Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery. (arXiv:2106.02190v4 [cs.LG] UPDATED)

We developed Distilled Graph Attention Policy Network (DGAPN), a
reinforcement learning model to generate novel graph-structured chemical
representations that optimize user-defined objectives by efficiently navigating
a physically constrained domain. The framework is examined on the task of
generating molecules that are designed to bind, noncovalently, to functional
sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT)
mechanism that leverages self-attention over both node and edge attributes as
well as encoding the spatial structure -- this capability is of …

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