FUSE-RT checkpoints
This repository distributes the trained FUSE-RT checkpoints for the original 179-method RepoRT cohort. It contains 180 checkpoints across 18 experimental configurations, with 10 seeds per configuration.
Model definitions, data preparation, evaluation scripts, and experimental results are available in the FUSE-RT code repository.
Directory overview
| Directory | Purpose | Configurations | Checkpoints |
|---|---|---|---|
base/ |
E1–E9 factorial experiments combining molecular-training strategies and RT architectures | 9 | 90 |
stage/ |
E8 auxiliary-data scaling with correctly paired RadonPy property labels | 4 | 40 |
shuffle/ |
E8 constrained shuffled-label controls at five auxiliary-data fractions | 5 | 50 |
Every experimental directory follows the same layout:
<group>/<configuration>/seed_<seed>/best_model.pt
For example, base/E8/seed_2034/best_model.pt is the E8 checkpoint for split seed 2034.
base/: E1–E9 experiments
The molecular-training strategies are M0, RT training without an auxiliary objective; M1, RadonPy property pretraining followed by RT fine-tuning; and M2, joint RT and RadonPy multitask training.
The RT architectures are R0, chromatographic-condition encoding with a shared prediction head; R1, chromatographic-condition encoding with method-specific prediction heads; and R2, method-specific prediction heads without chromatographic-condition encoding.
| Directory | Molecular training | RT architecture |
|---|---|---|
base/E1/ |
M0: no auxiliary objective | R0: conditions + shared head |
base/E2/ |
M0: no auxiliary objective | R1: conditions + method-specific heads |
base/E3/ |
M0: no auxiliary objective | R2: no conditions + method-specific heads |
base/E4/ |
M1: sequential pretraining | R0: conditions + shared head |
base/E5/ |
M1: sequential pretraining | R1: conditions + method-specific heads |
base/E6/ |
M1: sequential pretraining | R2: no conditions + method-specific heads |
base/E7/ |
M2: joint multitask training | R0: conditions + shared head |
base/E8/ |
M2: joint multitask training | R1: conditions + method-specific heads |
base/E9/ |
M2: joint multitask training | R2: no conditions + method-specific heads |
stage/ and shuffle/: auxiliary-data experiments
Both groups use the E8 architecture. Percentages denote the requested fraction of the available RadonPy auxiliary training pool, not the fraction of RepoRT retention-time observations.
| RadonPy fraction | Correct-label checkpoint directory | Shuffled-label checkpoint directory |
|---|---|---|
| 0% | base/E2/ |
— |
| 0.05% | stage/E8_p01/ |
shuffle/E8_p01/ |
| 0.334370% | stage/E8_p02/ |
shuffle/E8_p02/ |
| 2.236068% | stage/E8_p03/ |
shuffle/E8_p03/ |
| 14.953488% | stage/E8_p04/ |
shuffle/E8_p04/ |
| 100% | base/E8/ |
shuffle/E8_p05/ |
stage/ contains the four intermediate correct-label scaling points. The 0% and 100% endpoints reuse base/E2/ and base/E8/, respectively. Here, “stage” names the scaling-point collection; it does not refer to the M1 sequential-pretraining strategy.
shuffle/ contains the corresponding constrained shuffled-label controls. Within the selected auxiliary training rows, each complete property-target vector and its missing-value mask are reassigned together to another molecule. The permutation prohibits self-assignment, requires different molecular connectivity, and requires Morgan-fingerprint Tanimoto similarity below 0.50. RT labels, molecular inputs, and auxiliary validation labels remain unchanged. These checkpoints support matched comparisons of correct and disrupted molecule–property correspondence.
Seed directories and checkpoint files
Every configuration contains the same 10 seed directories:
seed_2004 seed_2006 seed_2011 seed_2012 seed_2016
seed_2020 seed_2022 seed_2027 seed_2032 seed_2034
Each seed identifies the corresponding molecule-disjoint train/validation/internal-test split and training run. Each directory contains one validation-selected PyTorch checkpoint, best_model.pt, including model parameters and the saved configuration, normalization statistics, and vocabularies used by the project evaluation scripts.
To use these files with the GitHub project, retain the configuration and seed subdirectories and map the top-level folders as follows:
| Hugging Face directory | Directory in the GitHub project |
|---|---|
base/ |
checkpoints/experiments/ |
stage/ |
checkpoints/scaling/ |
shuffle/ |
checkpoints/constrained_shuffle/ |
Training and evaluation procedures are documented in the GitHub README.