Molecular generation using gated graph convolutional neural networks and reinforcement learning
The design of molecules with bespoke chemical properties has wide-ranging applications in materials science, chemistry and drug-discovery. This can be formulated as a supervised learning problem, where we first seek to encode discrete molecular graphs to continuous latent representations, and then u...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2019
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| Online Access: | http://hdl.handle.net/10356/76936 |