dyphAI dynamic pharmacophore modeling with AI: a tool for efficient screening of new acetylcholinesterase inhibitors
Therapeutic strategies for Alzheimer’s disease (AD) often involve inhibiting acetylcholinesterase (AChE), underscoring the need for novel inhibitors with high selectivity and minimal side effects. A detailed analysis of the protein-ligand pharmacophore dynamics can facilitate this. In this study, we...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Chemistry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fchem.2025.1479763/full |
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| author | Yasser Hayek-Orduz Dorian Armando Acevedo-Castro Dorian Armando Acevedo-Castro Juan Sebastián Saldarriaga Escobar Brandon Eli Ortiz-Domínguez María Francisca Villegas-Torres Paola A. Caicedo Álvaro Barrera-Ocampo Natalie Cortes Edison H. Osorio Andrés Fernando González Barrios |
| author_facet | Yasser Hayek-Orduz Dorian Armando Acevedo-Castro Dorian Armando Acevedo-Castro Juan Sebastián Saldarriaga Escobar Brandon Eli Ortiz-Domínguez María Francisca Villegas-Torres Paola A. Caicedo Álvaro Barrera-Ocampo Natalie Cortes Edison H. Osorio Andrés Fernando González Barrios |
| author_sort | Yasser Hayek-Orduz |
| collection | DOAJ |
| description | Therapeutic strategies for Alzheimer’s disease (AD) often involve inhibiting acetylcholinesterase (AChE), underscoring the need for novel inhibitors with high selectivity and minimal side effects. A detailed analysis of the protein-ligand pharmacophore dynamics can facilitate this. In this study, we developed and employed dyphAI, an innovative approach integrating machine learning models, ligand-based pharmacophore models, and complex-based pharmacophore models into a pharmacophore model ensemble. This ensemble captures key protein-ligand interactions, including π-cation interactions with Trp-86 and several π-π interactions with residues Tyr-341, Tyr-337, Tyr-124, and Tyr-72. The protocol identified 18 novel molecules from the ZINC database with binding energy values ranging from −62 to −115 kJ/mol, suggesting their strong potential as AChE inhibitors. To further validate the predictions, nine molecules were acquired and tested for their inhibitory activity against human AChE. Experimental results revealed that molecules, 4 (P-1894047), with its complex multi-ring structure and numerous hydrogen bond acceptors, and 7 (P-2652815), characterized by a flexible, polar framework with ten hydrogen bond donors and acceptors, exhibited IC₅₀ values lower than or equal to that of the control (galantamine), indicating potent inhibitory activity. Similarly, molecules 5 (P-1205609), 6 (P-1206762), 8 (P-2026435), and 9 (P-533735) also demonstrated strong inhibition. In contrast, molecule 3 (P-617769798) showed a higher IC50 value, and molecules 1 (P-14421887) and 2 (P-25746649) yielded inconsistent results, likely due to solubility issues in the experimental setup. These findings underscore the value of integrating computational predictions with experimental validation, enhancing the reliability of virtual screening in the discovery of potent enzyme inhibitors. |
| format | Article |
| id | doaj-art-9a8f3e3c68064a95a78bf769d3abf0aa |
| institution | Kabale University |
| issn | 2296-2646 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Chemistry |
| spelling | doaj-art-9a8f3e3c68064a95a78bf769d3abf0aa2025-02-04T06:31:50ZengFrontiers Media S.A.Frontiers in Chemistry2296-26462025-02-011310.3389/fchem.2025.14797631479763dyphAI dynamic pharmacophore modeling with AI: a tool for efficient screening of new acetylcholinesterase inhibitorsYasser Hayek-Orduz0Dorian Armando Acevedo-Castro1Dorian Armando Acevedo-Castro2Juan Sebastián Saldarriaga Escobar3Brandon Eli Ortiz-Domínguez4María Francisca Villegas-Torres5Paola A. Caicedo6Álvaro Barrera-Ocampo7Natalie Cortes8Edison H. Osorio9Andrés Fernando González Barrios10Grupo de Diseño de Productos y Procesos (GDPP), Department of Chemical and Food Engineering, Universidad de los Andes, Bogotá, ColombiaGrupo de Diseño de Productos y Procesos (GDPP), Department of Chemical and Food Engineering, Universidad de los Andes, Bogotá, ColombiaComputational Bio-Organic Chemistry (COBO), Department of Chemistry, Universidad de los Andes, Bogotá, ColombiaGrupo Natura, Facultad de Ingenieria, Diseño y Ciencias Aplicadas, Departamento de Ciencias Biológicas, Bioprocesos y Biotecnología, Universidad ICESI, Cali, ColombiaGrupo Natura, Facultad de Ingenieria, Diseño y Ciencias Aplicadas, Departamento