DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach.We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. 
DeepGraphMolGen
Interaction binding models are learned from binding data using graph convolution networks (GCNs). 
graph convolution networks (GCNs)
DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach.We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem.  Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. 
reinforcement learning
Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. 
graph convolution policy approach