most citedScalable Fragment-Based 3D Molecular Design with Reinforcement Learning

7 citations · 7 across the 1 of their papers we have counts for

collaborators

1 paper

cs.LG2022★ 7 cited

Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning

Daniel Flam-Shepherd, Alexander Zhigalin, Alán Aspuru-Guzik

Machine learning has the potential to automate molecular design and drastically accelerate the discovery of new functional compounds. Towards this goal, generative models and reinf…