Insights into performance evaluation of com-pound-protein interaction prediction methods. (arXiv:2202.00001v1 [q-bio.QM])

Motivation: Machine learning based prediction of compound-protein
interactions (CPIs) is important for drug design, screening and repurposing
studies and can improve the efficiency and cost-effectiveness of wet lab
assays. Despite the publication of many research papers reporting CPI
predictors in the recent years, we have observed a number of fundamental issues
in experiment design that lead to over optimistic estimates of model
performance. Results: In this paper, we analyze the impact of several important
factors affecting generalization perfor-mance of CPI predictors that are
overlooked in existing work: 1. Similarity between training and test examples
in cross-validation 2. The strategy for generating negative examples, in the
absence of experimentally verified negative examples. 3. Choice of evaluation
protocols and performance metrics and their alignment with real-world use of
CPI predictors in screening large compound libraries. Using both an existing
state-of-the-art method (CPI-NN) and a proposed kernel based approach, we have
found that assessment of predictive performance of CPI predictors requires
careful con-trol over similarity between training and test examples. We also
show that random pairing for gen-erating synthetic negative examples for
training and performance evaluation results in models with better
generalization performance in comparison to more sophisticated strategies used
in existing studies. Furthermore, we have found that our kernel based approach,
despite its simple design, exceeds the prediction performance of CPI-NN. We
have used the proposed model for compound screening of several proteins
including SARS-CoV-2 Spike and Human ACE2 proteins and found strong evidence in
support of its top hits. Availability: Code and raw experimental results
available at Contact:
[email protected]



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