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 gain-cost 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 gain-cost 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.
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