Testing the limits of SMILES-based de novo molecular generation with curriculum and deep reinforcement learning
Maranga Mokaya(University of Oxford), Charlotte M. Deane(Oxford Research Group), Fergus Imrie(University of Oxford), A.R. Bradley(University of Oxford), Willem P. van Hoorn, Aleksandra Kalisz
Cited by 46
Related Papers
HOMSTRAD: A database of protein structure alignments for homologous families
|Protein Science|1998|510
PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences
|Chemical Science|2023|303
Deep Generative Models for 3D Linker Design
|Journal of Chemical Information and Modeling|2020|266
Protein Family-Specific Models Using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data
|Journal of Chemical Information and Modeling|2018|138
Deep generative design with 3D pharmacophoric constraints
|Chemical Science|2021|110