Message-Passing 33 combines information from neighboring nodes to enrich the information in each node. Iterative message passing can also be used to incorporate information from farther nodes. In this work, we show the use of graph convolution in encoding neighborhood information into the focal atom. This part of the model is called the "encoder". The encoded information is then utilized by a simple fully connected layer (the "decoder") to produce probabilities as output. Figure shows how the model is set up. The first step is encoding the molecule. We use one-hot encoding as node features for the atoms. For one-hot encoding, the list of all atom-types is placed in an ordered list. To convert an atom of type "T" to its corresponding vector, we construct the vector representation as follows: