Generalized Implicit Neural Representations for Dynamic Molecular Surface Modeling
Authors: Fang Wu, Bozhen Hu, Stan Z. Li
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate its effectiveness in analyzing complex molecular systems across continuous space and time domains. ... We verify the effectiveness of our Mo E-DSR on ATLAS (Vander Meersche et al. 2024), the largest existing MD simulation database of proteins. Comprehensive results demonstrate that incorporating the Mo E architecture and geometric symmetries significantly boosts INR s capability to comprehend protein dynamic changes and handle diverse protein distributions. ... Quantitative Results ... Ablation Studies |
| Researcher Affiliation | Academia | 1 Computer Science Department, Stanford University 2 School of Engineering, Westlake University EMAIL, EMAIL |
| Pseudocode | No | The paper includes a 'Model Overview' section with a diagram (Figure 1) illustrating the Mo E-DSR architecture, and describes the components using mathematical formulations, but it does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block with structured steps. |
| Open Source Code | No | The paper states: 'For baseline implementation, DSR (Sun et al. 2024) was reproduced using its official Git Hub website at https://github.com/Sundw-818/DSR.' This refers to the code for a baseline model (DSR), not the authors' own Mo E-DSR methodology. There is no explicit statement or link provided for the source code of Mo E-DSR. |
| Open Datasets | Yes | To comprehensively demonstrate and assess the ability of our method, we train Mo E-DSR on ATLAS (Vander Meersche et al. 2024), the largest up-to-date dataset of all-atom MD simulations for single-chain proteins. |
| Dataset Splits | Yes | The training split contains monomers not involved during the curation of the test split. Then selected test data points are divided randomly into the validation and final test sets with a ratio of 1:1. Using this cutoff, we obtain train/val/test splits of 1,290/50/50 ensembles. |
| Hardware Specification | Yes | All experiments are implemented in a data-parallel mode on 4 A100 GPUs, each with a memory of 80GB. |
| Software Dependencies | No | The paper mentions 'Py Torch Autograd' for gradient calculation and the 'Python scikit-image package' for Marching Cubes algorithm, but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | Following (Sun et al. 2024), we adopt the Softplus (i.e., Υ(x) = 1 β ln 1 + exp βx) as the activation function for the experts with β = 100. The gradient of leaners x E( ) is calculated by Py Torch Autograd. ... Each MLP has the same architecture with 8 layers and 512 hidden units as well as a single skip connection from the input to the middle layer. The initial latent code vector z is sampled from a normal distribution N(0, 1). ... The final loss of our Mo E-DSR is thus a weighted sum of LSDF and LMo E with different multiplicative coefficients λ1 and λ2 = 1e 2, respectively. ... Here, the choice of NK is a hyperparameter whose value is chosen according to application, and typically, NK = 1, 2. |