# Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors

## TL;DR

A machine-learning model was developed to identify PARP1 inhibitors for cancer treatment, outperforming traditional methods.

## Contribution

A PARP1-specific support vector machine model using protein-ligand fingerprints achieved superior performance in virtual screening.

## Key findings

- A PARP1-specific support vector machine model achieved a NEF1% of 0.588 on the hardest test set.
- Protein-ligand-extracted fingerprints led to the best-performing and most-efficient model.
- Deep learning models performed poorly compared to simpler machine-learning models for this target.

## Abstract

Poly ADP-ribose polymerase 1 (PARP1) is an attractive therapeutic target for cancer treatment. Machine-learning scoring functions constitute a promising approach to discovering novel PARP1 inhibitors. Cutting-edge PARP1-specific machine-learning scoring functions were investigated using semi-synthetic training data from docking activity-labelled molecules: known PARP1 inhibitors, hard-to-discriminate decoys property-matched to them with generative graph neural networks and confirmed inactives. We further made test sets harder by including only molecules dissimilar to those in the training set. Comprehensive analysis of these datasets using five supervised learning algorithms, and protein–ligand fingerprints extracted from docking poses and ligand only features revealed one highly predictive scoring function. This is the PARP1-specific support vector machine-based regressor, when employing PLEC fingerprints, which achieved a high Normalized Enrichment Factor at the top 1% on the hardest test set (NEF1% = 0.588, median of 10 repetitions), and was more predictive than any other investigated scoring function, especially the classical scoring function employed as baseline.

The online version contains supplementary material available at 10.1186/s13321-024-00832-1.

A new scoring tool based on machine-learning was developed to predict PARP1 inhibitors for potential cancer treatment.The majority of PARP1-specific machine-learning models performed better than generic and classical scoring functions.Augmenting the training set with ligand-only Morgan fingerprint features generally resulted in better performing models, but not for the best models where no further improvement was observed.Employing protein-ligand-extracted fingerprints as molecular descriptors led to the best-performing and most-efficient model for predicting PARP1 inhibitors.Deep learning performed poorly on this target in comparison with the simpler ML models.

A new scoring tool based on machine-learning was developed to predict PARP1 inhibitors for potential cancer treatment.

The majority of PARP1-specific machine-learning models performed better than generic and classical scoring functions.

Augmenting the training set with ligand-only Morgan fingerprint features generally resulted in better performing models, but not for the best models where no further improvement was observed.

Employing protein-ligand-extracted fingerprints as molecular descriptors led to the best-performing and most-efficient model for predicting PARP1 inhibitors.

Deep learning performed poorly on this target in comparison with the simpler ML models.

The online version contains supplementary material available at 10.1186/s13321-024-00832-1.