Search Results for author:
Found 3 papers, 2 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.
Improving Chemical Autoencoder Latent Space and Molecular De novo Generation Diversity with Heteroencoders
It is further explored to what extent translating between different chemical representations influences the latent space similarity to the SMILES strings or circular fingerprints.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
