AutoQSAR/DeepChem for billions of molecules

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2/25/2021 Ying Yang

Train a ligand-based ML model

Model training requires GPU thus will be on gimel5

  • Prepare the input file for training in the format of <ligand smiles>,<dock score>
Example distribution of dock score
SMILES,DOCK score
C[C@H]1COCCCN1C(=O)[C@@H]1CN2CCN1CCC2,-15.98
Cc1ccccc1-c1cc(C(=O)N2CC3(CN(C)C3)C2)n[nH]1,-17.43
CNC(=O)c1ccccc1NC(=O)[C@H]1C[C@H]2CCCCN2C1,-21.03
CC[C@H](F)CN[C@@H](CNC(=O)c1ccc(F)cc1F)C(C)C,4.73
C[C@@H]1CN(C(=O)C(=O)N[C@@H](c2cccc(F)c2)c2ccccn2)C[C@H]1N,13.34
CC(C)[C@H](NC(=O)NC[C@@H]1CCN(CCc2ccccc2)C1)C1CC1,5.9
CC[C@@H](F)CNCC1CCN(C(=O)c2ccc(F)s2)CC1,-14.68
Cc1cccc(C[C@@H](NCc2cccn2C)C2CC2)c1,-40.38
CC(C)NC(=O)c1ccc2nnc([C@H]3CN(Cc4ccccc4)CCN3C)n2c1,-23.15

IMHO, ML algorithm performs well with a normal distribution.

If < 30% molecules cannot be docked, it's safe to ignore the non-dockable (those without a dock score).

  • Prepare the submission file, and submit to
cat <<EOF > sbatch_ml_train.sh
#!/bin/bash
#SBATCH --job-name=ml_deepchem
#SBATCH --partition=gimel5.gpu
#SBATCH --gres=gpu:1
#SBATCH --ntasks=1

hostname
nvidia-smi

source /nfs/home/yingyang/programs/ligand_ml/anaconda/etc/profile.d/conda.sh
conda activate ligand_ml

# CHANGE here: to your prepared input file
infile=AL-dock_5HT5a_train.csv

i=1
ligand_ml train model_\${i} \${infile}
ligand_ml package model_\${i} \${i}
EOF

sbatch sbatch_ml_train.sh

Once the model training complete, you will see a folder 1/ and a file 1.tar.gz.

Transfer the ML model in folder 1/ to Wynton

scp -rp 1/ dt2.wynton.ucsf.edu:<path_to_where_to_run_prediction>

Apply the ML model to predict all molecules (smiles) of interest

Model prediction can leverage large number of CPUs thus will be on wynton

Prepare molecules (smiles) for prediction

For example, H26.smi is the file including ZINC ids and smiles of molecules for prediction

  • set up the folder and break down the input smiles
mkdir raw
cd raw
split -l 50000 ../H26.smi -
cd ../
  • run molecules standardization
mkdir -p standardized

set num=` ls raw/* | wc -l `
echo "Number of file to process:" $num

cat << EOF > qsub_standardize.csh
#\$ -S /bin/csh
#\$ -cwd
#\$ -pe smp 1
#\$ -l mem_free=2G
#\$ -l scratch=5G
#\$ -l h_rt=02:00:00
#\$ -j yes
#\$ -o std.out
#\$ -t 1-$num

hostname
date

echo  ""
source /wynton/home/shoichetlab/yingyang/programs/ligand_ml/anaconda/etc/profile.d/conda.csh
conda activate ligand_ml
which python
which ligand_ml
echo  ""

set BASE_DIR=\`pwd\`

# Do Not Edit Below This Point
set RAW_DIR=\${BASE_DIR}/raw/
set DEST_DIR=\${BASE_DIR}/standardized/
set CUDA_VISIBLE_DEVICES=-1

python /wynton/home/shoichetlab/yingyang/scripts_ML/code/standardize.py \$SGE_TASK_ID \${RAW_DIR} \${DEST_DIR}

EOF

qsub qsub_standardize.csh

Once complete, check if the number of files in the standardize folder is the same as in the raw folder

