Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Fragment-based Pretraining and Finetuning on Molecular Graphs
Authors: Kha-Dinh Luong, Ambuj K Singh
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 23,345 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,928 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our graph fragment-based pretraining (Graph FP) advances the performances on 5 out of 8 common molecular benchmarks and improves the performances on long-range biological benchmarks by at least 11.5%. Code is available at: https://github.com/lvkd84/Graph FP. (Abstract) and '4 Experiments' |
| Researcher Affiliation | Academia | Kha-Dinh Luong, Ambuj Singh Department of Computer Science University of California, Santa Barbara Santa Barbara, CA 93106 EMAIL |
| Pseudocode | No | The paper describes the methodology using text and mathematical equations but does not include structured pseudocode or algorithm blocks with explicit labels such as 'Algorithm' or 'Pseudocode'. |
| Open Source Code | Yes | Code is available at: https://github.com/lvkd84/Graph FP. |
| Open Datasets | Yes | We use a processed subset containing 456K molecules from the Ch EMBL database [24] for pretraining. |
| Dataset Splits | Yes | For downstream evaluation, we consider 8 binary graph classification tasks from Molecule Net [36] with scaffold split [15]. Moreover, to assess the ability of the models in recognizing global arrangement, we consider two graph prediction tasks on large peptide molecules from the Long-range Graph Benchmark [7]. Long-range graph benchmarks are split using stratified random split. |
| Hardware Specification | Yes | All experiments are run on individual Tesla V 100 GPUs. |
| Software Dependencies | No | The paper mentions software components like GIN and Adam W optimizer but does not specify their version numbers or the versions of other key software dependencies (e.g., Python, PyTorch/TensorFlow libraries). |
| Experiment Setup | Yes | All pretrainings are done in 100 epochs, with Adam W optimizer, batch size 256, and initial learning rate 1 × 10−3. We reduce the learning rate by a factor of 0.1 every 5 epochs without improvement. On graph classification benchmarks, to ensure comparability, our finetuning setting is mostly similar to that of previous works [15, 23]: 100 epochs, Adam optimizer, batch size 256, initial learning rate 1 × 10−3, and dropout rate chosen from {0.0, 0.5}. |