ReviewJournal of molecular modeling2025
Conceptual density functional theory in drug discovery: an overview.
Review in Journal of molecular modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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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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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Who cites it
5 citing papers in PubMed.
- New Chromene Derivatives as Promising VEGFR-2 Inhibitors: Experimental Cytotoxicity and Comprehensive Computational Characterization.Archiv der Pharmazie · 2026Article
- Integrated AI-Driven Discovery of MAPK3 Inhibitors for Oral Inflammatory and Proliferative Diseases.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Synthesis, structural characterization, and dual DNA/HSA binding of novel Palladium(II) violurate complex with selective p53/Caspase-3-mediated anticancer activity.Scientific reports · 2026Article
- Phytochemical Profiling and Computational Screening ofPharmaceuticals (Basel, Switzerland) · 2026Article
- Mechanistic insights into daidzin from Glycine max against breast cancer via network pharmacology and multi-level molecular modeling.PloS one · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
contextAs a development from Density Functional Theory, Conceptual Density Functional Theory (CDFT) has emerged as a valuable complementary approach in modern drug discovery. Both global and local chemical reactivity descriptors within the framework of CDFT have made it easier to study chemical reactions and how drugs affect their targets. They aid to predict the electronic properties of drug candidates, which simplifies the process of enhancing important characteristics such as their binding affinity, level of selectivity among others. They help in exploring and analyzing inhibitors that work specifically and allow to predict the negative effects those inhibitors may have. When applied with other approaches such as molecular docking and QSAR modeling, CDFT strengthens the whole drug discovery process. This review highlights the increasing importance of CDFT in rational drug design and especially in context of combined efforts with molecular docking and QSAR modeling. It also provides a fundamental knowledge of CDFT-based descriptors and their role in drug discovery process. In addition to highlighting existing challenges, this review outlines potential future directions.
Indexed as
Identifiers
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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.