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94391f2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 | data_path="./data"
save_root="./save"
save_name="screen_pocket"
save_dir="${save_root}/${save_name}/savedir_screen"
tmp_save_dir="${save_root}/${save_name}/tmp_save_dir_screen"
tsb_dir="${save_root}/${save_name}/tsb_dir_screen"
mkdir -p ${save_dir}
n_gpu=2
MASTER_PORT=10062
finetune_mol_model="./pretrain/mol_pre_no_h_220816.pt" # unimol pretrained mol model
finetune_pocket_model="./pretrain/pocket_pre_220816.pt" # unimol pretrained pocket model
batch_size=24
batch_size_valid=32
epoch=50
dropout=0.0
warmup=0.06
update_freq=1
dist_threshold=8.0
recycling=3
lr=1e-4
export NCCL_ASYNC_ERROR_HANDLING=1
export OMP_NUM_THREADS=1
CUDA_VISIBLE_DEVICES="0,1" python -m torch.distributed.launch --nproc_per_node=$n_gpu --master_port=$MASTER_PORT $(which unicore-train) $data_path --user-dir ./unimol --train-subset train --valid-subset valid \
--num-workers 8 --ddp-backend=c10d \
--task train_task --loss rank_softmax --arch pocketscreen \
--max-pocket-atoms 256 \
--optimizer adam --adam-betas "(0.9, 0.999)" --adam-eps 1e-8 --clip-norm 1.0 \
--lr-scheduler polynomial_decay --lr $lr --warmup-ratio $warmup --max-epoch $epoch --batch-size $batch_size --batch-size-valid $batch_size_valid \
--fp16 --fp16-init-scale 4 --fp16-scale-window 256 --update-freq $update_freq --seed 1 \
--tensorboard-logdir $tsb_dir \
--log-interval 100 --log-format simple \
--validate-interval 1 \
--best-checkpoint-metric valid_bedroc --patience 2000 --all-gather-list-size 2048000 \
--save-dir $save_dir --tmp-save-dir $tmp_save_dir --keep-best-checkpoints 8 --keep-last-epochs 10 \
--find-unused-parameters \
--maximize-best-checkpoint-metric \
--finetune-pocket-model $finetune_pocket_model \
--finetune-mol-model $finetune_mol_model \
--valid-set CASF \
--max-lignum 16 \
--protein-similarity-thres 1.0 > ${save_root}/train_log/train_log_${save_name}.txt
save_name="screen_pocket_norank"
save_dir="${save_root}/${save_name}/savedir_screen"
tmp_save_dir="${save_root}/${save_name}/tmp_save_dir_screen"
tsb_dir="${save_root}/${save_name}/tsb_dir_screen"
mkdir -p ${save_dir}
n_gpu=2
MASTER_PORT=10062
finetune_mol_model="./pretrain/mol_pre_no_h_220816.pt" # unimol pretrained mol model
finetune_pocket_model="./pretrain/pocket_pre_220816.pt" # unimol pretrained pocket model
export NCCL_ASYNC_ERROR_HANDLING=1
export OMP_NUM_THREADS=1
CUDA_VISIBLE_DEVICES="0,1" python -m torch.distributed.launch --nproc_per_node=$n_gpu --master_port=$MASTER_PORT $(which unicore-train) $data_path --user-dir ./unimol --train-subset train --valid-subset valid \
--num-workers 8 --ddp-backend=c10d \
--task train_task --loss rank_softmax --arch pocketscreen \
--max-pocket-atoms 256 \
--optimizer adam --adam-betas "(0.9, 0.999)" --adam-eps 1e-8 --clip-norm 1.0 \
--lr-scheduler polynomial_decay --lr $lr --warmup-ratio $warmup --max-epoch $epoch --batch-size $batch_size --batch-size-valid $batch_size_valid \
--fp16 --fp16-init-scale 4 --fp16-scale-window 256 --update-freq $update_freq --seed 1 \
--tensorboard-logdir $tsb_dir \
--log-interval 100 --log-format simple \
--validate-interval 1 \
--best-checkpoint-metric valid_bedroc --patience 2000 --all-gather-list-size 2048000 \
--save-dir $save_dir --tmp-save-dir $tmp_save_dir --keep-best-checkpoints 8 --keep-last-epochs 10 \
--find-unused-parameters \
--maximize-best-checkpoint-metric \
--finetune-pocket-model $finetune_pocket_model \
--finetune-mol-model $finetune_mol_model \
--valid-set CASF \
