In this paper, we investigate potential biases in datasets used to make drug binding predictions using machine learning. We investigate a recently published metric called the Asymmetric Validation Embedding (AVE) bias which is used to quantify this bias and detect overfitting. We compare it to a slightly revised version and introduce a new weighted metric. We find that the new metrics allow to quantify overfitting while not overly limiting training data and produce models with greater predictive value.
CITATION STYLE
Davis, B., Mcloughlin, K., Allen, J., & Ellingson, S. R. (2020). Quantifying overfitting potential in drug binding datasets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12139 LNCS, pp. 585–598). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-50420-5_44
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