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 | Venue 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 EMAIL Yufei Huang Westlake University EMAIL Odin Zhang Zhejiang University EMAIL Lirong Wu Westlake University EMAIL Siyuan Li Westlake University EMAIL Zhiyuan Chen Deep Potential EMAIL Stan Z. Li Westlake University EMAIL |
| 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. |