This repo contains the code needed to reproduce the workflow (i.e. data featurization, splitting and model training) reported in the manuscript
We featurize data based on the SMILES strings, and split the data (using scaffold split) first into test(0.1)/train(0.9) and then, additionally, the train set is split in train(0.8)/valid(0.2) for 5-fold cross validation.
The features used are:
- ECFP4, 2048 bit fingerprints
- RDKit descriptors (excl. BCUT2D)
- LogD calculcations for the range pH 0.5-7.4
- Molecular Graph Convolutions
Features are calculcated using DeepChem module (https://deepchem.io/), except for LogD which was done with Chemaxons cxcalc commandline tool (https://docs.chemaxon.com/display/docs/cxcalc-command-line-tool.md).
Data splits and features are saved locally, and used the train a set of models:
- XGBoost
- AttentiveFP
- Fully-connected Neural Network (FCNN)
- ChemProp
Each model training is composed of 5 hyperparameter optimization (100 epochs, 20 iterations) using hyperopt module and TPE search algorithm. The hyperoptimization is then followed by a re-training of the best model settings.
- numpy v1.20
- scipy v1.9.3
- rdkit v2022.03.5
- pytorch v1.12.1
- xgboost v1.7.6
- deepchem v2.7.1
- chemprop v1.6.0
- hyperopt v0.2.7
- dgllife v0.3.2
- dgl v1.1.2
- clone the repo:
git clone https://github.com/danielvik/arc_rtpred.git cd arc_rtpred- create conda environment:
conda env create -f environment.yml conda activate arc_rtpred
The build from the yml file is not GPU-enabled, so installing pytorch-gpu and xgboost-gpu should be done after building the environment.
The notebook contains a step-by-step walkthrough of data featurization, splitting, model training and evaluation using the public METLIN SMRT dataset.
Model training scripts can then be run once the directories ./data/features/<your_features>/cv_splits/ contain featurized data.