Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation.
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| Formato: | Artículo científico |
| Lenguaje: | en |
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Journal of chemical information and modeling
2024
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| _version_ | 1868266275369648130 |
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| author | Soares, Renato Azevedo, Luísa Vasconcelos, Vitor Pratas, Diogo Sousa, Sérgio F Carneiro, João |
| author_facet | Soares, Renato Azevedo, Luísa Vasconcelos, Vitor Pratas, Diogo Sousa, Sérgio F Carneiro, João Soares, Renato Azevedo, Luísa Vasconcelos, Vitor Pratas, Diogo Sousa, Sérgio F Carneiro, João |
| collection | PubMed - marine biology |
| contents | Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation. Soares, Renato Azevedo, Luísa Vasconcelos, Vitor Pratas, Diogo Sousa, Sérgio F Carneiro, João Cyanobacteria Machine Learning Biodegradation, Environmental Drug Discovery Molecular Docking Simulation Databases, Factual Databases, Chemical Humans Software Cyanobacteria strains have the potential to produce bioactive compounds that can be used in therapeutics and bioremediation. Therefore, compiling all information about these compounds to consider their value as bioresources for industrial and research applications is essential. In this study, a searchable, updated, curated, and downloadable database of cyanobacteria bioactive compounds was designed, along with a machine-learning model to predict the compounds' targets of newly discovered molecules. A Python programming protocol obtained 3431 cyanobacteria bioactive compounds, 373 unique protein targets, and 3027 molecular descriptors. PaDEL-descriptor, Mordred, and Drugtax software were used to calculate the chemical descriptors for each bioactive compound database record. The biochemical descriptors were then used to determine the most promising protein targets for human therapeutic approaches and environmental bioremediation using the best machine learning (ML) model. The creation of our database, coupled with the integration of computational docking protocols, represents an innovative approach to understanding the potential of cyanobacteria bioactive compounds. This resource, adhering to the findability, accessibility, interoperability, and reuse of digital assets (FAIR) principles, is an excellent tool for pharmaceutical and bioremediation researchers. Moreover, its capacity to facilitate the exploration of specific compounds' interactions with environmental pollutants is a significant advancement, aligning with the increasing reliance on data science and machine learning to address environmental challenges. This study is a notable step forward in leveraging cyanobacteria for both therapeutic and ecological sustainability. |
| format | Artículo científico |
| id | pubmed_39602490 |
| institution | PubMed |
| language | en |
| publishDate | 2024 |
| publisher | Journal of chemical information and modeling |
| record_format | pubmed |
| spellingShingle | Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation. Soares, Renato Azevedo, Luísa Vasconcelos, Vitor Pratas, Diogo Sousa, Sérgio F Carneiro, João Cyanobacteria Machine Learning Biodegradation, Environmental Drug Discovery Molecular Docking Simulation Databases, Factual Databases, Chemical Humans Software Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation. Soares, Renato Azevedo, Luísa Vasconcelos, Vitor Pratas, Diogo Sousa, Sérgio F Carneiro, João Cyanobacteria Machine Learning Biodegradation, Environmental Drug Discovery Molecular Docking Simulation Databases, Factual Databases, Chemical Humans Software Cyanobacteria strains have the potential to produce bioactive compounds that can be used in therapeutics and bioremediation. Therefore, compiling all information about these compounds to consider their value as bioresources for industrial and research applications is essential. In this study, a searchable, updated, curated, and downloadable database of cyanobacteria bioactive compounds was designed, along with a machine-learning model to predict the compounds' targets of newly discovered molecules. A Python programming protocol obtained 3431 cyanobacteria bioactive compounds, 373 unique protein targets, and 3027 molecular descriptors. PaDEL-descriptor, Mordred, and Drugtax software were used to calculate the chemical descriptors for each bioactive compound database record. The biochemical descriptors were then used to determine the most promising protein targets for human therapeutic approaches and environmental bioremediation using the best machine learning (ML) model. The creation of our database, coupled with the integration of computational docking protocols, represents an innovative approach to understanding the potential of cyanobacteria bioactive compounds. This resource, adhering to the findability, accessibility, interoperability, and reuse of digital assets (FAIR) principles, is an excellent tool for pharmaceutical and bioremediation researchers. Moreover, its capacity to facilitate the exploration of specific compounds' interactions with environmental pollutants is a significant advancement, aligning with the increasing reliance on data science and machine learning to address environmental challenges. This study is a notable step forward in leveraging cyanobacteria for both therapeutic and ecological sustainability. |
| title | Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation. |
| topic | Cyanobacteria Machine Learning Biodegradation, Environmental Drug Discovery Molecular Docking Simulation Databases, Factual Databases, Chemical Humans Software |
| url | https://pubmed.ncbi.nlm.nih.gov/39602490/ |