# Virtual screening


<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->

## Setup

``` python
from af_kit.core import *
import pandas as pd
```

## Run single protein default pipeline

### Json:

``` python
data = get_protein_json('proteinA','AAA','data/proteinA.json',seeds=[1,2,3])
```

``` python
data
```

    {'name': 'proteinA',
     'modelSeeds': [1, 2, 3],
     'sequences': [{'protein': {'id': 'A', 'sequence': 'AAA'}}],
     'bondedAtomPairs': [],
     'dialect': 'alphafold3',
     'version': 3}

### Docker command

Move the generated `proteinA.json` to the `af_input/project_name` folder

``` python
project_name='sdf'
```

``` python
docker_single_full(json_path=f"af_input/{project_name}/proteinA.json",
                               output_dir=f"af_output/{project_name}")
```

    docker run --rm \
        --volume "$HOME/af_input:/root/af_input" \
        --volume "$HOME/af_output/sdf:/root/af_output" \
        --volume "$HOME/af_model:/root/models" \
        --volume "$HOME/af_database:/root/public_databases" \
        --gpus "device=0" \
        sky1ove/alphafold3 \
        python run_alphafold.py \
        --json_path=/root/af_input/sdf/proteinA.json \
        --output_dir=/root/af_output \
        --model_dir=/root/models

## Prepare protein-smiles files

### Read output json

``` python
protein_json = read_json('data/seq_only_data.json')
```

``` python
str(protein_json)[:1000]
```

    '{\'dialect\': \'alphafold3\', \'version\': 2, \'name\': \'PDCD1_seq_only\', \'sequences\': [{\'protein\': {\'id\': \'A\', \'sequence\': \'LDSPDRPWNPPTFSPALLVVTEGDNATFTCSFSNTSESFVLNWYRMSPSNQTDKLAAFPEDRSQPGQDCRFRVTQLPNGRDFHMSVVRARRNDSGTYLCGAISLAPKAQIKESLRAELRVTERRAEVPTAHPSPSPRPAGQFQTLV\', \'modifications\': [], \'unpairedMsa\': ">query\\nLDSPDRPWNPPTFSPALLVVTEGDNATFTCSFSNTSESFVLNWYRMSPSNQTDKLAAFPEDRSQPGQDCRFRVTQLPNGRDFHMSVVRARRNDSGTYLCGAISLAPKAQIKESLRAELRVTERRAEVPTAHPSPSPRPAGQFQTLV\\n>UniRef90_UPI0009801507/25-167 [subseq from] Programmed cell death protein 1 n=10 Tax=Homo sapiens TaxID=9606 RepID=UPI0009801507\\nLDSPDRPWNPPTFSPALLVVTEGDNATFTCSFSNTSESFVLNWYRMSPSNQTDKLAAFPEDRSQPGQDCRFRVTQLPNGRDFHMSVVRARRNDSGTYLCGAISLAPKAQIKESLRAELRVTERRAEVPTAHPSPSPRPAGQFQ---\\n>UniRef90_A0A5F7ZCX7/24-168 [subseq from] Programmed cell death 1 n=1 Tax=Macaca mulatta TaxID=9544 RepID=A0A5F7ZCX7_MACMU\\n-ESPDRPWNPPTFSPALLLVTEGDNATFTCSFSNASESFVLNWYRMSPSNQTDKLAAFPEDRSQPGRDCRFRVTQLPNGRDFHMSVVRARRNDSGTYLCGAISLAPKAQIKESLRAELRVTERRAEVPTAHPSPSPRP'

### Single protein-smile pair

``` python
out = get_protein_smiles_json('smi_name','CCC',protein_json,'data/protein_smi.json')
```

### Multiple protein-smile pairs in a df

``` python
df = pd.DataFrame({'idx':['a','b'],'smi':['CCC','OCO']})
df
```

<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }
&#10;    .dataframe tbody tr th {
        vertical-align: top;
    }
&#10;    .dataframe thead th {
        text-align: right;
    }
</style>

