Retrosynthetic Planning with Dual Value Networks
Authors: Guoqing Liu, Di Xue, Shufang Xie, Yingce Xia, Austin Tripp, Krzysztof Maziarz, Marwin Segler, Tao Qin, Zongzhang Zhang, Tie-Yan Liu
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On the widely-used USPTO dataset, our PDVN algorithm improves the search success rate of existing multi-step planners (e.g., increasing the success rate from 85.79% to 98.95% for Retro*, and reducing the number of model calls by half while solving 99.47% molecules for Retro Graph).To demonstrate the effectiveness of our PDVN algorithm, we conduct extensive experiments on the widely used USPTO dataset (Lowe, 2012; Chen et al., 2020). |
| Researcher Affiliation | Collaboration | 1Microsoft Research AI4Science 2National Key Laboratory for Novel Software Technology, Nanjing University 3Renmin University of China 4University of Cambridge. |
| Pseudocode | No | The paper describes the algorithm steps in detailed text and provides a flowchart in Figure 2, but it does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide an explicit statement or a link for the availability of its source code. |
| Open Datasets | Yes | On the widely-used USPTO dataset (Lowe, 2012; Chen et al., 2020). For the training target molecule dataset Dtrain, we follow the procedure from (Chen et al., 2020; Kim et al., 2021) and construct synthesis routes based on the publicly available reaction dataset extracted from the United States Patent Office (USPTO) (Lowe, 2012). |
| Dataset Splits | No | The paper mentions using "Dtrain" for generating simulated experiences and "Dtest" for evaluation, but it does not specify explicit train/validation/test dataset splits with percentages, counts, or predefined citations for model development or hyperparameter tuning. |
| Hardware Specification | Yes | The whole training process takes about 18 hours on a server with four NVIDIA TITAN Xp and 48 CPU cores (using 15 parallel workers). |
| Software Dependencies | No | The paper mentions "RDKit" and "Adam optimizer" but does not provide specific version numbers for these or any other software dependencies needed to replicate the experiments. |
| Experiment Setup | Yes | During the Planning phase, the batch size of sampled target molecules is 1024. We set crxn(s, a) = 0.1 and cdead = 5. During the Updating phase, the Adam optimizer (Kingma & Ba, 2014) with a mini-batch of size 128 and a learning rate of 0.001 is used for all models. Table 6. Hyper-parameters for PDVN planning. C (PUCT) 1.0, alpha (Synthesizability penalty) 0.8, MCTS depth 15, Number of simulations 100, cdead 5.0, crxn(s, a) 0.1 Table 7. Hyper-parameters for PDVN training. Training dataset size 299202, Batch size 1024, Optimizer Adam, Learning rate 1e-3, Dropout rate 0.1, Mini-batch size 128, SL epochs 8. |