ArticleJournal of cheminformatics2020
One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome.
Article in Journal of cheminformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 150 papers.
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Who cites it
150 citing papers in PubMed, 363 citations in OpenAlex.
- Interpretable Machine Learning Frameworks for QSAR Modeling: A Comparative Study of Classical Algorithms and Transformer-CNN Architectures.ACS omega · 2026Article
- Revisiting molecular descriptors with TDiMS for interpretable intramolecular interactions based on substructure pairs.Nature computational science · 2026Article
- A review of machine learning in toxicology: current practices and reporting gaps.Archives of toxicology · 2026Review
- Explainable Artificial Intelligence (XAI) and Molecular Modeling Techniques to Discover Putative HER2 Inhibitors.International journal of molecular sciences · 2026Article
- VITAGRAPH: building a knowledge graph for biologically relevant learning tasks.Scientific data · 2026Article
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- A historical journey of metabolite-protein interaction discovery: from data harmonization to AI-driven prediction.Briefings in bioinformatics · 2026Review
- Machine Learning and Virtual Screening Methods to Discover Potential Cyclin-Dependent Kinase 2 (CDK2) Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Count your bits: fingerprint benchmarking to assess broad chemical space representation.Journal of cheminformatics · 2026Article
- Challenges and Opportunities in Machine Learning for Light-Emitting Polymers.Macromolecular rapid communications · 2026Review
- HP-MoleQ: An Effective Predictive Model for High-Throughput Screening of Food-Derived Hepatoprotective Compounds.Interdisciplinary sciences, computational life sciences · 2026Article
- A chemoinformatics-guided platform for efficient discovery of RNA-binding small molecules: Proof-of-concept for myotonic dystrophy type 1.bioRxiv : the preprint server for biology · 2026Article
- An Explainable 2D-QSAR Machine Learning Approach for Predicting COX-2 Inhibitory Activity Using Molecular Fingerprints.Pharmaceuticals (Basel, Switzerland) · 2026Article
- A unified multi-scale deep learning framework for molecular property prediction that bridges molecular structures and fingerprinting.Communications chemistry · 2026Article
- CheMLT-F: multitask learning in biochemistry through transformer fusion.Journal of cheminformatics · 2026Article
- Data-Driven Engineering of Thermostable Collagen-Mimetic Peptoid Triple Helices.Macromolecular rapid communications · 2026Article
- GenReP: An Ensemble Model for Predicting TP53 in Response to Pharmaceutical Compounds.Molecules (Basel, Switzerland) · 2026Article
- Cross ionization mode chemical similarity prediction between tandem mass spectra in metabolomics.Nature communications · 2026Article
- PepFoundry: A Pipeline for Building Machine-Learning Ready Representations of Nonstandard Peptides Containing Cycles, Non-natural Residues, Polymer Units, and More.Journal of chemical information and modeling · 2026Article
- Exploring and expanding the chemical multiverse of peptides.Chemical science · 2026Review
90 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundMolecular fingerprints are essential cheminformatics tools for virtual screening and mapping chemical space. Among the different types of fingerprints, substructure fingerprints perform best for small molecules such as drugs, while atom-pair fingerprints are preferable for large molecules such as peptides. However, no available fingerprint achieves good performance on both classes of molecules.
resultsHere we set out to design a new fingerprint suitable for both small and large molecules by combining substructure and atom-pair concepts. Our quest resulted in a new fingerprint called MinHashed atom-pair fingerprint up to a diameter of four bonds (MAP4). In this fingerprint the circular substructures with radii of r = 1 and r = 2 bonds around each atom in an atom-pair are written as two pairs of SMILES, each pair being combined with the topological distance separating the two central atoms. These so-called atom-pair molecular shingles are hashed, and the resulting set of hashes is MinHashed to form the MAP4 fingerprint. MAP4 significantly outperforms all other fingerprints on an extended benchmark that combines the Riniker and Landrum small molecule benchmark with a peptide benchmark recovering BLAST analogs from either scrambled or point mutation analogs. MAP4 furthermore produces well-organized chemical space tree-maps (TMAPs) for databases as diverse as DrugBank, ChEMBL, SwissProt and the Human Metabolome Database (HMBD), and differentiates between all metabolites in HMBD, over 70% of which are indistinguishable from their nearest neighbor using substructure fingerprints.
conclusionMAP4 is a new molecular fingerprint suitable for drugs, biomolecules, and the metabolome and can be adopted as a universal fingerprint to describe and search chemical space. The source code is available at https://github.com/reymond-group/map4 and interactive MAP4 similarity search tools and TMAPs for various databases are accessible at http://map-search.gdb.tools/ and http://tm.gdb.tools/map4/.
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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.