ReviewJournal of molecular modeling2020
Druggability and drug-likeness concepts in drug design: are biomodelling and predictive tools having their say?
Review in Journal of molecular modeling, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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
44 citing papers in PubMed.
- Design, synthesis, and biological evaluation of novel isobenzofuran-1(3H)-one derivatives with antioxidant properties and improved oral bioavailability.Molecular diversity · 2026Article
- A proteome-driven strategy to discover novel therapeutic targets in Orientia tsutsugamushi.Antonie van Leeuwenhoek · 2026Article
- In Silico Druggability Assessment ofAntibiotics (Basel, Switzerland) · 2026Article
- Abietic Acid Enhances the Sedative Activity of Diazepam: In vivo Approach along with Receptor Binding Affinity and Molecular Interaction with the GABAergic System.ChemistryOpen · 2026Article
- Artificial Intelligence and the Discovery of Antibiotics: Reinventing with Opportunities, Challenges, and Clinical Translation.Antibiotics (Basel, Switzerland) · 2026Review
- Evaluation of the Sedative Activity of Naringenin: In Vivo Study with Pharmacokinetics and Molecular Docking Insights.ChemistryOpen · 2025Article
- Identification of Structurally Novel KRASJournal of medicinal chemistry · 2025Article
- New Difunctional Derivatives of Betulin: Preparation, Characterization and Antiproliferative Potential.Molecules (Basel, Switzerland) · 2025Article
- Derivatization of Microcystins Can Increase Target Inhibition while Reducing Cellular Uptake.Journal of natural products · 2025Article
- Advanced Technologies for Cancer Immunotherapy: Focus on Gastrointestinal Cancers.Anti-cancer agents in medicinal chemistry · 2025Review
- Computational advances in the design and discovery of artemis inhibitors for radiosensitization in cancer therapy.Frontiers in chemistry · 2025Article
- Investigation ofIn silico pharmacology · 2025Article
- Exploring the Pharmacological Potential of Carrageenan Disaccharides as Antitumor Agents: An In Silico Approach.Marine drugs · 2024Article
- Alzheimer's Disease Immunotherapy and Mimetic Peptide Design for Drug Development: Mutation Screening, Molecular Dynamics, and a Quantum Biochemistry Approach Focusing on Aducanumab::Aβ2-7 Binding Affinity.ACS chemical neuroscience · 2024Article
- Role of Alkannin in the Therapeutic Targeting of Protein-Tyrosine Phosphatase 1B and Aldose Reductase in Type 2 Diabetes: AnACS omega · 2024Article
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- Computational approach for drug discovery against Gardnerella vaginalis in quest for safer and effective treatments for bacterial vaginosis.Scientific reports · 2024Article
- Editorial of Special Issue "Current Trends in Chemistry Towards Biology".International journal of molecular sciences · 2024Article
- Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The drug discovery process typically involves target identification and design of suitable drug molecules against these targets. Despite decades of experimental investigations in the drug discovery domain, about 96% overall failure rate has been recorded in drug development due to the "undruggability" of various identified disease targets, in addition to other challenges. Likewise, the high attrition rate of drug candidates in the drug discovery process has also become an enormous challenge for the pharmaceutical industry. To alleviate this negative outlook, new trends in drug discovery have emerged. By drifting away from experimental research methods, computational tools and big data are becoming valuable in the prediction of biological target druggability and the drug-likeness of potential therapeutic agents. These tools have proven to be useful in saving time and reducing research costs. As with any emerging technique, however, controversial opinions have been presented regarding the validation of predictive computational tools. To address the challenges associated with these varying opinions, this review attempts to highlight the principles of druggability and drug-likeness and their recent advancements in the drug discovery field. Herein, we present the different computational tools and their reliability of predictive analysis in the drug discovery domain. We believe that this report would serve as a comprehensive guide towards computational-oriented drug discovery research. Graphical abstract Highlights of methods for assessing the druggability of biological targets.
Indexed as
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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.