In this section, we are going to discuss how reinforcement learning (RL) is used to generate molecules that not only will bind strongly to the receptor but also will have the desired property. Note that while atom-type rules have been hard-coded into the program, there is flexibility in the choice of atom-type at many stages in the growth of the molecule. We realized that any change in the choice of atom-type affects the kinds of molecules generated. In our approach discussed above, atom-types were chosen randomly as long as they conform to some objective rules. We modify that choice here using a reinforcement algorithm, where a machinelearned agent chooses the next atom-type to optimize the generation of molecules with specific molecule-level properties such as synthesizability, logP values, drug-likeness, etc. The model is a Graph Convolution Policy Network (GCPN) , simplified slightly to suit SPOTLIGHT. FIG. A depiction of the entire flow of information through the policy network, with an example molecule that is partially grown. (a) The "encoder" deals with incorporating neighborhood information for the focal atom. (b) The "decoder" uses the combined information of the point of attachment and the newly added atom to decide a probability to pick that atom.