Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Generalized Implicit Neural Representations for Dynamic Molecular Surface Modeling
Authors: Fang Wu, Bozhen Hu, Stan Z. Li
AAAI 2025 | Venue PDF | 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. |