Quickstart

1. Prepare data (Papyrus++)

python uqdd/data/data_papyrus.py \
  --activity xc50 \
  --descriptor-protein ankh-large \
  --descriptor-chemical ecfp2048 \
  --split-type time \
  --n-targets -1 \
  --file-ext pkl \
  --sanitize \
  --verbose

Outputs: preprocessed splits under data/ in the chosen format.

1. Train models

2. Baseline (PNN):

python uqdd/models/model_parser.py --model pnn --data_name papyrus --n_targets -1 --activity_type xc50 --descriptor_protein ankh-large --descriptor_chemical ecfp2048 --split_type random --ext pkl --task_type regression --wandb_project_name pnn-test

• Deep Ensemble:

python uqdd/models/model_parser.py --model ensemble --ensemble_size 10 --data_name papyrus --n_targets -1 --activity_type xc50 --descriptor_protein ankh-large --descriptor_chemical ecfp2048 --split_type random --ext pkl --task_type regression --wandb_project_name ensemble-test

• MC-Dropout:

python uqdd/models/model_parser.py --model mcdropout --num_mc_samples 100 --data_name papyrus --n_targets -1 --activity_type xc50 --descriptor_protein ankh-large --descriptor_chemical ecfp2048 --split_type random --ext pkl --task_type regression --wandb_project_name mcdp-test

• Evidential:

python uqdd/models/model_parser.py --model evidential --data_name papyrus --n_targets -1 --activity_type xc50 --descriptor_protein ankh-large --descriptor_chemical ecfp2048 --split_type random --ext pkl --task_type regression --wandb_project_name evidential-test

• Ensemble of Evidential (EOE):

python uqdd/models/model_parser.py --model eoe --ensemble_size 10 --data_name papyrus --n_targets -1 --activity_type xc50 --descriptor_protein ankh-large --descriptor_chemical ecfp2048 --split_type random --ext pkl --task_type regression --wandb_project_name eoe-test

• Evidential MC-Dropout (EMC):

python uqdd/models/model_parser.py --model emc --num_mc_samples 100 --data_name papyrus --n_targets -1 --activity_type xc50 --descriptor_protein ankh-large --descriptor_chemical ecfp2048 --split_type random --ext pkl --task_type regression --wandb_project_name emc-test

Tips:

• Use --seed, --epochs, --batch_size, and --lr to control training.
• Set --device cuda to train on GPU.
• Logs can be sent to Weights & Biases via --wandb_project_name.