Skip to content

Latest commit

 

History

History

01_prop_pred_FFN

README.md

FFN Property Prediction

The repository uses a feed forward network applied to molecule Morgan Fingerprints to predict molecular properties.

Install

Install the conda environment:

conda env create -f environment.yml
conda activate dl-chem
pip install -r requirements.txt

To install this module for development:

python setup.py develop

Installing and structuring a package as a module makes it easy to avoid relative imports in code, which tend to complicate development.

Training a model

While the package files contained inside src/ should have the bulk of code, these files should not contain methods runnable directly. Instead, it's cleaner to have run scripts located outside the package file. Here, we use run_scripts/ to hold these different files for executing code inside the package.

A simple model can be trained by running the command:

python run_scripts/train_ffn.py --batch-size 64 --max-epochs 500 --layers 3 --dropout 0.1 --hidden-size 256

It can also be trained with gpu by simply adding a gpu flag (with a small datset and small model, this is runnable on cpu):

python run_scripts/train_ffn.py --batch-size 64 --max-epochs 500 --layers 3 --dropout 0.1 --hidden-size 256 --gpu

To view all arguments with argparse:

python run_scripts/train_ffn.py -h

Using the trained model

Making predictions

The trained model can be loaded directly from a TorchLightning checkpoint file and used to make predictions. Here, we can use our trained model to make predictions on the Enamine dataset of smiles strings alone.

python run_scripts/make_preds.py --num-workers 16 --batch-size 256 --save-name results/example_run/preds.tsv --checkpoint-pth results/example_run/version_0/epoch\=232-val_loss\=0.98.ckpt --smiles-file data/Enamine10k.csv

Again, this can also be made to run on gpu by adding --gpu at the end of the call.

Analyzing results

Rather than maintain analysis scripts in the package itself. You can use a separate folder to contain any simple scripts for analyzing outputs. Here, we suggest using analysis/ to hold such scripts.

As an example, we provide a script to print the top k predictions:

python analysis/get_top_smiles.py  --input-file  results/example_run/preds.tsv

Launching experiments with config files

While it's great to run scripts one at at time and get results, we often want to launch several experiments, take a break (lunch, beach, week-long vacation, etc.), and come back to get results. Launching these should be done programatically and consistently. We provide an example launcher script that shows how to build grids of different parameter inputs for a given python script, generate commands, and launch these in one of three settings:

  1. Slurm
  2. Local
  3. Local_parallel

As an example, say we want to sweep over 3 different hidden parameter sizes:

python launcher_scripts/run_from_config.py configs/2022_08_07_exp_config_debug.yaml

The config file provides a list of hidden sizes, and program iterations with each are launched.

Analysis

Once again, we can write a single analysis script to collect the results once it's done and view the test loss for each of these.

python analysis/collect_hidden_sweep.py