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Found 4 papers, 3 papers with code
Learning to Navigate in Synthetically Accessible Chemical Space Using Reinforcement Learning
In this work, we propose a novel reinforcement learning (RL) setup for drug discovery that addresses this challenge by embedding the concept of synthetic accessibility directly into the de novo compound design system.
Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
Over the last decade, there has been significant progress in the field of machine learning for de novo drug design, particularly in deep generative models.
Large-scale 3D point cloud representations via graph inception networks with applications to autonomous driving
We present a novel graph-neural-network-based system to effectively represent large-scale 3D point clouds with the applications to autonomous driving.
Generalized Value Iteration Networks: Life Beyond Lattices
GVIN emulates the value iteration algorithm by using a novel graph convolution operator, which enables GVIN to learn and plan on irregular spatial graphs.
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