Practical Graph Neural Networks for Molecular Machine Learning

Chemical fingerprints [1] have long been the representation used to represent chemical structures as numbers, which are suitable inputs to machine learning models. A brief summary of chemical fingerprints is provided in another of my blog posts here. Above, we computed the fingerprint for Atorvastatin, a drug which generated over $100B in revenue over 2003–2013. At some point a few years ago, people started to realize [3] that instead of computing a non-differentiable fingerprint, we can compute a differentiable fingerprint. Then, by backpropagation, we can train not only a deep-learning model but also train the fingerprint-generating function itself. The promise would be to learn richer molecular representations.