Longer than expected running time for Binding Pose Prediction

In the Uni-Mol paper, the average number of seconds per ligand is 0.2.
> Efficiency benchmark We compare Uni-Mol binding pose prediction with popular docking tools in efficiency. The baseline results are taken from EquiBind [99] paper. And Uni-Mol binding pose prediction is run on a single V100 GPU. For each molecule, Uni-Mol is run with 10 different initial conformations, and the total time cost is reported. As shown in Table 20, Uni-Mol is significantly faster than traditional docking tools, about 250x faster.


I tried running the docking pose prediction on the provided `test.lmdb` and the run time took much longer than expected on a single RTX 3090. The average time was about 3 seconds per ligand. Were anything done to improve the run time of this?

This step was relatively fast ~36 seconds:
```
data_path="./protein_ligand_binding_pose_prediction" # replace to your data path
results_path="./infer_pose" # replace to your results path
weight_path="./save_pose/checkpoint.pt"
batch_size=8
dist_threshold=8.0
recycling=3

python ./unimol/infer.py --user-dir ./unimol $data_path --valid-subset test \
--results-path $results_path \
--num-workers 8 --ddp-backend=c10d --batch-size $batch_size \
--task docking_pose --loss docking_pose --arch docking_pose \
--path $weight_path \
--fp16 --fp16-init-scale 4 --fp16-scale-window 256 \
--dist-threshold $dist_threshold --recycling $recycling \
--log-interval 50 --log-format simple
```

But this step took around 15 mins for 285 ligand/pocket pairs.
```
`nthreads=20` # Num of threads
predict_file="./infer_pose/save_pose_test.out.pkl" # Your inference file dir
reference_file="./protein_ligand_binding_pose_prediction/test.lmdb" # Your reference file dir
output_path="./protein_ligand_binding_pose_prediction" # Docking results path

python ./unimol/utils/docking.py --nthreads $nthreads --predict-file $predict_file --reference-file $reference_file --output-path $output_path
```