Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
Authors: Gregor Simm, Robert Pinsler, Jose Miguel Hernandez-Lobato
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments to evaluate the performance of the policy introduced in Section 3. In our experiments, we show that our agent can efficiently learn to solve these tasks from scratch by working in a translation and rotation invariant state-action space. |
| Researcher Affiliation | Academia | 1Department of Engineering, University of Cambridge, Cambridge, UK. |
| Pseudocode | No | The paper describes the methodology in text but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code of the agent and environment is available at https://github.com/gncs/molgym. |
| Open Datasets | Yes | We train the agent on the multi-bag task using all formulas contained in the QM9 dataset (Ruddigkeit et al., 2012; Ramakrishnan et al., 2014) with up to 5 atoms, resulting in 11 bags (see Table 1). |
| Dataset Splits | No | The paper describes training an RL agent to generate molecules but does not specify explicit train/validation/test dataset splits with percentages or sample counts for the data used to train or evaluate the agent. |
| Hardware Specification | Yes | Experiments were run on a 16-core Intel Xeon Skylake 6142 CPU with 2.6GHz and 96GB RAM. |
| Software Dependencies | Yes | Structures are considered valid if they can be successfully parsed by RDKIT (Landrum, 2019). |
| Experiment Setup | No | Details on the model architecture and hyperparameters are in the Appendix. |