ReviewMolecular informatics2020
Cheminformatics in Natural Product-based Drug Discovery.
Review in Molecular informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
54 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis and review of in silico methods in drug discovery - part 1: technological evolution and trends from big data to chemical space.The pharmacogenomics journal · 2025Pooled it
- Analyzing the Chemical Space of Opioid Receptor Agonists and Antagonists: Insights from Computational Models.ACS omega · 2026Article
- Repositioning Natural Products in Modern Drug Discovery: Technological Innovation, Systems Pharmacology, and Pathological Validation.International journal of molecular sciences · 2026Review
- Machine Learning Approaches for Compound-Target Interaction Prediction: A Review.Foods (Basel, Switzerland) · 2026Review
- Targeting tumor-associated macrophages through phytochemicals: a promising strategy for cold tumor therapy.Chinese medicine · 2026Review
- The Computational Revolution in Natural Product Research: A Data-Driven Roadmap for Next-Generation Drug Development.Biology · 2026Review
- Nature meets machine: the AI renaissance in natural product drug discovery.Natural products and bioprospecting · 2026Review
- Black Gold in Medicine: Rediscovering the Pharmacological Potential.Molecules (Basel, Switzerland) · 2026Review
- Targeting cancer hallmarks with microbiome-derived bacteriocins and postbiotics: molecular mechanisms and translational opportunities in oncology.Frontiers in oncology · 2026Review
- BioRGroup dataset: R-group expansion of ChEBI molecules referenced in the Rhea database.Scientific data · 2025Article
- Optimization model for predicting quantum theoretic characteristics of hexagonal systems using temperature dependent graph theoretic estimators.Scientific reports · 2025Article
- Natural compounds attenuate combined chromium and arsenic-induced oxidative stress and nephritic apoptosis by activating the Nrf2/Keap1 signaling and associated xenobiotic metabolizing enzymes.Scientific reports · 2025Article
- Leveraging tree-transformer VAE with fragment tokenization for high-performance large chemical model generation.Communications chemistry · 2025Article
- GABAPharmaceuticals (Basel, Switzerland) · 2025Article
- Natural product databases for drug discovery: Features and applications.Pharmaceutical science advances · 2024Review
- Cheminformatics-Guided Exploration of Synthetic Marine Natural Product-Inspired Brominated Indole-3-Glyoxylamides and Their Potentials for Drug Discovery.Molecules (Basel, Switzerland) · 2024Article
- Natural Products Have Increased Rates of Clinical Trial Success throughout the Drug Development Process.Journal of natural products · 2024Article
- Gram matrix: an efficient representation of molecular conformation and learning objective for molecular pretraining.Briefings in bioinformatics · 2024Article
- Effectiveness of molecular fingerprints for exploring the chemical space of natural products.Journal of cheminformatics · 2024Article
- The recent advance and prospect of natural source compounds for the treatment of heart failure.Heliyon · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product-based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product-likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products-inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule-based, similarity-based, shape-based, pharmacophore-based and network-based approaches, docking and machine learning methods.
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What OpenQuestion holds
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.