Benchmark:
chembl_29_ecfp4_0.333.csv/smi is the pretraining dataset. The kinase and GPCR datasets have their corresponding pretrain datasets.
cdk contains 10 random 10% fine-tuning and the target sets of CDK.
cdk_100 contains 100 random 1% fine-tuning and the target sets of CDK.

Scripts:
evaluation.py is used to calculate the metrics whose environment requires Pytorch and RDKit.

usage: evaluation.py [-h] [--train_path TRAIN_PATH] [--goal_path GOAL_PATH] [--gen_path GEN_PATH] [--n_jobs N_JOBS]
                     [--metrics_path METRICS_PATH]

optional arguments:
  -h, --help            show this help message and exit
  --train_path TRAIN_PATH
                        Path to fine-tuning molecules csv
  --goal_path GOAL_PATH
                        Path to target molecules csv
  --gen_path GEN_PATH   Path to generated molecules csv
  --n_jobs N_JOBS       Number of threads
  --metrics_path METRICS_PATH
                        Path to output file with metrics

Example:

python evaluation.py --train_path ../Benchmark/kinase/cdk/cdk_1_train.csv --goal_path ../Benchmark/kinase/cdk/cdk_1_goal.csv --gen_path generated_molecules.csv --n_jobs 48 --metrics_path metrics.csv

The molecules of the generated_molecules.csv have to be valid, unique and novel.