A 3D Generative Model for Structure-Based Drug Design
Authors: Shitong Luo, Jiaqi Guan, Jianzhu Ma, Jian Peng
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to evaluate our approach. Quantitative and qualitative results show that: (1) our method is able to generate diverse drug-like molecules that have high binding affinity to specific targets based on 3D structures of protein binding sites; (2) our method is able to generate molecules with fairly high drug-likeness score (QED) [4] and synthetic accessibility score (SA) [6] even if the model is not specifically optimized for them; (3) in addition to molecule generation, the proposed method is also applicable to other relevant tasks such as linker design. |
| Researcher Affiliation | Collaboration | Shitong Luo Heli Xon Research luost@helixon.com luost26@gmail.com Jiaqi Guan University of Illinois Urbana-Champaign jiaqi@illinois.edu Jianzhu Ma Peking University majianzhu@pku.edu.cn Jian Peng University of Illinois Urbana-Champaign jianpeng@illinois.edu |
| Pseudocode | No | The paper describes algorithms in prose (e.g., Section 3.2 'Sampling') and includes illustrations (Figure 1), but it does not contain a structured pseudocode or algorithm block. |
| Open Source Code | Yes | Other details about model architectures and training parameters are provided in the supplementary material and the open source repository: https://github.com/ luost26/3D-Generative-SBDD. |
| Open Datasets | Yes | We use the Cross Docked dataset [7] following [20]. The dataset originally contains 22.5 million docked protein-ligand pairs at different levels of quality. |
| Dataset Splits | No | We use mmseqs2 [31] to cluster data at 30% sequence identity, and randomly draw 100,000 protein-ligand pairs for training and 100 proteins from remaining clusters for testing. No explicit validation set is mentioned. |
| 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. It only implies that models were trained and experiments conducted. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'Open Babel [21, 20]' but does not provide specific version numbers for these or any other key software dependencies required for replication. |
| Experiment Setup | Yes | The number of message passing layers in context encoder L is 6, and the hidden dimension is 256. We train the model using the Adam optimizer at learning rate 0.0001. |