  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.
