ReviewMethods in molecular biology (Clifton, N.J.)2025
Overview of Molecular Modeling in Drug Discovery with a Special Emphasis on the Applications of Artificial Intelligence.
Review in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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0 citing papers in PubMed.
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Authors and funding
1 author.
Funding
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Abstract
Molecular modeling is a process to predict the structure of a molecule in the absence of wet-lab-based methods. This is a computational prediction process and is often used as a starting point for bench works. There are different algorithms available for the purpose with varying degrees of accuracies. In general, the main focus of the work is directed toward building the structures of biological macromolecules or their complexes. In this review the basic principle of methods of molecular modeling will be reviewed. A special emphasis will be given on thermodynamic aspects of molecular interactions involved in the process of building of the models. The flowchart of the method will be provided. Finally, the algorithm behind some of the popular modeling tools will be analyzed. The readers of the article may gain some first-hand knowledge of the process, which they might use for their computational experimentations.
Indexed as
Identifiers
40553324What 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.