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..
Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplers
Authors: Kiyoung Seong, Seonghyun Park, Seonghwan Kim, Woo Youn Kim, Sungsoo Ahn
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively evaluate our approach, termed TPSDPS, on a synthetic system, small peptide, and challenging fast-folding proteins, demonstrating that it produces more realistic and diverse transition pathways than existing baselines. |
| Researcher Affiliation | Academia | Kiyoung Seong , Seonghyun Park , Seonghwan Kim, Woo Youn Kim, Sungsoo Ahn KAIST EMAIL |
| Pseudocode | Yes | Algorithm 1 Training 1: Initialize an empty replay buffer ˆD, an policy vθ, a scalar parameter w, the number of rollout I and training per rollout J, and an annealing schedule λstart = λ1 > > λI = λend. 2: for i = 1, . . . , I do |
| Open Source Code | Yes | We provide links to our project page and code. |
| Open Datasets | Yes | We extensively evaluate our method on the synthetic double-well potential with dual channels, Alanine Dipeptide, and four fast-folding proteins: Chignolin, Trp-cage, BBA, and BBL (Lindorff-Larsen et al., 2011). |
| Dataset Splits | No | All metrics are averaged over 1024 paths for the double-well system, and 64 paths for Alanine Dipeptide. All metrics are averaged over 64 paths. |
| Hardware Specification | Yes | RT and RI denote runtime (second) per rollout in training and inference on a single RTX A5000 GPU. |
| Software Dependencies | Yes | All real-world molecular systems are simulated using the Open MM library (Eastman et al., 2023). For the top two TICA components, we use Py EMMA library (Scherer et al., 2015)... |
| Experiment Setup | Yes | We use a 3-layer MLP for the double-well system, and a 6-layer MLP for real-world molecules with Re LU activation functions for neural bias force, potential, and scale. We update the parameters of the neural network with a learning rate of 0.0001, while the scalar parameter w is updated with a learning rate of 0.001. Table 3: Model configurations of TPS-DPS. |