ArticleNature communications2021
Bioactivity descriptors for uncharacterized chemical compounds.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
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- Discovery of natural RORγt inhibitor using machine learning, virtual screening, and in vivo validation.Journal of advanced research · 2026Article
- Phenotypic AI-based design of cell-specific small molecule cytotoxics.Communications chemistry · 2026Article
- Collision-free morgan fingerprints: a principled approach to enhance machine learning performance and interpretability in chemistry.Journal of cheminformatics · 2026Article
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- Decoding GuaB: Machine Learning-Powered Discovery of Enzyme Inhibitors Against the SuperbugPharmaceuticals (Basel, Switzerland) · 2025Article
- Integration of diverse bioactivity data into the Chemical Checker compound universe.Nature protocols · 2025Review
- Artificial intelligence coupled to pharmacometrics modelling to tailor malaria and tuberculosis treatment in Africa.Nature communications · 2025Article
- A bottom-up approach to find lead compounds in expansive chemical spaces.Communications chemistry · 2025Article
- Machine learning-assisted analysis of serum metabolomics and network pharmacology reveals the effective compound from herbal formula against alcoholic liver injury.Chinese medicine · 2025Article
- MAYA (Multiple ActivitY Analyzer): An Open Access Tool to Explore Structure-Multiple Activity Relationships in the Chemical Universe.Molecular informatics · 2025Article
- APBIO: bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactions.Journal of cheminformatics · 2025Article
- Discovering geroprotectors through the explainable artificial intelligence-based platform AgeXtend.Nature aging · 2025Article
- Theoretical-Cheminformatic Study of Four Indolylphytoquinones, Prospective Anticancer Candidates.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Identification of potent anti-immunogenic agents through virtual screening, 3D-QSAR studies, and in vitro experiments.Molecular diversity · 2024Article
- Comprehensive detection and characterization of human druggable pockets through binding site descriptors.Nature communications · 2024Article
- Stereochemically-aware bioactivity descriptors for uncharacterized chemical compounds.Journal of cheminformatics · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chemical descriptors encode the physicochemical and structural properties of small molecules, and they are at the core of chemoinformatics. The broad release of bioactivity data has prompted enriched representations of compounds, reaching beyond chemical structures and capturing their known biological properties. Unfortunately, bioactivity descriptors are not available for most small molecules, which limits their applicability to a few thousand well characterized compounds. Here we present a collection of deep neural networks able to infer bioactivity signatures for any compound of interest, even when little or no experimental information is available for them. Our signaturizers relate to bioactivities of 25 different types (including target profiles, cellular response and clinical outcomes) and can be used as drop-in replacements for chemical descriptors in day-to-day chemoinformatics tasks. Indeed, we illustrate how inferred bioactivity signatures are useful to navigate the chemical space in a biologically relevant manner, unveiling higher-order organization in natural product collections, and to enrich mostly uncharacterized chemical libraries for activity against the drug-orphan target Snail1. Moreover, we implement a battery of signature-activity relationship (SigAR) models and show a substantial improvement in performance, with respect to chemistry-based classifiers, across a series of biophysics and physiology activity prediction benchmarks.
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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.