Are 2D fingerprints still valuable for drug discovery?

Kaifu Gao, Duc Duy Nguyen, Vishnu Sresht, Alan M. Mathiowetz, Meihua Tu, Guo Wei Wei

Research output: Contribution to journalArticlepeer-review

69 Scopus citations


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-fingerprint-based 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.

Original languageEnglish
Pages (from-to)8373-8390
Number of pages18
JournalPhysical Chemistry Chemical Physics
Issue number16
StatePublished - Apr 28 2020

Bibliographical note

Funding Information:
This work was supported in part by NSF Grants DMS-1721024, DMS-1761320, and IIS1900473 and NIH grant GM126189.

Publisher Copyright:
© the Owner Societies 2020.

ASJC Scopus subject areas

  • Physics and Astronomy (all)
  • Physical and Theoretical Chemistry


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