ArticlePhysical chemistry chemical physics : PCCP2020
Are 2D fingerprints still valuable for drug discovery?
Article in Physical chemistry chemical physics : PCCP, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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Who cites it
59 citing papers in PubMed.
- Conformer-covariance diagnostics for aqueous solubility.Journal of computer-aided molecular design · 2026Article
- NMR-AI: An Open Platform for NMR-Enhanced Molecular Representations and Physicochemical Property Prediction.Journal of chemical information and modeling · 2026Article
- Two faces of drug repurposing data in machine learning-driven structure-based virtual screening supported by interaction fingerprints.Journal of computer-aided molecular design · 2026Article
- Enhancing Drug Response Prediction in Epilepsy with Emerging Multimodal Models: Focus on Clinical, Pharmacologic, and Genomic Factors.CNS drugs · 2026Review
- Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation.Frontiers in pharmacology · 2026Review
- MetAP DB and Metal-FP: a database and fingerprint framework for advancing metal-based drug discovery.Journal of computer-aided molecular design · 2025Article
- Transfer learning enables discovery of sub-micromolar antibacterials for ESKAPE pathogens from ultra-large chemical spaces.Chemical science · 2025Article
- From NMR to AI: FusingJournal of chemical information and modeling · 2025Article
- Predicting cardiotoxicity in drug development: A deep learning approach.Journal of pharmaceutical analysis · 2025Article
- The topology of molecular representations and its influence on machine learning performance.Journal of cheminformatics · 2025Article
- Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal Learning.Journal of chemical information and modeling · 2025Article
- Neural Network Models for Prediction of Biological Activity using Molecular Dynamics Data: A Case of Photoswitchable Peptides.Molecular informatics · 2025Article
- Predictive Modeling of Yield Sooting Index Using Machine Learning with Uncertainty Estimation.ACS omega · 2025Article
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- Advancing promiscuous aggregating inhibitor analysis with intelligent machine learning classification.Briefings in bioinformatics · 2025Article
- The physics-AI dialogue in drug design.RSC medicinal chemistry · 2025Review
- Understanding Conformation Importance in Data-Driven Property Prediction Models.Journal of chemical information and modeling · 2025Article
- From NMR to AI: Do We NeedJournal of chemical information and modeling · 2025Article
- Mayer-Homology Learning Prediction of Protein-Ligand Binding Affinities.Journal of computational biophysics and chemistry · 2025Article
- Importance of Computer-aided Drug Design in Modern Pharmaceutical Research.Current drug discovery technologies · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
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-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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.