Are 2D fingerprints still valuable for drug discovery?

TL;DR

This study evaluates the effectiveness of classical 2D molecular fingerprints in drug discovery tasks, finding they perform comparably to advanced 3D structure-based models in several key applications.

Contribution

The paper provides a comprehensive comparison of 2D fingerprints with 3D structure-based methods across multiple drug discovery datasets, highlighting the continued relevance of 2D fingerprints.

Findings

2D fingerprints perform well in toxicity, solubility, and binding affinity predictions.

2D models are comparable to 3D models in several tasks.

3D models outperform 2D in complex protein-ligand binding predictions.

Abstract

Recently, molecular fingerprints extracted from three-dimensional (3D) structures using advanced mathematics, such as algebraic topology, differential geometry, and graph theory have been paired with efficient machine learning, especially deep learning algorithms to outperform other methods in drug discovery applications and competitions. This raises the question of whether classical 2D fingerprints are still valuable in computer-aided drug discovery. This work considers 23 datasets associated with four typical problems, namely protein-ligand binding, toxicity, solubility and partition coefficient to assess the performance of eight 2D fingerprints. Advanced machine learning algorithms including random forest, gradient boosted decision tree, single-task deep neural network and multitask deep neural network are employed to construct efficient 2D-fingerprint based models. Additionally, appropriate consensus models are built to further enhance the performance of 2D-fingerprintbased methods. It is demonstrated that 2D-fingerprint-based models perform as well as the state-of-the-art 3D structure-based models for the predictions of toxicity, solubility, partition coefficient and protein-ligand binding affinity based on only ligand information. However, 3D structure-based models outperform 2D fingerprint-based methods in complex-based protein-ligand binding affinity predictions.