Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration
Authors: Haitao Lin, Yufei Huang, Odin Zhang, Yunfan Liu, Lirong Wu, Siyuan Li, Zhiyuan Chen, Stan Z. Li
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, our method can generate molecules with more realistic 3D structures, competitive affinities toward the protein targets, and better drug properties. |
| Researcher Affiliation | Collaboration | Haitao Lin Westlake University linhaitao@westlake.edu.cn Yufei Huang Westlake University huangyufei@westlake.edu.cn Odin Zhang Zhejiang University haotianzhang@zju.edu.cn Lirong Wu Westlake University wulirong@westlake.edu.cn Siyuan Li Westlake University lisiyuan@westlake.edu.cn Zhiyuan Chen Deep Potential chenzhiyuan@dp.tech Stan Z. Li Westlake University stan.zq.li@westlake.edu.cn |
| Pseudocode | Yes | Algorithm 1 Joint Generation for Molecules using D3FG |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | In the experiments, we use Cross Docked2020[32] for evaluation. |
| Dataset Splits | Yes | The datasets for training and evaluation are split according to POCKET2MOL [9] and TARGETDIFF [13]. 22.5 million docked protein binding complexes with low RMSD (< 1Å) and sequence identity less than 30% are selected, leading to 100,000 pairs of pocket-ligand complexes, with 100 novel complexes as references for evaluation. |
| Hardware Specification | Yes | We use a single NVIDIA A100(81920Mi B) GPU for a trial. |
| Software Dependencies | Yes | The codes are implemented in Python 3.9 mainly with Pytorch 1.12 |
| Experiment Setup | Yes | In the diffusion of orientation and position, we employ a cosine variance schedule for αt, which reads αt = cos^2(π/2 * (t/T + s)/(1 + s)) / cos^2(π/2 * s/(1 + s)), where s = 0.01. In the diffusion of atom type, βt is set as βt = t/T. For the denoiser, the layer number is set as 6, and the embedding size is set as 256. The model is trained with Adam optimizer in 5000 epochs. |