Binding-Adaptive Diffusion Models for Structure-Based Drug Design
Authors: Zhilin Huang, Ling Yang, Zaixi Zhang, Xiangxin Zhou, Yu Bao, Xiawu Zheng, Yuwei Yang, Yu Wang, Wenming Yang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies on the Cross Docked2020 dataset show BINDDM can generate molecules with more realistic 3D structures and higher binding affinities towards the protein targets, with up to -5.92 Avg. Vina Score, while maintaining proper molecular properties. |
| Researcher Affiliation | Collaboration | Zhilin Huang1,2*, Ling Yang3*, Zaixi Zhang4, Xiangxin Zhou5, Yu Bao6, Xiawu Zheng2, Yuwei Yang6, Yu Wang2 , Wenming Yang1,2 1Shenzhen International Graduate School, Tsinghua University 2Peng Cheng Laboratory 3Peking University 4University of Science and Technology of China 5University of Chinese Academy of Sciences 6Byte Dance |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/Yang Ling0818/Bind DM |
| Open Datasets | Yes | As for molecular generation, following the previous work (Luo et al. 2021; Peng et al. 2022; Guan et al. 2023a), we train and evaluate BINDDM on the Cross Docked2020 dataset (Francoeur et al. 2020). |
| Dataset Splits | Yes | We follow the same data preparation and splitting as Luo et al. (2021), where the 22.5 million docked binding complexes are refined to high-quality docking poses (RMSD between the docked pose and the ground truth < 1 A) and diverse proteins (sequence identity < 30%). This produces 100, 000 protein-ligand pairs for training and 100 proteins for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings in the main text. It mentions following procedures from Guan et al. (2023a) but does not detail them here. |