--max-lignum 16 \
--protein-similarity-thres 1.0 \
--rank-weight 0.0 > ${save_root}/train_log/train_log_${save_name}.txt
save_name="screen_pocket_no_similar_protein0.8"
save_dir="${save_root}/${save_name}/savedir_screen"
tmp_save_dir="${save_root}/${save_name}/tmp_save_dir_screen"
tsb_dir="${save_root}/${save_name}/tsb_dir_screen"
mkdir -p ${save_dir}
n_gpu=2
MASTER_PORT=10062
finetune_mol_model="./pretrain/mol_pre_no_h_220816.pt" # unimol pretrained mol model
finetune_pocket_model="./pretrain/pocket_pre_220816.pt" # unimol pretrained pocket model
export NCCL_ASYNC_ERROR_HANDLING=1
export OMP_NUM_THREADS=1
CUDA_VISIBLE_DEVICES="0,1" python -m torch.distributed.launch --nproc_per_node=$n_gpu --master_port=$MASTER_PORT $(which unicore-train) $data_path --user-dir ./unimol --train-subset train --valid-subset valid \
--num-workers 8 --ddp-backend=c10d \
--task train_task --loss rank_softmax --arch pocketscreen \
--max-pocket-atoms 256 \
--optimizer adam --adam-betas "(0.9, 0.999)" --adam-eps 1e-8 --clip-norm 1.0 \
--lr-scheduler polynomial_decay --lr $lr --warmup-ratio $warmup --max-epoch $epoch --batch-size $batch_size --batch-size-valid $batch_size_valid \
--fp16 --fp16-init-scale 4 --fp16-scale-window 256 --update-freq $update_freq --seed 1 \
--tensorboard-logdir $tsb_dir \
--log-interval 100 --log-format simple \
--validate-interval 1 \
--best-checkpoint-metric valid_bedroc --patience 2000 --all-gather-list-size 2048000 \
--save-dir $save_dir --tmp-save-dir $tmp_save_dir --keep-best-checkpoints 8 --keep-last-epochs 10 \
--find-unused-parameters \
--maximize-best-checkpoint-metric \
--finetune-pocket-model $finetune_pocket_model \
--finetune-mol-model $finetune_mol_model \
--valid-set CASF \
--max-lignum 16 \
--protein-similarity-thres 0.8 > ${save_root}/train_log/train_log_${save_name}.txt
save_name="screen_pocket_no_similar_protein"
save_dir="${save_root}/${save_name}/savedir_screen"
tmp_save_dir="${save_root}/${save_name}/tmp_save_dir_screen"
tsb_dir="${save_root}/${save_name}/tsb_dir_screen"
mkdir -p ${save_dir}
n_gpu=2
MASTER_PORT=10062
finetune_mol_model="./pretrain/mol_pre_no_h_220816.pt" # unimol pretrained mol model
finetune_pocket_model="./pretrain/pocket_pre_220816.pt" # unimol pretrained pocket model
export NCCL_ASYNC_ERROR_HANDLING=1
export OMP_NUM_THREADS=1
CUDA_VISIBLE_DEVICES="0,1" python -m torch.distributed.launch --nproc_per_node=$n_gpu --master_port=$MASTER_PORT $(which unicore-train) $data_path --user-dir ./unimol --train-subset train --valid-subset valid \
--num-workers 8 --ddp-backend=c10d \
--task train_task --loss rank_softmax --arch pocketscreen \
--max-pocket-atoms 256 \
--optimizer adam --adam-betas "(0.9, 0.999)" --adam-eps 1e-8 --clip-norm 1.0 \
--lr-scheduler polynomial_decay --lr $lr --warmup-ratio $warmup --max-epoch $epoch --batch-size $batch_size --batch-size-valid $batch_size_valid \
--fp16 --fp16-init-scale 4 --fp16-scale-window 256 --update-freq $update_freq --seed 1 \
--tensorboard-logdir $tsb_dir \
--log-interval 100 --log-format simple \
--validate-interval 1 \
--best-checkpoint-metric valid_bedroc --patience 2000 --all-gather-list-size 2048000 \
--save-dir $save_dir --tmp-save-dir $tmp_save_dir --keep-best-checkpoints 8 --keep-last-epochs 10 \
--find-unused-parameters \
--maximize-best-checkpoint-metric \
--finetune-pocket-model $finetune_pocket_model \
--finetune-mol-model $finetune_mol_model \
--valid-set CASF \
--max-lignum 16 \
--protein-similarity-thres 0.4 > ${save_root}/train_log/train_log_${save_name}.txt |