AI Case Study

From Atomwise's website: "Convolutional networks are known to achieve the best predictive performance in areas such as speech and image recognition by hierarchically composing simple local features into complex models. This idea was applied to chemistry to predict the bioactivity of small molecules for drug discovery applications. This research and subsequent developments led to AtomNet. AtomNet showed that the convolutional concepts of feature locality and hierarchical composition could be applied to the modeling of bioactivity and chemical interactions. Specifically, where an image is represented as a 2-dimensional grid of pixels containing channels for red, green, and blue colors, AtomNet represents a protein-ligand pair as a set of 3-dimensional volumetric pixels containing channels for carbon, oxygen, nitrogen, etc atom types. In this way, AtomNet autonomously learns the features governing molecular binding, and avoids the manual process of tweaking and over-parameterizing binding features that typified traditional computational methods. AtomNet’s application of local convolutional filters to structural target information was able to successfully predict new active molecules for targets with no previously known modulators. Biochemical interactions are primarily local, and can be modelled by similarly-constrained machine learning architectures."