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..
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. |