ReviewTrends in pharmacological sciences2008
Novel paradigms for drug discovery: computational multitarget screening.
Review in Trends in pharmacological sciences, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.
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.
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
50 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Computational Drug Discovery, Development, and Repurposing for Ebola Virus Disease Treatment.Molecules (Basel, Switzerland) · 2017Pooled it
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- Design and Synthesis of Red-Absorbing Fluoran Leuco Dyes Supported by Computational Screening.ACS omega · 2024Article
- GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions.Cell reports methods · 2024Article
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- Article
- Identifying Protein Features and Pathways Responsible for Toxicity Using Machine Learning and Tox21: Implications for Predictive Toxicology.Molecules (Basel, Switzerland) · 2022Article
- Virtual Screening of Drug-Like Compounds as Potential Inhibitors of the Dengue Virus NS5 Protein.Frontiers in chemistry · 2022Article
- Evaluating the performance of drug-repurposing technologies.Drug discovery today · 2022Review
- Optimal COVID-19 therapeutic candidate discovery using the CANDO platform.Frontiers in pharmacology · 2022Article
- A Deep-Learning Proteomic-Scale Approach for Drug Design.Pharmaceuticals (Basel, Switzerland) · 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
5 authors.
Funding
Abstract
An established paradigm in current drug development is (i) to identify a single protein target whose inhibition is likely to result in the successful treatment of a disease of interest; (ii) to assay experimentally large libraries of small-molecule compounds in vitro and in vivo to identify promising inhibitors in model systems; and (iii) to determine whether the findings are extensible to humans. This complex process, which is largely based on trial and error, is risk-, time- and cost-intensive. Computational (virtual) screening of drug-like compounds simultaneously against the atomic structures of multiple protein targets, taking into account protein-inhibitor dynamics, might help to identify lead inhibitors more efficiently, particularly for complex drug-resistant diseases. Here we discuss the potential benefits of this approach, using HIV-1 and Plasmodium falciparum infections as examples. We propose a virtual drug discovery 'pipeline' that will not only identify lead inhibitors efficiently, but also help minimize side-effects and toxicity, thereby increasing the likelihood of successful therapies.
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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.