ReviewTrends in pharmacological sciences2020
Selective and Effective: Current Progress in Computational Structure-Based Drug Discovery of Targeted Covalent Inhibitors.
Review in Trends in pharmacological sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Molecular Dynamics-Guided Design and Chemoproteomic Profiling of Covalent Kinase Activity Probes.Journal of the American Chemical Society · 2026Article
- From Serendipity to Strategy: Rationalizing Molecular Glue Discovery and Proximity-Induced Pharmacology through Chemical Biology.Journal of the American Chemical Society · 2026Review
- Article
- The covalent docking software landscape: features and applications in drug design.Briefings in bioinformatics · 2025Review
- Molecular Dynamics-Guided Design and Chemoproteomic Profiling of Covalent Kinase Activity Probes.bioRxiv : the preprint server for biology · 2025Article
- Kinetic Modeling of Covalent Inhibition: Effects of Rapidly Fluctuating Intermediate States.Journal of chemical information and modeling · 2025Article
- Cysteine Alkylation in Enzymes and Transcription Factors: A Therapeutic Strategy for Cancer.Cancers · 2025Review
- Advancements in Protein Kinase Inhibitors: From Discovery to Clinical Applications.Research (Washington, D.C.) · 2025Review
- Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design.Journal of computer-aided molecular design · 2024Article
- Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development.ACS omega · 2024Review
- Two-Step Covalent Docking with Attracting Cavities.Journal of chemical information and modeling · 2023Article
- Reactive Docking: A Computational Method for High-Throughput Virtual Screenings of Reactive Species.Journal of chemical information and modeling · 2023Article
- Docking covalent targets for drug discovery: stimulating the computer-aided drug design community of possible pitfalls and erroneous practices.Molecular diversity · 2023Review
- SCARdock: A Web Server and Manually Curated Resource for Discovering Covalent Ligands.ACS omega · 2023Article
- Biology of Pellino1: a potential therapeutic target for inflammation in diseases and cancers.Frontiers in immunology · 2023Review
- CovBinderInPDB: A Structure-Based Covalent Binder Database.Journal of chemical information and modeling · 2022Article
- Small molecule inhibitors targeting the cancers.MedComm · 2022Review
- Reactivity of Covalent Fragments and Their Role in Fragment Based Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2022Review
- Photopharmacology of Ion Channels through the Light of the Computational Microscope.International journal of molecular sciences · 2021Review
- Structural basis for inhibition of the AAA-ATPase Drg1 by diazaborine.Nature communications · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Targeted covalent inhibitors are currently showing great promise for systems that are normally difficult to target with small molecule therapies. This renewed interest has spurred the refinement of existing computational methods as well as the designof new ones, expanding the toolbox for discovery and optimization of selectiveand effective covalent inhibitors. Commonly applied approaches are covalentdocking methods that predict the conformation of the covalent complex with known residues. More recently, a new predictive method, reactive docking, was developed, building on the growing corpus of data generated by large proteomics experiments. This method was successfully used in several 'inverse drug discovery' programs that use high-throughput techniques to isolate effective compounds based on screening of entire compound libraries based on desired phenotypes.
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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.