Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
QGFN: Controllable Greediness with Action Values
Authors: Elaine Lau, Stephen Lu, Ling Pan, Doina Precup, Emmanuel Bengio
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 30,387 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,403 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 |