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Exclude AutoDock Vina from ensembling
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@amorehead
amorehead committed Aug 16, 2024
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16 changes: 8 additions & 8 deletions README.md
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Expand Up @@ -793,20 +793,20 @@ Benchmark (ensemble-)ranked predictions across each test dataset

```bash
# benchmark using the PoseBusters Benchmark dataset e.g., after generating 40 complexes per target with each method
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/posebusters_benchmark/ensemble_inputs.csv output_dir=data/test_cases/posebusters_benchmark/top_consensus_ensemble_predictions_1 max_method_predictions=40 export_top_n=1 export_file_format=null skip_existing=true relax_method_ligands_post_ranking=false resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa, vina]' ensemble_benchmarking=true ensemble_benchmarking_dataset=posebusters_benchmark ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/posebusters_benchmark/ensemble_inputs.csv output_dir=data/test_cases/posebusters_benchmark/top_consensus_ensemble_predictions_1 max_method_predictions=40 export_top_n=1 export_file_format=null skip_existing=true relax_method_ligands_post_ranking=true resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa, vina]' ensemble_benchmarking=true ensemble_benchmarking_dataset=posebusters_benchmark ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/posebusters_benchmark/ensemble_inputs.csv output_dir=data/test_cases/posebusters_benchmark/top_consensus_ensemble_predictions_1 max_method_predictions=40 export_top_n=1 export_file_format=null skip_existing=true relax_method_ligands_post_ranking=false resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa]' ensemble_benchmarking=true ensemble_benchmarking_dataset=posebusters_benchmark ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/posebusters_benchmark/ensemble_inputs.csv output_dir=data/test_cases/posebusters_benchmark/top_consensus_ensemble_predictions_1 max_method_predictions=40 export_top_n=1 export_file_format=null skip_existing=true relax_method_ligands_post_ranking=true resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa]' ensemble_benchmarking=true ensemble_benchmarking_dataset=posebusters_benchmark ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
...
# benchmark using the Astex Diverse dataset e.g., after generating 40 complexes per target with each method
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/astex_diverse/ensemble_inputs.csv output_dir=data/test_cases/astex_diverse/top_consensus_ensemble_predictions_1 max_method_predictions=40 export_top_n=1 export_file_format=null skip_existing=true relax_method_ligands_post_ranking=false resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa, vina]' ensemble_benchmarking=true ensemble_benchmarking_dataset=astex_diverse ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/astex_diverse/ensemble_inputs.csv output_dir=data/test_cases/astex_diverse/top_consensus_ensemble_predictions_1 max_method_predictions=40 export_top_n=1 export_file_format=null skip_existing=true relax_method_ligands_post_ranking=true resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa, vina]' ensemble_benchmarking=true ensemble_benchmarking_dataset=astex_diverse ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/astex_diverse/ensemble_inputs.csv output_dir=data/test_cases/astex_diverse/top_consensus_ensemble_predictions_1 max_method_predictions=40 export_top_n=1 export_file_format=null skip_existing=true relax_method_ligands_post_ranking=false resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa]' ensemble_benchmarking=true ensemble_benchmarking_dataset=astex_diverse ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/astex_diverse/ensemble_inputs.csv output_dir=data/test_cases/astex_diverse/top_consensus_ensemble_predictions_1 max_method_predictions=40 export_top_n=1 export_file_format=null skip_existing=true relax_method_ligands_post_ranking=true resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa]' ensemble_benchmarking=true ensemble_benchmarking_dataset=astex_diverse ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
...
# benchmark using the DockGen dataset e.g., after generating 40 complexes per target with each method
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/dockgen/ensemble_inputs.csv output_dir=data/test_cases/dockgen/top_consensus_ensemble_predictions_1 max_method_predictions=40 export_top_n=1 export_file_format=null skip_existing=true relax_method_ligands_post_ranking=false resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa, vina]' ensemble_benchmarking=true ensemble_benchmarking_dataset=dockgen ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/dockgen/ensemble_inputs.csv output_dir=data/test_cases/dockgen/top_consensus_ensemble_predictions_1 max_method_predictions=40 export_top_n=1 export_file_format=null skip_existing=true relax_method_ligands_post_ranking=true resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa, vina]' ensemble_benchmarking=true ensemble_benchmarking_dataset=dockgen ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/dockgen/ensemble_inputs.csv output_dir=data/test_cases/dockgen/top_consensus_ensemble_predictions_1 max_method_predictions=40 export_top_n=1 export_file_format=null skip_existing=true relax_method_ligands_post_ranking=false resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa]' ensemble_benchmarking=true ensemble_benchmarking_dataset=dockgen ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/dockgen/ensemble_inputs.csv output_dir=data/test_cases/dockgen/top_consensus_ensemble_predictions_1 max_method_predictions=40 export_top_n=1 export_file_format=null skip_existing=true relax_method_ligands_post_ranking=true resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa]' ensemble_benchmarking=true ensemble_benchmarking_dataset=dockgen ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
...
