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. We provide an example based on the binding potency of small molecules to dopamine transporters. Some of the molecules generated, while legitimate chemically, can have excellent drug-likeness scores but appear unusual. Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. Since the experimentally obtained property scores are recognised as having potentially gross errors, we adopted a robust loss for the model. Interaction binding models are learned from binding data using graph convolution networks (GCNs). We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. The Creative Commons Public Domain Dedication waiver ( ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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