# ArtiDock: Accurate Machine Learning Approach to Protein–Ligand Docking Optimized for High-Throughput Virtual Screening

## TL;DR

ArtiDock is a machine learning-based docking method that improves accuracy and efficiency for drug discovery screening.

## Contribution

ArtiDock introduces a novel ML-based docking approach optimized for high-throughput virtual screening with improved accuracy and performance.

## Key findings

- ArtiDock is 29–38% more accurate than leading docking tools like AutoDock, Vina, and Glide.
- It excels in challenging scenarios with unbound proteins and binding sites containing ions and structured water.
- ArtiDock achieves competitive accuracy at higher throughput compared to AI docking and cofolding methods.

## Abstract

Classical protein–ligand docking has been a cornerstone
technique in computational drug discovery for decades but has reached
an accuracy and performance plateau. Recently introduced Machine Learning
(ML)-based docking methods offer a promising paradigm shift, but their
practical adoption is hampered by accuracy-to-speed trade-offs, inadequate
benchmarking standards, and questionable chemical validity of predicted
poses. In this study, we introduce ArtiDockan ML-based docking
technique optimized for high-throughput virtual screening applications.
To evaluate ArtiDock, we developed a dedicated performance and accuracy
benchmark for pocket-specific rigid protein–ligand docking,
which mimics realistic industrial drug discovery scenarios and is
based on the novel PLINDER data set. We demonstrate that ArtiDock
is 29–38% more accurate in comparison to leading open-source
and commercial classical docking techniques such as AutoDock, Vina,
and Glide, while providing a low computational cost. ArtiDock notably
excels in challenging docking scenarios involving unbound protein
structures and binding sites containing ions and structured water
molecules. Additionally, we demonstrated competitive accuracy of our
approach at considerably higher throughput compared to a wide range
of AI docking and AI cofolding methods using the PoseX benchmark.
Our results show that ArtiDock could be considered as a method of
choice in high-throughput virtual screening scenarios.