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 [1].
Retrosynthesis Prediction with Conditional Graph Logic Network
Authors: Hanjun Dai, Chengtao Li, Connor Coley, Bo Dai, Le Song
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiment Dataset: We mainly evaluate our method on a benchmark dataset named USPTO-50k, which contains 50k reactions of 10 different types in the US patent literature. We use exactly the same training/validation/test splits as Coley et al. [8], which contain 80%/10%/10% of the total 50k reactions. Table 1 contains the detailed information about the benchmark. [...] We present the top-k exact match accuracy in Table 3, where k ranges from {1, 3, 5, 10, 20, 50}. |
| Researcher Affiliation | Collaboration | Hanjun Dai , Chengtao Li2, Connor W. Coley , Bo Dai , Le Song Google Research, Brain Team, EMAIL 2Galixir Inc., EMAIL Massachusetts Institute of Technology, EMAIL Georgia Institute of Technology, Ant Financial, EMAIL |
| Pseudocode | Yes | Algorithm 1 Importance Sampling for br ( ) |
| Open Source Code | Yes | Our code is released at https://github.com/Hanjun Dai/GLN. |
| Open Datasets | Yes | We mainly evaluate our method on a benchmark dataset named USPTO-50k, which contains 50k reactions of 10 different types in the US patent literature. We use exactly the same training/validation/test splits as Coley et al. [8], which contain 80%/10%/10% of the total 50k reactions. |
| Dataset Splits | Yes | We use exactly the same training/validation/test splits as Coley et al. [8], which contain 80%/10%/10% of the total 50k reactions. |
| Hardware Specification | Yes | It takes about 12 hours to train with a single GTX 1080Ti GPU. |
| Software Dependencies | No | The paper mentions software like 'RDKit' and 'rdchiral' ('We use rdchiral [31] to extract the retrosynthesis templates...'), but does not specify their version numbers or the versions of other dependencies like PyTorch, TensorFlow, or Python. |
| Experiment Setup | Yes | We train our model for up to 150k updates with batch size of 64. [...] We tune embedding sizes in {128, 256}, GNN layers {3, 4, 5} and GNN aggregation in {max, mean, sum} using validation set. |