A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases
Currently, the age structure of the world population is changing due to declining birth rates and increasing life expectancy. As a result, physicians worldwide have to treat an increasing number of age-related diseases, of which neurological disorders represent a significant part. In this context, t...
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MDPI AG
2024-10-01
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| Series: | Biomolecules |
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| Online Access: | https://www.mdpi.com/2218-273X/14/10/1330 |
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| author | Caterina Vicidomini Francesco Fontanella Tiziana D’Alessandro Giovanni N. Roviello |
| author_facet | Caterina Vicidomini Francesco Fontanella Tiziana D’Alessandro Giovanni N. Roviello |
| author_sort | Caterina Vicidomini |
| collection | DOAJ |
| description | Currently, the age structure of the world population is changing due to declining birth rates and increasing life expectancy. As a result, physicians worldwide have to treat an increasing number of age-related diseases, of which neurological disorders represent a significant part. In this context, there is an urgent need to discover new therapeutic approaches to counteract the effects of neurodegeneration on human health, and computational science can be of pivotal importance for more effective neurodrug discovery. The knowledge of the molecular structure of the receptors and other biomolecules involved in neurological pathogenesis facilitates the design of new molecules as potential drugs to be used in the fight against diseases of high social relevance such as dementia, Alzheimer’s disease (AD) and Parkinson’s disease (PD), to cite only a few. However, the absence of comprehensive guidelines regarding the strengths and weaknesses of alternative approaches creates a fragmented and disconnected field, resulting in missed opportunities to enhance performance and achieve successful applications. This review aims to summarize some of the most innovative strategies based on computational methods used for neurodrug development. In particular, recent applications and the state-of-the-art of molecular docking and artificial intelligence for ligand- and target-based approaches in novel drug design were reviewed, highlighting the crucial role of in silico methods in the context of neurodrug discovery for neurodegenerative diseases. |
| format | Article |
| id | doaj-art-b4b17b177b3b4cfbbfa6b99ae0342d5d |
| institution | OA Journals |
| issn | 2218-273X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomolecules |
| spelling | doaj-art-b4b17b177b3b4cfbbfa6b99ae0342d5d2025-08-20T02:11:14ZengMDPI AGBiomolecules2218-273X2024-10-011410133010.3390/biom14101330A Survey on Computational Methods in Drug Discovery for Neurodegenerative DiseasesCaterina Vicidomini0Francesco Fontanella1Tiziana D’Alessandro2Giovanni N. Roviello3Institute of Biostructures and Bioimaging-Italian National Council for Research (IBB-CNR), Via De Amicis 95, 80145 Naples, ItalyDepartment of Electrical and Information Engineering “Maurizio Scarano”, University of Cassino and Southern Lazio, 03043 Cassino, ItalyDepartment of Electrical and Information Engineering “Maurizio Scarano”, University of Cassino and Southern Lazio, 03043 Cassino, ItalyInstitute of Biostructures and Bioimaging-Italian National Council for Research (IBB-CNR), Via De Amicis 95, 80145 Naples, ItalyCurrently, the age structure of the world population is changing due to declining birth rates and increasing life expectancy. As a result, physicians worldwide have to treat an increasing number of age-related diseases, of which neurological disorders represent a significant part. In this context, there is an urgent need to discover new therapeutic approaches to counteract the effects of neurodegeneration on human health, and computational science can be of pivotal importance for more effective neurodrug discovery. The knowledge of the molecular structure of the receptors and other biomolecules involved in neurological pathogenesis facilitates the design of new molecules as potential drugs to be used in the fight against diseases of high social relevance such as dementia, Alzheimer’s disease (AD) and Parkinson’s disease (PD), to cite only a few. However, the absence of comprehensive guidelines regarding the strengths and weaknesses of alternative approaches creates a fragmented and disconnected field, resulting in missed opportunities to enhance performance and achieve successful applications. This review aims to summarize some of the most innovative strategies based on computational methods used for neurodrug development. In particular, recent applications and the state-of-the-art of molecular docking and artificial intelligence for ligand- and target-based approaches in novel drug design were reviewed, highlighting the crucial role of in silico methods in the context of neurodrug discovery for neurodegenerative diseases.https://www.mdpi.com/2218-273X/14/10/1330neurodegenerationneurodrugsmachine learningmolecular dockingartificial intelligencedrug discovery |
| spellingShingle | Caterina Vicidomini Francesco Fontanella Tiziana D’Alessandro Giovanni N. Roviello A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases Biomolecules neurodegeneration neurodrugs machine learning molecular docking artificial intelligence drug discovery |
| title | A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases |
| title_full | A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases |
| title_fullStr | A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases |
| title_full_unstemmed | A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases |
| title_short | A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases |
| title_sort | survey on computational methods in drug discovery for neurodegenerative diseases |
| topic | neurodegeneration neurodrugs machine learning molecular docking artificial intelligence drug discovery |
| url | https://www.mdpi.com/2218-273X/14/10/1330 |
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