<table class="dataframe" data-quarto-postprocess="true" data-border="1">
<thead>
<tr style="text-align: right;">
<th data-quarto-table-cell-role="th"></th>
<th data-quarto-table-cell-role="th">idx</th>
<th data-quarto-table-cell-role="th">smi</th>
</tr>
</thead>
<tbody>
<tr>
<td data-quarto-table-cell-role="th">0</td>
<td>a</td>
<td>CCC</td>
</tr>
<tr>
<td data-quarto-table-cell-role="th">1</td>
<td>b</td>
<td>OCO</td>
</tr>
</tbody>
</table>

</div>

``` python
for idx, smi in df.values:
    _ = get_protein_smiles_json(idx,smi,protein_json,f'af_input/{project_name}/{idx}.json',seeds=[1,2,3])
```

This will generate many json files in the directory

## Split file into multiple subfolder for multi-GPUs

``` python
split_nfolder(f'af_input/{project_name}')
```

    Distributed 2 files into 4 folders.

## Docker

``` bash
docker pull sky1ove/alphafold3
```

``` python
for i in range(4):
    docker_multi_infer(input_dir=f"af_input/{project_name}/folder_{i}",
                               output_dir=f"af_output/{project_name}",
                               gpus=i)
# norun_data_pipeline means skip template search as we already did in the first step
```

    docker run --rm \
        --volume "$HOME/af_input:/root/af_input" \
        --volume "$HOME/af_output/sdf:/root/af_output" \
        --volume "$HOME/af_model:/root/models" \
        --volume "$HOME/af_database:/root/public_databases" \
        --volume "$HOME/af_cache:/root/cache" \
        --gpus "device=0" \
        sky1ove/alphafold3 \
        python run_alphafold.py \
        --input_dir=/root/af_input/sdf/folder_0 \
        --output_dir=/root/af_output \
        --model_dir=/root/models \
        --jax_compilation_cache_dir=/root/cache \
        --norun_data_pipeline
    docker run --rm \
        --volume "$HOME/af_input:/root/af_input" \
        --volume "$HOME/af_output/sdf:/root/af_output" \
        --volume "$HOME/af_model:/root/models" \
        --volume "$HOME/af_database:/root/public_databases" \
        --volume "$HOME/af_cache:/root/cache" \
        --gpus "device=1" \
        sky1ove/alphafold3 \
        python run_alphafold.py \
        --input_dir=/root/af_input/sdf/folder_1 \
        --output_dir=/root/af_output \
        --model_dir=/root/models \
        --jax_compilation_cache_dir=/root/cache \
        --norun_data_pipeline
    docker run --rm \
        --volume "$HOME/af_input:/root/af_input" \
        --volume "$HOME/af_output/sdf:/root/af_output" \
        --volume "$HOME/af_model:/root/models" \
        --volume "$HOME/af_database:/root/public_databases" \
        --volume "$HOME/af_cache:/root/cache" \
        --gpus "device=2" \
        sky1ove/alphafold3 \
        python run_alphafold.py \
        --input_dir=/root/af_input/sdf/folder_2 \
        --output_dir=/root/af_output \
        --model_dir=/root/models \
        --jax_compilation_cache_dir=/root/cache \
        --norun_data_pipeline
    docker run --rm \
        --volume "$HOME/af_input:/root/af_input" \
        --volume "$HOME/af_output/sdf:/root/af_output" \
        --volume "$HOME/af_model:/root/models" \
        --volume "$HOME/af_database:/root/public_databases" \
        --volume "$HOME/af_cache:/root/cache" \
        --gpus "device=3" \
        sky1ove/alphafold3 \
        python run_alphafold.py \
        --input_dir=/root/af_input/sdf/folder_3 \
        --output_dir=/root/af_output \
        --model_dir=/root/models \
        --jax_compilation_cache_dir=/root/cache \
        --norun_data_pipeline