Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery
Authors: Yulun Wu, Nicholas Choma, Andrew Deru Chen, Mikaela Cashman, Erica Teixeira Prates, Veronica G Melesse Vergara, Manesh B Shah, Austin Clyde, Thomas Brettin, Wibe Albert de Jong, Neeraj Kumar, Martha S Head, Rick L. Stevens, Peter Nugent, Daniel A Jacobson, James B Brown
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis. |
| Researcher Affiliation | Collaboration | University of California, Berkeley, National Virtual Biotechnology Laboratory, US Department of Energy, Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory, || University of Tennessee, Knoxville, University of Chicago, Argonne National Laboratory, Pacific Northwest National Laboratory |
| Pseudocode | No | The paper contains mathematical equations and descriptions but does not include structured pseudocode or algorithm blocks with labels like 'Algorithm X' or 'Pseudocode'. |
| Open Source Code | Yes | To see samples of molecules generated by DGAPN in evaluation, visit our repository https://github.com/yulun-rayn/DGAPN. |
| Open Datasets | Yes | Dataset For the models/settings that do require a dataset, we used a set of SMILES IDs taken from more than six million compounds from the MCULE molecular library a publicly available dataset of purchasable molecules (Kiss et al., 2012), and their docking scores for the NSP15 target. |
| Dataset Splits | No | The paper mentions 'validation loss' in Figure 3 but does not provide specific percentages, sample counts, or explicit methodology for training/validation/test dataset splits. |
| Hardware Specification | Yes | Structural information about the putative protein-ligand complexes was integrated into this framework with Auto Dock-GPU (Santos-Martins et al., 2021), which leverages the GPU resources from leadership-class computing facilities, including the Summit supercomputer, for high-throughput molecular docking (Le Grand et al., 2020). |
| Software Dependencies | No | The paper mentions software like Pytorch-Geometric, Auto Dock-GPU, ADADELTA, and RDKit, but does not specify version numbers for these software dependencies. |
| Experiment Setup | Yes | Based on a parameter sweep, we set number of GNN layers to be 3, MLP layers to be 3, with 3 of the GNN layers and 0 of the MLP layers shared between query and key. Number of layers in RND is set to 1; all numbers of hidden neurons 256; learning rate for actor 2 3, for critic 1 4, for RND 2 3; update time steps (i.e. batch size) 300. Number of epochs per iteration and clipping parameter ϵ for PPO are 30 and 0.1. Output dimensions and clipping parameter η for RND are 8 and 5. In evaluation mode, we use arg max policy instead of sampling policy, expand the number of candidates per step from 15-20 to 128 and expand the maximum time steps per episode from 12 to 20 compared to training. |