| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: ibm-research/GP-MoLFormer-Uniq |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: NPComposer-v2 |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/npcomposer/npcomposer/runs/lpvwn86m) |
| # NPComposer |
|
|
| NPComposer is a conditional molecular generation model trained by fine-tuning GP-MoLFormer [ibm-research/GP-MoLFormer-Uniq](https://huggingface.co/ibm-research/GP-MoLFormer-Uniq) — a 46.8M parameter transformer decoder foundation model — on the COCONUT database containing over 700,000 experimentally validated natural products: |
| https://github.com/shawnralyn/NPComposer |
|
|
| By providing class labels and molecular property information as special tokens during model fine-tuning, NPComposer allows for conditional natural product generation based on: NP biosynthesis pathway (7 pathways including Alkaloids, Terpenoids, Shikimates and Phenylpropanoids, etc.), NP superclass (70+ superclasses), presence or absence of glycoside, number of aromatic rings (0–22), QED drug-likeness (0–1), and synthetic accessibility score (1–10). |
|
|
| ## Model description |
|
|
| [More information needed](https://github.com/shawnralyn/NPComposer) |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.03 |
| - num_epochs: 8.0 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:-----:|:------:|:---------------:| |
| | 0.4069 | 1.0 | 27763 | 0.3820 | |
| | 0.3507 | 2.0 | 55526 | 0.3371 | |
| | 0.3247 | 3.0 | 83289 | 0.3142 | |
| | 0.3074 | 4.0 | 111052 | 0.2995 | |
| | 0.291 | 5.0 | 138815 | 0.2888 | |
| | 0.2765 | 6.0 | 166578 | 0.2803 | |
| | 0.2677 | 7.0 | 194341 | 0.2750 | |
| | 0.2569 | 8.0 | 222104 | 0.2718 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.44.2 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 2.21.0 |
| - Tokenizers 0.19.1 |
| |
| ### Hardware |
| 1 x NVIDIA A40 48GB GPU |
| |
