NPComposer

NPComposer is a conditional molecular generation model trained by fine-tuning GP-MoLFormer 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

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