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Updated: Aug 2, 2026

Protein Target Prediction and Validation of Small Molecule Compound
Published on: February 23, 2024
Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results
Anthony E Klon1, Meir Glick, John W Davies
1Novartis Institute for Biomedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA. anthony.klon@pharma.novartis.com
Abstract:
We have previously shown that a machine learning technique can improve the enrichment of high-throughput docking (HTD) results. In the previous cases studied, however, the application of a naive Bayes classifier failed to improve enrichment for instances where HTD alone was unable to generate an acceptable enrichment. We present here a protocol to rescue poor docking results a priori using a combination of rank-by-median consensus scoring and naive Bayesian categorization.
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