AlienChen's picture
download
raw
1.6 kB
export CUDA_VISIBLE_DEVICES=0
python -u pcomol.py \
--pcomol_config /scratch/pranamlab/tong/pCoMol/configs/config_selfies.yaml \
--bindevaluator_config /scratch/pranamlab/tong/pCoMol/peptidomimetics/bindevaluator_config.yaml \
--ckpt /scratch/pranamlab/tong/pCoMol/peptidomimetics/ckpt/SELFIES_EditFlows.ckpt \
--num_steps 30 \
--num_candidates 50 \
--num_rollouts 50 \
--objective_weights 4 2 4 0.2 4 2 1 \
--input 'C[C@H](NC(=O)CN)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](Cc1c[nH]cn1)C(=O)N[C@@H](CCCNC(=N)N)C(=O)O' \
--target 'SVWCRHCGATSAGLRCEWQNNYTQCAPCASLSSCPVCYRNYREEDLILQCRQCDRWMHAVCQNLNTEEEVENVADIGFDCSMCR' \
--motifs '30,31,40,41,42,43,44,45,46,47,48,49,55,66,69,70,73,77' \
--specificity \
--output_file /scratch/pranamlab/tong/pCoMol/peptidomimetics/samples/rebuttal/6MLC_R50_C50.csv
python -u pcomol.py \
--pcomol_config /scratch/pranamlab/tong/pCoMol/configs/config_selfies.yaml \
--bindevaluator_config /scratch/pranamlab/tong/pCoMol/peptidomimetics/bindevaluator_config.yaml \
--ckpt /scratch/pranamlab/tong/pCoMol/peptidomimetics/ckpt/SELFIES_EditFlows.ckpt \
--num_steps 30 \
--num_candidates 50 \
--num_rollouts 50 \
--objective_weights 4 2 4 0.2 4 2 1 \
--input 'CC(C)[C@H](N)C(=O)N1CCC[C@H]1C(=O)N1CCC[C@H]1C(=O)N1CCC[C@H]1C(=O)N[C@H](C(=O)N1CCC[C@H]1C(=O)N1CCC[C@H]1C(=O)N1CCC[C@H]1C(=O)N1CCC[C@H]1C(=O)N[C@@H](CO)C(=O)O)C(C)C' \
--target 'GIDPFTGEAIAKFNFNGDTQVEMSFRKGERITLLRQVDENWYEGRIPGTSRQGIFPITYVDVIKRPL' \
--motifs '13,37,38,40,41,54,58,59' \
--specificity \
--output_file /scratch/pranamlab/tong/pCoMol/peptidomimetics/samples/rebuttal/2O9V_R50_C50.csv

Xet Storage Details

Size:
1.6 kB
·
Xet hash:
955121a78c6ee9f8b54144331cd0969535ccd8e43cbc5bc9ef331bf53980e2e6

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.