ArticleJournal of chemical theory and computation2012
Druggability Assessment of Allosteric Proteins by Dynamics Simulations in the Presence of Probe Molecules.
Article in Journal of chemical theory and computation, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers.
What it found
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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.
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Who cites it
98 citing papers in PubMed, 170 citations in OpenAlex.
- RinPy, a Python Package for Residue Interaction Network Model to Analyze Protein Structures and Predict Ligand Binding Sites.Journal of chemical information and modeling · 2026Article
- DruGUI 2.0: mapping protein druggability with probe-based molecular dynamics.Bioinformatics (Oxford, England) · 2026Article
- Computational Analysis of ELOVL6 Structure and Inhibition for Rational Drug Design.Journal of chemical information and modeling · 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
- Computational Ligand-Binding Site Prediction.Advances in experimental medicine and biology · 2026Review
- Identifying Novel Inhibitors for Dengue NS2B-NS3 Protease by Combining Topological similarity, Molecular Dynamics, MMGBSA and SiteMap Analysis.Current computer-aided drug design · 2026Article
- Chasing allosteric inhibition of the SARS-CoV-2 PLpro via molecular dynamics simulations with flooding fragments (MDFFr).Journal of computer-aided molecular design · 2025Article
- Boosting Drug Discovery: Expanding the Applicability of Fragment Dissolved Molecular Dynamics to Accelerate Binding Mode Elucidation.Journal of chemical information and modeling · 2025Article
- Resistance to Allosteric Inhibitors.Journal of molecular biology · 2025Review
- Allosteric Autoregulation of Ferroptosis Suppressor Protein 1 Activity by its N-myristoylated Tail.Journal of molecular biology · 2025Article
- MGAT1-Guided complex N-Glycans on CD73 regulate immune evasion in triple-negative breast cancer.Nature communications · 2025Article
- Lysosomal dysfunction and inflammatory sterol metabolism in pulmonary arterial hypertension.Science (New York, N.Y.) · 2025Article
- Recent computational advances in the identification of cryptic binding sites for drug discovery.Bioinformatics advances · 2025Review
- Global hinge sites of proteins as target sites for drug binding.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Exploration of Cryptic Pockets Using Enhanced Sampling Along Normal Modes: A Case Study of KRASJournal of chemical information and modeling · 2024Article
- Which cryptic sites are feasible drug targets?Drug discovery today · 2024Review
- Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots.Journal of chemical information and modeling · 2024Article
- Cosolvent Molecular Dynamics Applied to DPP4, DPP8 and DPP9: Reproduction of Important Binding Features and Use in Inhibitor Design.Journal of chemical information and modeling · 2024Article
- Article
- In silico identification of putative druggable pockets in PRL3, a significant oncology target.Biochemistry and biophysics reports · 2024Article
38 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Druggability assessment of a target protein has emerged in recent years as an important concept in hit-to-lead optimization. A reliable and physically relevant measure of druggability would allow informed decisions on the risk of investing in a particular target. Here, we define "druggability" as a quantitative estimate of binding sites and affinities for a potential drug acting on a specific protein target. In the present study, we describe a new methodology that successfully predicts the druggability and maximal binding affinity for a series of challenging targets, including those that function through allosteric mechanisms. Two distinguishing features of the methodology are (i) simulation of the binding dynamics of a diversity of probe molecules selected on the basis of an analysis of approved drugs and (ii) identification of druggable sites and estimation of corresponding binding affinities on the basis of an evaluation of the geometry and energetics of bound probe clusters. The use of the methodology for a variety of targets such as murine double mutant-2, protein tyrosine phosphatase 1B (PTP1B), lymphocyte function-associated antigen 1, vertebrate kinesin-5 (Eg5), and p38 mitogen-activated protein kinase provides examples for which the method correctly captures the location and binding affinities of known drugs. It also provides insights into novel druggable sites and the target's structural changes that would accommodate, if not promote and stabilize, drug binding. Notably, the ability to identify high affinity spots even in challenging cases such as PTP1B or Eg5 shows promise as a rational tool for assessing the druggability of protein targets and identifying allosteric or novel sites for drug binding.
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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.