be made by predictive models. One of the challenges of iterative screening is to decide how many iterations to perform. This is mainly related to difficulties in estimating the prospective hit rate in any given iteration. In this article, a novel method based on Venn-ABERS predictors is proposed. The method provides accurate estimates of the number of hits retrieved in any given iteration during an HTS campaign. The estimates provide the necessary information to support the decision on the number of iterations needed to maximize the screening outcome. Thus, this method offers a prospective screening strategy for early-stage drug discovery. |
| Databáze: | OpenAIRE |
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