Ishaan Gupta, Rajan
We lifted 5 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| theochem/B3DB | canonical | 0 of 4 |
| Qiong-Yang/GNN-TL | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| MolecularGraphNeuralNetwork | Not yet run | Qiong-Yang/GNN-TL/model/transferlearning.py pointer only (licence: NONE) · get_code("e7f761187beea928") |
| add_isomeric | Not yet run | theochem/B3DB/data_curation/cleaning/01_combine_clean_rest_api_v4.py code served (permissive licence) · get_code("1f7348629786ba62") |
| append_value | Not yet run | theochem/B3DB/data_curation/grouping/classification_grouping.py code served (permissive licence) · get_code("455237cc25932ba4") |
| combine_excels | Not yet run | theochem/B3DB/data_curation/preprocessing/combine_clean.py code served (permissive licence) · get_code("6d8e2c0619dfddef") |
| split_regression_classification | Not yet run | theochem/B3DB/data_curation/preprocessing/combine_clean.py code served (permissive licence) · get_code("c37c243459821bda") |
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Molecules are often represented as SMILES strings, which can be readily converted to hand-crafted descriptors or fingerprints (FP) for molecular property prediction. Research has demonstrated that SMILES can be converted to molecular graphs G = (V, E), with atoms as nodes (V ) and bonds as edges (E). These molecular graphs can subsequently be used to train graph neural networks (GNN) models. Despite the recent surge in application of GNN (existing and novel architectures) for molecular property prediction, a rigorous benchmark is still lacking. We propose MolGraphBench (https://github. com/rajanbit/MolGraphBench), a comprehensive benchmark of four commonly used GNN models for molecular property prediction. Benchmarking results demonstrate graph convolutional network (GCN) and graph isomorphism networks (GIN) as the optimal GNN architectures for molecular graph regression tasks, based on absolute performance, training efficiency, transfer learning and prediction quality. The study also indicates the non-complementary nature of molecular fingerprints in the fusion (GNN-FP) framework. Furthermore, our GNN models achieved performance superior or comparable performance to current state-of-the-art GNN baselines across three datasets (GCN with RMSE of 0.518 on B3DB, GIN-FP with RMSE of 1.022 on FreeSolv and GIN with MAE of 63.783 on RT datasets). Findings from this study indicate that type of GNN-layer, should be treated as a tunable hyperparameter rather than a fixed design choice to achieve superior performance.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2602.20573")
get_code_for_paper("2602.20573")
have("2602.20573")
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