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We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from binding data using graph convolution networks (GCNs). Since the experimentally obtained property scores are recognised as having potentially gross errors, we adopted a robust loss for the model. Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. Some of the molecules generated, while legitimate chemically, can have excellent drug-likeness scores but appear unusual. We provide an example based on the binding potency of small molecules to dopamine transporters. We extend our method successfully to use a multi-objective reward function, in this case for generating novel molecules that bind with dopamine transporters but not with those for norepinephrine. Our method should be generally applicable to the generation in silico of molecules with desirable properties.
Khemchandani, Y., O’Hagan, S., Samanta, S., Swainston, N., Roberts, T. J., Bollegala, D., & Kell, D. B. (2020). DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: A graph convolution and reinforcement learning approach. Journal of Cheminformatics, 12(1). https://doi.org/10.1186/s13321-020-00454-3