Bioactive structures for inhibitors of Candida auris polymerase enzyme by artificial intelligence

Present new bioactive compounds, created by De novo Drug Design and artificial intelligence (AI), as possible inhibitors of polymerase. MolAICal's AI module was configured to identify FDA-approved molecular fragments with therapeutic effectiveness against polymerase, where the model with optimi...

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Vydané v:Future medicinal chemistry Ročník 17; číslo 8; s. 869
Hlavní autori: Pinto, Francisco Das Chagas Lima, Cabongo, Sadrack Queque, João, Pedro Paulino, Lima, Maria Do Socorro Pereira Costa, Paiva, Maria Mabelle Pereira Costa, Madureira, Junilson Martinho Canjanja, Caluaco, Bernardino Joaquim, Colares, Regilany Paulo, Neto, Moises Maia, Dos Santos, Hélcio Silva, Marinho, Emmanuel Silva, da Fonseca, Aluísio Marques
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England 18.04.2025
Predmet:
Antifungal Agents - chemistry
Antifungal Agents - pharmacology
Artificial Intelligence
Candida - drug effects
Candida - enzymology
Drug Design
Enzyme Inhibitors - chemistry
Enzyme Inhibitors - pharmacology
Humans
Molecular Docking Simulation
Molecular Dynamics Simulation
Molecular Structure
deep learning
molecular docking
molecular dynamics
Protease
superfungo
ISSN:1756-8927, 1756-8927
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Shrnutí:Present new bioactive compounds, created by De novo Drug Design and artificial intelligence (AI), as possible inhibitors of polymerase. MolAICal's AI module was configured to identify FDA-approved molecular fragments with therapeutic effectiveness against polymerase, where the model with optimized synthetic accessibility and structural complexity was subjected to docking and molecular dynamics simulations and pharmacokinetic prediction. Among 1,722 new forms, the Hit-960 compound stood out for its high bioaffinity and stability, with a binding energy of -9.12 kcal/mol and 75% synthetic accessibility. Clinical studies are recommended to test its efficacy, contributing to the development of new treatments for infections.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1756-8927
1756-8927
DOI:10.1080/17568919.2025.2491301