Using attribution to decode binding mechanism in neural network models for chemistry

Significance Advances in machine learning have led to neural network-based methods for virtual screening, making it possible to sift through trillions of small molecules to find those that are pharmacologically important. Such methods have the potential to make chemical discoveries, but only if it is possible to untangle why models make the predictions that they do. Here we use attribution methods to investigate neural network models for small-molecule binding, and show that, while it is possible to identify pharmacophores with attribution, there is also the real possibility that a model which seems to perform perfectly instead learns spurious correlations in the underlying dataset that have nothing to do with binding chemistry. We propose an attribution-based test for determining whether a model can learn a hypothesized binding mechanism on a training set. Deep neural networks have achieved state-of-the-art accuracy at classifying molecules with respect to whether they bind to specific protein targets. A key breakthrough would occur if these models could reveal the fragment pharmacophores that are causally involved in binding. Extracting chemical details of binding from the networks could enable scientific discoveries about the mechanisms of drug actions. However, doing so requires shining light into the black box that is the trained neural network model, a task that has proved difficult across many domains. Here we show how the binding mechanism learned by deep neural network models can be interrogated, using a recently described attribution method. We first work with carefully constructed synthetic datasets, in which the molecular features responsible for “binding” are fully known. We find that networks that achieve perfect accuracy on held-out test datasets still learn spurious correlations, and we are able to exploit this nonrobustness to construct adversarial examples that fool the model. This makes these models unreliable for accurately revealing information about the mechanisms of protein–ligand binding. In light of our findings, we prescribe a test that checks whether a hypothesized mechanism can be learned. If the test fails, it indicates that the model must be simplified or regularized and/or that the training dataset requires augmentation.