QGFN: Controllable Greediness with Action Values
Authors: Elaine Lau, Stephen Lu, Ling Pan, Doina Precup, Emmanuel Bengio
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed methods on 5 standard tasks used in prior GFN works: the fragment-based molecular design task introduced by Bengio et al. [2021a], 2 RNA design tasks introduced by Sinai et al. [2020], a small molecule design task based on QM9 [Jain et al., 2023], as well as a bit sequence task from Malkin et al. [2022a], Shen et al. [2023]. The proposed method outperforms strong baselines, achieving high average rewards and discovering modes more efficiently, sometimes by a large margin. |
| Researcher Affiliation | Collaboration | Elaine Lau1 2 Stephen Zhewen Lu2 Ling Pan1 5 Doina Precup1 2 3 Emmanuel Bengio4 1Mila Québec AI Institute 2Mc Gill University 3Google Deepmind 4Valence Labs 5Hong Kong University of Science and Technology |
| Pseudocode | Yes | Algorithm 1 QGFN: Full training algorithm details |
| Open Source Code | Yes | Source code available at: https://github.com/yunglau/QGFN/ |
| Open Datasets | Yes | We evaluate the proposed methods on 5 standard tasks used in prior GFN works: the fragment-based molecular design task introduced by Bengio et al. [2021a], 2 RNA design tasks introduced by Sinai et al. [2020], a small molecule design task based on QM9 [Jain et al., 2023], as well as a bit sequence task from Malkin et al. [2022a], Shen et al. [2023]. |
| Dataset Splits | Yes | We evaluate the proposed methods on 5 standard tasks used in prior GFN works: the fragment-based molecular design task introduced by Bengio et al. [2021a], 2 RNA design tasks introduced by Sinai et al. [2020], a small molecule design task based on QM9 [Jain et al., 2023], as well as a bit sequence task from Malkin et al. [2022a], Shen et al. [2023]. |
| Hardware Specification | Yes | All of our experiments were conducted using A100 and V100 GPUs. |
| Software Dependencies | No | The paper mentions software and libraries like 'RDKit' and 'Vienna RNA', but it does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | Yes | Parameter Value Batch size 64 Number of steps 10,000 Optimizer Adam Number of Layers 4 Hidden Dim. Size 128 Number of Heads 2 Positional Embeddings Rotary Reward scaling β in Rβ 32 Learning rate 1 10 4 Z Learning rate 1 10 3 |