# benchmark using the CASP15 dataset e.g., after generating 40 complexes per target with each method
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_consensus_ensemble_predictions_1 combine_casp_output_files=true max_method_predictions=40 export_top_n=5 export_file_format=casp15 skip_existing=true relax_method_ligands_post_ranking=false resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa, vina]' ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_consensus_ensemble_predictions_1 combine_casp_output_files=true max_method_predictions=40 export_top_n=5 export_file_format=casp15 skip_existing=true relax_method_ligands_post_ranking=true resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa, vina]' ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_consensus_ensemble_predictions_1 combine_casp_output_files=true max_method_predictions=40 export_top_n=5 export_file_format=casp15 skip_existing=true relax_method_ligands_post_ranking=false resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa]' ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
python3 posebench/models/ensemble_generation.py input_csv_filepath=data/test_cases/casp15/ensemble_inputs.csv output_dir=data/test_cases/casp15/top_consensus_ensemble_predictions_1 combine_casp_output_files=true max_method_predictions=40 export_top_n=5 export_file_format=casp15 skip_existing=true relax_method_ligands_post_ranking=true resume=true cuda_device_index=0 ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa]' ensemble_benchmarking=true ensemble_benchmarking_dataset=casp15 ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index=1
...
# analyze benchmarking results for the PoseBusters Benchmark dataset
python3 posebench/analysis/inference_analysis.py method=ensemble dataset=posebusters_benchmark repeat_index=1
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2 changes: 1 addition & 1 deletion configs/model/ensemble_generation.yaml
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# General inference arguments:
ensemble_methods: [diffdock, dynamicbind, neuralplexer, rfaa, vina] # the methods from which to gather predictions for ensembling - NOTE: must be one of (`diffdock`, `dynamicbind`, `neuralplexer`, `rfaa`, `vina`, `tulip`)
ensemble_methods: [diffdock, dynamicbind, neuralplexer, rfaa] # the methods from which to gather predictions for ensembling - NOTE: must be one of (`diffdock`, `dynamicbind`, `neuralplexer`, `rfaa`, `vina`, `tulip`)
generate_vina_scripts: false # whether to generate Vina scripts using other methods' binding site predictions - NOTE: `resume` must also be `true` when this is `true`, meaning other methods' predictions must have already been generated locally
rank_single_method_intrinsically: true # whether to rank single-method predictions using either `consensus` or `vina` ranking (false) or instead using their intrinsic (explicit) rank assignment (true)
output_bash_file_dir: ensemble_generation_scripts # the directory in which to save the generated Bash scripts
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4 changes: 2 additions & 2 deletions scripts/build_inference_script.py
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Expand Up @@ -174,8 +174,8 @@
},
"ensemble": {
"run_inference": [
"python3 posebench/models/ensemble_generation.py pocket_only_baseline={pocket_only_baseline} ensemble_ranking_method={ensemble_ranking_method} input_csv_filepath=data/test_cases/{dataset}/ensemble_inputs.csv output_dir=data/test_cases/{dataset}/top_consensus_ensemble_predictions_{repeat_index} max_method_predictions=40 method_top_n_to_select=3 export_top_n={export_top_n} export_file_format={dataset} skip_existing=true relax_method_ligands_post_ranking=false relax_protein={relax_protein} resume=true cuda_device_index={cuda_device_index} ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa, vina]' vina_binding_site_methods=[{vina_binding_site_method}] ensemble_benchmarking=true ensemble_benchmarking_dataset={dataset} ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index={repeat_index}",
"python3 posebench/models/ensemble_generation.py pocket_only_baseline={pocket_only_baseline} ensemble_ranking_method={ensemble_ranking_method} input_csv_filepath=data/test_cases/{dataset}/ensemble_inputs.csv output_dir=data/test_cases/{dataset}/top_consensus_ensemble_predictions_{repeat_index} max_method_predictions=40 method_top_n_to_select=3 export_top_n={export_top_n} export_file_format={dataset} skip_existing=true relax_method_ligands_post_ranking=true relax_protein={relax_protein} resume=true cuda_device_index={cuda_device_index} ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa, vina]' vina_binding_site_methods=[{vina_binding_site_method}] ensemble_benchmarking=true ensemble_benchmarking_dataset={dataset} ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index={repeat_index}",
"python3 posebench/models/ensemble_generation.py pocket_only_baseline={pocket_only_baseline} ensemble_ranking_method={ensemble_ranking_method} input_csv_filepath=data/test_cases/{dataset}/ensemble_inputs.csv output_dir=data/test_cases/{dataset}/top_consensus_ensemble_predictions_{repeat_index} max_method_predictions=40 method_top_n_to_select=3 export_top_n={export_top_n} export_file_format={dataset} skip_existing=true relax_method_ligands_post_ranking=false relax_protein={relax_protein} resume=true cuda_device_index={cuda_device_index} ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa]' vina_binding_site_methods=[{vina_binding_site_method}] ensemble_benchmarking=true ensemble_benchmarking_dataset={dataset} ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index={repeat_index}",
"python3 posebench/models/ensemble_generation.py pocket_only_baseline={pocket_only_baseline} ensemble_ranking_method={ensemble_ranking_method} input_csv_filepath=data/test_cases/{dataset}/ensemble_inputs.csv output_dir=data/test_cases/{dataset}/top_consensus_ensemble_predictions_{repeat_index} max_method_predictions=40 method_top_n_to_select=3 export_top_n={export_top_n} export_file_format={dataset} skip_existing=true relax_method_ligands_post_ranking=true relax_protein={relax_protein} resume=true cuda_device_index={cuda_device_index} ensemble_methods='[diffdock, dynamicbind, neuralplexer, rfaa]' vina_binding_site_methods=[{vina_binding_site_method}] ensemble_benchmarking=true ensemble_benchmarking_dataset={dataset} ensemble_ranking_method=consensus ensemble_benchmarking_repeat_index={repeat_index}",
],
"analyze_results": [
"python3 posebench/analysis/inference_analysis.py method=ensemble dataset={dataset} pocket_only_baseline={pocket_only_baseline} relax_protein={relax_protein} repeat_index={repeat_index}",
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