Maximizing Gain in HTS Screening Using Conformal Prediction
[edit]
Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications, PMLR 60:103117, 2017.
Abstract
Today, screening of large compound collections in high throughput screening campaigns form the backbone of early drug discovery.
Although widely applied, this approach is resource and potentially labour intensive.
Therefore, improved computational approaches to streamline screening is in high demand.
In this study we introduce conformal prediction paired with a gaincost function to make predictions
in order to maximise the gain of screening campaigns on new screening sets.
Our results indicate that using 20\% of the screening library as an initial screening set
and using the data obtained together with a gaincost function,
the significance level of the predictor that maximise the gain can be identified.
Importantly, the parameters for the predictor derived from the initial screening set was highly predictive of the maximal gain also on the remaining data.
Using this approach, the gain of a screening campaign can be improved considerably.
Related Material