de Ciencias Biológicas, Bioprocesos y Biotecnología, Universidad ICESI, Cali, ColombiaCentro de Investigaciones Microbiológicas (CIMIC), Department of Biological Sciences, Universidad de los Andes, Bogotá, ColombiaGrupo Natura, Facultad de Ingenieria, Diseño y Ciencias Aplicadas, Departamento de Ciencias Biológicas, Bioprocesos y Biotecnología, Universidad ICESI, Cali, ColombiaGrupo Natura, Facultad de Ingenieria, Diseño y Ciencias Aplicadas, Departamento de Ciencias Farmacéuticas y Químicas, Universidad ICESI, Cali, ColombiaGrupo de Investigación en Química Bioorgánica y Sistemas Moleculares (QBOSMO), Faculty of Natural Sciences and Mathematics, Universidad de Ibagué, Ibagué, ColombiaGrupo de Investigación en Química Bioorgánica y Sistemas Moleculares (QBOSMO), Faculty of Natural Sciences and Mathematics, Universidad de Ibagué, Ibagué, ColombiaGrupo de Diseño de Productos y Procesos (GDPP), Department of Chemical and Food Engineering, Universidad de los Andes, Bogotá, ColombiaTherapeutic strategies for Alzheimer’s disease (AD) often involve inhibiting acetylcholinesterase (AChE), underscoring the need for novel inhibitors with high selectivity and minimal side effects. A detailed analysis of the protein-ligand pharmacophore dynamics can facilitate this. In this study, we developed and employed dyphAI, an innovative approach integrating machine learning models, ligand-based pharmacophore models, and complex-based pharmacophore models into a pharmacophore model ensemble. This ensemble captures key protein-ligand interactions, including π-cation interactions with Trp-86 and several π-π interactions with residues Tyr-341, Tyr-337, Tyr-124, and Tyr-72. The protocol identified 18 novel molecules from the ZINC database with binding energy values ranging from −62 to −115 kJ/mol, suggesting their strong potential as AChE inhibitors. To further validate the predictions, nine molecules were acquired and tested for their inhibitory activity against human AChE. Experimental results revealed that molecules, 4 (P-1894047), with its complex multi-ring structure and numerous hydrogen bond acceptors, and 7 (P-2652815), characterized by a flexible, polar framework with ten hydrogen bond donors and acceptors, exhibited IC₅₀ values lower than or equal to that of the control (galantamine), indicating potent inhibitory activity. Similarly, molecules 5 (P-1205609), 6 (P-1206762), 8 (P-2026435), and 9 (P-533735) also demonstrated strong inhibition. In contrast, molecule 3 (P-617769798) showed a higher IC50 value, and molecules 1 (P-14421887) and 2 (P-25746649) yielded inconsistent results, likely due to solubility issues in the experimental setup. These findings underscore the value of integrating computational predictions with experimental validation, enhancing the reliability of virtual screening in the discovery of potent enzyme inhibitors.https://www.frontiersin.org/articles/10.3389/fchem.2025.1479763/fulldockingMD simulationspharmacophoreacetylcholinesterasemachine learning |
| spellingShingle | Yasser Hayek-Orduz Dorian Armando Acevedo-Castro Dorian Armando Acevedo-Castro Juan Sebastián Saldarriaga Escobar Brandon Eli Ortiz-Domínguez María Francisca Villegas-Torres Paola A. Caicedo Álvaro Barrera-Ocampo Natalie Cortes Edison H. Osorio Andrés Fernando González Barrios dyphAI dynamic pharmacophore modeling with AI: a tool for efficient screening of new acetylcholinesterase inhibitors Frontiers in Chemistry docking MD simulations pharmacophore acetylcholinesterase machine learning |
| title | dyphAI dynamic pharmacophore modeling with AI: a tool for efficient screening of new acetylcholinesterase inhibitors |
| title_full | dyphAI dynamic pharmacophore modeling with AI: a tool for efficient screening of new acetylcholinesterase inhibitors |
| title_fullStr | dyphAI dynamic pharmacophore modeling with AI: a tool for efficient screening of new acetylcholinesterase inhibitors |
| title_full_unstemmed | dyphAI dynamic pharmacophore modeling with AI: a tool for efficient screening of new acetylcholinesterase inhibitors |
| title_short | dyphAI dynamic pharmacophore modeling with AI: a tool for efficient screening of new acetylcholinesterase inhibitors |
| title_sort | dyphai dynamic pharmacophore modeling with ai a tool for efficient screening of new acetylcholinesterase inhibitors |
| topic | docking MD simulations pharmacophore acetylcholinesterase machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fchem.2025.1479763/full |
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