- GCPN: Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation, You et al 2018, NIPS.
- MolDQN: Optimization of Molecules via Deep Reinforcement Learning, Zhou et al 2018, Sci. Rep.
- MolGAN: MolGAN: An implicit generative model for small molecular graphs, De Cao et al 2018, ICML.
- MolGym: Reinforcement Learning for Molecular Design Guided by Quantum Mechanics, N. C. Simm et al 2020, ICML.
- REINVENT: REINVENT 2.0: An AI Tool for De Novo Drug Design, Blaschke et al 2020, J Chem Inf Model.
- RationaleRL: Multi-Objective Molecule Generation using Interpretable Substructures, Jin et al 2020, ICML.
- MCMG: Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning, Wang et al 2020, Nat. Mach. Intell.
- DeepLigBuilder: Structure-based de novo drug design using 3D deep generative models, Li et al 2021, Chem. Sci.
- GEGL: Guiding Deep Molecular Optimization with Genetic Exploration (neurips.cc), Ahn et al 2020, NIPS.
- MOLER: MOLER: Incorporate Molecule-Level Reward to Enhance Deep Generative Model for Molecule Optimization, Fu et al 2022, IEEE Trans Knowl Data Eng.
- PROTAC-RL: Accelerated rational PROTAC design via deep learning and molecular simulations, Zheng et al 2022, Nat. Mach. Intell.