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Making suitable modeling choices is crucial for successful in silico drug design, and one of the most important of these is the proper extraction and curation of data from qHTS screens, and the use of optimized statistical learning methods to obtain valid models. More specifically, we aim to learn the top‐1 % most potent compounds against a variety of targets in a procedure we call virtual screening...
Classification algorithms suffer from the curse of dimensionality, which leads to overfitting, particularly if the problem is over‐determined. Therefore it is of particular interest to identify the most relevant descriptors to reduce the complexity. We applied Bayesian estimates to model the probability distribution of descriptors values used for binary classification using n‐fold cross‐validation...
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