Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Active Retrosynthetic Planning Aware of Route Quality
Authors: Luotian Yuan, Yemin Yu, Ying Wei, Yongwei Wang, Zhihua Wang, Fei Wu
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our framework to different existing approaches on both the benchmark and an expert dataset and demonstrate that it outperforms the existing state-of-the-art approach by 6.2% in route quality while reducing the query cost by 12.8%. [...] 4 EXPERIMENTS |
| Researcher Affiliation | Academia | Luotian Yuan1 , Yemin Yu2,4 , Ying Wei3 , Yongwei Wang1,4 , Zhihua Wang4, Fei Wu1,4 1Zhejiang University, 2City University of Hong Kong, 3Nanyang Technological University 4Shanghai Institute for Advanced Study of Zhejiang University |
| Pseudocode | Yes | Algorithm 1: Training algorithm |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing its code or a link to a code repository for its own implementation. |
| Open Datasets | Yes | We use two test sets to evaluate our methods. The first one is a widely used USPTO-50k benchmark dataset that has 178 hard molecules raised in Chen et al. (2020). [...] Initially, the method in Guo et al. (2020) is employed to pre-train a model utilizing the USPTO-MIT dataset, followed by the fine-tuning of the model in reactions derived from the high-quality, expert-annotated dataset. |
| Dataset Splits | Yes | We partition the expert dataset as 0.8/0.1/0.1 into train/valid/test sets. |
| Hardware Specification | No | The paper does not explicitly describe the hardware specifications (e.g., GPU models, CPU types, or cloud instance details) used for its experiments. |
| Software Dependencies | No | The paper mentions algorithms (e.g., TD3) and models (e.g., MLP, Morgan fingerprint) but does not provide specific version numbers for software dependencies or libraries used. |
| Experiment Setup | Yes | As for the hyper-parameters, we set the maximum route depth as 6 and λ in Eq. 1 as 4. [...] Hyperparameters Values Encoder layers 4 Decoder layers 4 Encoder embedding dimension 2048 Encoder FFN embedding dimension 2048 Encoder attention heads 8 Decoder embedding dimension 2048 Decoder FFN embedding dimension 2048 Decoder attention heads 8 Optimizer Adam Learning rate 1e-4 Weight decay 0.0001 N epochs 12 Clip norm 0.25 Dropout rate 0.1 |