KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors

TL;DR

KinDEL is a large, publicly available DNA-encoded library dataset for kinase inhibitors, including binding poses and validation data, aimed at advancing machine learning in drug discovery.

Contribution

This paper introduces KinDEL, the first large-scale DEL dataset with binding poses and validation data, facilitating machine learning research in kinase inhibitor discovery.

Findings

KinDEL contains 81 million compounds for MAPK14 and DDR1 kinases.

Provides comprehensive biophysical assay validation data.

Benchmark dataset includes both 2D and 3D structural information.

Abstract

DNA-Encoded Libraries (DELs) represent a transformative technology in drug discovery, facilitating the high-throughput exploration of vast chemical spaces. Despite their potential, the scarcity of publicly available DEL datasets presents a bottleneck for the advancement of machine learning methodologies in this domain. To address this gap, we introduce KinDEL, one of the largest publicly accessible DEL datasets and the first one that includes binding poses from molecular docking experiments. Focused on two kinases, Mitogen-Activated Protein Kinase 14 (MAPK14) and Discoidin Domain Receptor Tyrosine Kinase 1 (DDR1), KinDEL includes 81 million compounds, offering a rich resource for computational exploration. Additionally, we provide comprehensive biophysical assay validation data, encompassing both on-DNA and off-DNA measurements, which we use to evaluate a suite of machine learning techniques, including novel structure-based probabilistic models. We hope that our benchmark, encompassing both 2D and 3D structures, will help advance the development of machine learning models for data-driven hit identification using DELs.