Maximizing gain in high-throughput screening using conformal prediction.

ABSTRACT: Iterative screening has emerged as a promising approach to increase the efficiency of screening campaigns compared to traditional high throughput approaches. By learning from a subset of the compound library, inferences on what compounds to screen next can be made by predictive models, resulting in more efficient screening. One way to evaluate screening is to consider the cost of screening compared to the gain associated with finding an active compound. In this work, we introduce a conformal predictor coupled with a gain-cost function with the aim to maximise gain in iterative screening. Using this setup we were able to show that by evaluating the predictions on the training data, very accurate predictions on what settings will produce the highest gain on the test data can be made. We eval

SUBMITTER: Svensson F 

PROVIDER: S-EPMC5821614 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature