  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.
