MTPNet: Multi-Grained Target Perception for Unified Activity Cliff Prediction
Authors: Zishan Shu, Yufan Deng, Hongyu Zhang, Zhiwei Nie, Jie Chen
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on 30 representative activity cliff datasets demonstrate that MTPNet significantly outperforms previous approaches, achieving an average RMSE improvement of 18.95% on top of several mainstream GNN architectures. |
| Researcher Affiliation | Academia | 1School of Electronic and Computer Engineering, Peking University, Shenzhen, China 2Pengcheng Laboratory, Shenzhen, China 3AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using prose, equations (Eq. 5 to Eq. 13), and architectural diagrams (Figure 3), but it does not contain a clearly labeled pseudocode block or algorithm. |
| Open Source Code | Yes | Codes are available at: https://github.com/Zishan Shu/MTPNet. |
| Open Datasets | Yes | All evaluations in this section are conducted on datasets from Molecule ACE (Activity Cliff Estimation) [Van Tilborg et al., 2022], an open-access benchmarking platform available on Git Hub at https://github.com/mol ML/Molecule ACE. [...] Specifically, we conducted experiments on the CYP3A4 dataset [Rao et al., 2022] (see Table 2), which includes activity cliff data of Cytochrome P450 3A4 inhibitors/substrates experimentally measured by Veith et al. (2009) [Veith et al., 2009] |
| Dataset Splits | No | The paper mentions that "12 datasets contain fewer than 1,000 molecules in the training set" and provides total sample counts for the CYP3A4 dataset ("3,626 active inhibitors/substrates and 5,496 inactive compounds"). However, it does not specify the exact percentages or counts for training, validation, and test splits required for reproducibility across all datasets used. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions models like ESM2 and Mole-BERT but does not provide specific version numbers for these or any other software libraries, frameworks, or programming languages used in the implementation. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs), optimizer settings, or other training configurations in the main text. |