Run ML prediction

Go to the folder where to run prediction (where ML model (1/) is located).

mkdir prediction

set in=./
set model=1

set out=prediction_1
mkdir -p ${out}

set num=` ls standardized/*.csv | wc -l `
echo "Number of file to process:" $num

cat << EOF >! qsub_ml_${hac}.sh
#\$ -S /bin/sh
#\$ -cwd
#\$ -pe smp 1
#\$ -l mem_free=50G
#\$ -l scratch=50G
#\$ -l h_rt=01:00:00
#\$ -o qsub_ml_${hac}.out
#\$ -j yes
#\$ -t 1-$num

hostname
date

echo  ""
source /wynton/home/shoichetlab/yingyang/programs/ligand_ml/anaconda/etc/profile.d/conda.sh
conda activate ligand_ml
which python
which ligand_ml
echo  ""

# Set your variables
export TRAIN_DIR=\$(pwd)/${in}
export MODEL_NAME=$model
export MODEL=\${TRAIN_DIR}/\${MODEL_NAME}

export BASE_DIR=\$(pwd)/
export DEST_DIR=\${BASE_DIR}/${out}/

# Do Not Edit Below This Point
export CODE_DIR=/wynton/home/shoichetlab/yingyang/scripts_ML/code
export STANDARDIZED_DIR=\${BASE_DIR}/standardized/

export INFILE=\${STANDARDIZED_DIR}/\${SGE_TASK_ID}.csv
export OUTFILE=\${DEST_DIR}/\${SGE_TASK_ID}.csv

# Do path magic to set things up and use 1 cpu
#export LD_LIBRARY_PATH=/nfs/soft/schrodinger/2019-4/internal/lib/cuda-stubs/:\$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/wynton/home/shoichetlab/yingyang/cuda-stubs/:\$LD_LIBRARY_PATH
export CUDA_VISIBLE_DEVICES=-1

export CPU_STATS=\$(cat /dev/urandom | tr -cd 'a-f0-9' | head -c 32)
export MY_CPU_FILE=\$(cat /dev/urandom | tr -cd 'a-f0-9' | head -c 32)
sleep \$[ ( \$RANDOM % 10 ) ]s
mpstat -P ALL 5 1 > /tmp/\$CPU_STATS
python \${CODE_DIR}/get_idle_cpu.py /tmp/\$CPU_STATS /tmp/\$MY_CPU_FILE
export MY_CPU=\$(cat /tmp/\$MY_CPU_FILE)
echo "Using cpu \$MY_CPU"

n=0
until [ \$n -ge 5 ]
do
        echo "taskset -c \$MY_CPU ligand_ml evaluate \$MODEL \$INFILE \$OUTFILE --skip_standardization=True --skip_version_check=True"
        taskset -c \$MY_CPU ligand_ml evaluate \$MODEL \$INFILE \$OUTFILE --skip_standardization=True --skip_version_check=True && break
        n=\$[\$n+1]
        sleep 5
done
EOF

qsub qsub_ml.sh


Analyze prediction

Get the top 5% of ML prediction. A larger memory node is recommended for sorting...

source /wynton/home/shoichetlab/yingyang/programs/miniconda3/etc/profile.d/conda.sh
conda activate opencadd

# 50,000 mols per file
# 12,500 --> 25% 
# 25,000 --> 5%
#  5,000 --> 1%

# CHANGE here: to the prediction folder
dir_ml=prediction_1
echo "Process ${dir_ml} ... "

rm    ml_5percent.csv 
touch ml_5percent.csv 
for f in $(ls ${dir_ml}/* ); do
  echo $f
  head -n 25000 ${f}  | egrep -hiv 'score|model'  >> ml_5percent.csv
done

python /wynton/home/shoichetlab/yingyang/scripts_ML/out_analysis/sort_qsar_prediction.py ml_5percent.csv
rm ml_5percent.csv

Once complete, the top 5% from ML prediction will be in ml_5percent_sort.csv

The same procedure can be applied to other scores: dock scores, FEP predicted values...