Update README.md

README.md

@@ -219,7 +219,7 @@ rm dockgen_ensemble_benchmark_method_predictions.tar.gz
 rm casp15_ensemble_benchmark_method_predictions.tar.gz
 ```
 
-**NOTE:** One can reproduce the *pocket-only* experiments with the PoseBusters Benchmark set by adding the argument `pocket_only_baseline=true` to each command below used to run PoseBusters Benchmark dataset inference with all the baseline methods (n.b., besides `tulip`, which does not support pocket-level docking currently), since the pocket-only versions of the dataset's holo-aligned predicted protein structures have also been included in the downloadable Zenodo archive `posebusters_benchmark_set.tar.gz` referenced above. Similarly, one can reproduce the *NeuralPLexer w/o inter-ligand clash loss (ILCL)* experiments with the CASP15 set by adding the argument `no_ilcl=true` (`neuralplexer_no_ilcl=true`) to the commands `python3 posebench/models/neuralplexer_inference.py dataset=casp15 ...` and `python3 posebench/analysis/inference_analysis_casp.py dataset=casp15 ...` below (`python3 posebench/models/ensemble_generation.py ensemble_benchmarking_dataset=casp15 ...`) used to run CASP15 dataset inference with NeuralPLexer. Lastly, one can reproduce the *DiffDock w/o structural cluster training (SCT)* by adding the argument `v1_baseline=true` to the DiffDock inference commands below. Please see the config files within `configs/data/`, `configs/model/`, and `configs/analysis/` for more details.
+**NOTE:** One can reproduce the *pocket-only* experiments with the PoseBusters Benchmark set by adding the argument `pocket_only_baseline=true` to each command below used to run PoseBusters Benchmark dataset inference with all the baseline methods (n.b., besides `tulip`, which does not support pocket-level docking currently), since the pocket-only versions of the dataset's holo-aligned predicted protein structures have also been included in the downloadable Zenodo archive `posebusters_benchmark_set.tar.gz` referenced above. Similarly, one can reproduce the *NeuralPLexer w/o inter-ligand clash loss (ILCL)* experiments with the CASP15 set by adding the argument `no_ilcl=true` (`neuralplexer_no_ilcl=true`) to the commands `python3 posebench/models/neuralplexer_inference.py dataset=casp15 ...` and `python3 posebench/analysis/inference_analysis_casp.py dataset=casp15 ...` below (`python3 posebench/models/ensemble_generation.py ensemble_benchmarking_dataset=casp15 ...`) used to run CASP15 dataset inference with NeuralPLexer. Lastly, one can reproduce the *DiffDock w/o structural cluster training (SCT)* experiments by adding the argument `v1_baseline=true` to the DiffDock inference commands below. Please see the config files within `configs/data/`, `configs/model/`, and `configs/analysis/` for more details.
 
 ### Downloading sequence databases (required only for RoseTTAFold-All-Atom inference)