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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
A Semi-supervised Molecular Learning Framework for Activity Cliff Estimation
Authors: Fang Wu
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on 30 activity cliff datasets demonstrate that Semi Mol significantly enhances graphbased ML architectures and outpasses state-of-the-art pretraining and SSL baselines. |
| Researcher Affiliation | Academia | Fang Wu Stanford University & Westlake University EMAIL |
| Pseudocode | Yes | Algorithm 1 Semi Mol Algorithm |
| Open Source Code | No | The paper mentions that the Molecule ACE benchmarking platform is 'available on Github https://github.com/molML/Molecule ACE', but it does not state that the code for their proposed method, Semi Mol, is open-source or provide a link to it. |
| Open Datasets | Yes | All our evaluations in this section are performed on datasets from Molecule ACE (Activity Cliff Estimation), which is an open-access benchmarking platform and available on Github https://github.com/molML/Molecule ACE. |
| Dataset Splits | No | Typically, D is divided into the training and validation sets, denoted as Dtrain = xtrain i , ytrain i N1 i=1 and Dval = xval i , yval i N2 i=1, respectively. More experimental details and dataset statistics are elaborated on Appendix. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper refers to various models and architectures (e.g., GIN, GMT, GNNs) but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | No | The paper mentions that 'More experimental details and dataset statistics are elaborated on Appendix.' and does not provide specific hyperparameter values or detailed training configurations in the main text. |