README.md

DeepFrag

This repository contains code for machine learning based lead optimization.

Overview

• config: fixed configuration information (eg. TRAIN/VAL/TEST partitions)
• configurations: benchmark model configurations (see configurations/README.md)
• data: training/inference data (see data/README.md)
• leadopt: main module code
  • models: pytorch architecture definitions
  • data_util.py: utility code for reading packed fragment/fingerprint data files
  • grid_util.py: GPU-accelerated grid generation code
  • metrics.py: pytorch implementations of several metrics
  • model_conf.py: contains code to configure and train models
  • util.py: utility code for rdkit/openbabel processing
• scripts: data processing scripts (see scripts/README.md)
• train.py: CLI interface to launch training runs

Dependencies

You can build a virtualenv with the requirements:

$ python3 -m venv leadopt_env
$ source ./leadopt_env/bin/activate
$ pip install -r requirements.txt

Note: Cuda 10.1 is required during training

Training

To train a model, you can use the train.py utility script. You can specify model parameters as command line arguments or load parameters from a configuration args.json file.

python train.py \
    --save_path=/path/to/model \
    --wandb_project=my_project \
    {model_type} \
    --model_arg1=x \
    --model_arg2=y \
    ...

or

python train.py \
    --save_path=/path/to/model \
    --wandb_project=my_project \
    --configuration=./configurations/args.json

save_path is a directory to save the best model. The directory will be created if it doesn't exist. If this is not provided, the model will not be saved.

wandb_project is an optional wandb project name. If provided, the run will be logged to wandb.

See below for available models and model-specific parameters:

Leadopt Models

In this repository, trainable models are subclasses of model_conf.LeadoptModel. This class encapsulates model configuration arguments and pytorch models and enables saving and loading multi-component models.

from leadopt.model_conf import LeadoptModel, MODELS

model = MODELS['voxel']({args...})
model.train(save_path='./mymodel')

...

model2 = LeadoptModel.load('./mymodel')

Internally, model arguments are configured by setting up an argparse parser and passing around a dict of configuration parameters in self._args.

VoxelNet

--no_partitions     If set, disable the use of TRAIN/VAL partitions during
                    training.
-f FRAGMENTS, --fragments FRAGMENTS
                    Path to fragments file.
-fp FINGERPRINTS, --fingerprints FINGERPRINTS
                    Path to fingerprints file.
-lr LEARNING_RATE, --learning_rate LEARNING_RATE
--num_epochs NUM_EPOCHS
                    Number of epochs to train for.
--test_steps TEST_STEPS
                    Number of evaluation steps per epoch.
-b BATCH_SIZE, --batch_size BATCH_SIZE
--grid_width GRID_WIDTH
--grid_res GRID_RES
--fdist_min FDIST_MIN
                    Ignore fragments closer to the receptor than this
                    distance (Angstroms).
--fdist_max FDIST_MAX
                    Ignore fragments further from the receptor than this
                    distance (Angstroms).
--fmass_min FMASS_MIN
                    Ignore fragments smaller than this mass (Daltons).
--fmass_max FMASS_MAX
                    Ignore fragments larger than this mass (Daltons).
--ignore_receptor
--ignore_parent
-rec_typer {single,single_h,simple,simple_h,desc,desc_h}
-lig_typer {single,single_h,simple,simple_h,desc,desc_h}
-rec_channels REC_CHANNELS
-lig_channels LIG_CHANNELS
--in_channels IN_CHANNELS
--output_size OUTPUT_SIZE
--pad
--blocks BLOCKS [BLOCKS ...]
--fc FC [FC ...]
--use_all_labels
--dist_fn {mse,bce,cos,tanimoto}
--loss {direct,support_v1}