cidalsDB: an AI-empowered platform for anti-pathogen therapeutics research
Abstract Computer-aided drug discovery (CADD) is nurtured by late advances in big data analytics and Artificial Intelligence (AI) towards enhanced drug discovery (DD) outcomes. In this context, reliable datasets are of utmost importance. We herein present CidalsDB a novel web server for AI-assisted...
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| Format: | Article |
| Language: | English |
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BMC
2024-11-01
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| Series: | Journal of Cheminformatics |
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| Online Access: | https://doi.org/10.1186/s13321-024-00929-7 |
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| author | Emna Harigua-Souiai Ons Masmoudi Samer Makni Rafeh Oualha Yosser Z. Abdelkrim Sara Hamdi Oussama Souiai Ikram Guizani |
| author_facet | Emna Harigua-Souiai Ons Masmoudi Samer Makni Rafeh Oualha Yosser Z. Abdelkrim Sara Hamdi Oussama Souiai Ikram Guizani |
| author_sort | Emna Harigua-Souiai |
| collection | DOAJ |
| description | Abstract Computer-aided drug discovery (CADD) is nurtured by late advances in big data analytics and Artificial Intelligence (AI) towards enhanced drug discovery (DD) outcomes. In this context, reliable datasets are of utmost importance. We herein present CidalsDB a novel web server for AI-assisted DD against infectious pathogens, namely Leishmania parasites and Coronaviruses. We performed a literature search on molecules with validated anti-pathogen effects. Then, we consolidated these data with bioassays from PubChem. Finally, we constructed a database to store these datasets and make them accessible and ready-to-use for the scientific community through CidalsDB, a web-based interface. In a second step, we implemented and optimized four machine learning (ML) and three deep learning (DL) algorithms that optimally predicted the biological activity of molecules. Random Forests (RF), Multi-Layer Perceptron (MLP) and ChemBERTa were the best classifiers of anti-Leishmania molecules, while Gradient Boosting (GB), Graph-Convolutional Network (GCN) and ChemBERTa achieved the best performances on the Coronaviruses dataset. All six models were optimized and deployed through CidalsDB as anti-pathogen activity prediction models. Scientific contribution CidalsDB is an open access web-based tool that allows browsing and access to ready-to-use datasets of anti-pathogen molecules, alongside best performing AI models for biological activity prediction. It offers a democratized no-code platform for AI-based CADD, which shall foster innovation and collaboration within the DD community. CidalsDB is accessible through https://cidalsdb.streamlit.app/ . |
| format | Article |
| id | doaj-art-9ff8ec457dbc46079f85d76fcbe61c77 |
| institution | Kabale University |
| issn | 1758-2946 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj-art-9ff8ec457dbc46079f85d76fcbe61c772024-12-01T12:42:13ZengBMCJournal of Cheminformatics1758-29462024-11-0116111810.1186/s13321-024-00929-7cidalsDB: an AI-empowered platform for anti-pathogen therapeutics researchEmna Harigua-Souiai0Ons Masmoudi1Samer Makni2Rafeh Oualha3Yosser Z. Abdelkrim4Sara Hamdi5Oussama Souiai6Ikram Guizani7Laboratory of Molecular Epidemiology and Experimental Pathology - LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El ManarLaboratory of Molecular Epidemiology and Experimental Pathology - LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El ManarLaboratory of Molecular Epidemiology and Experimental Pathology - LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El ManarLaboratory of Molecular Epidemiology and Experimental Pathology - LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El ManarLaboratory of Molecular Epidemiology and Experimental Pathology - LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El ManarLaboratory of Molecular Epidemiology and Experimental Pathology - LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El ManarLaboratory of BioInformatics, BioMathematics and BioStatistics - LR20IPT09, Institut Pasteur de Tunis, Université de Tunis El ManarLaboratory of Molecular Epidemiology and Experimental Pathology - LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El ManarAbstract Computer-aided drug discovery (CADD) is nurtured by late advances in big data analytics and Artificial Intelligence (AI) towards enhanced drug discovery (DD) outcomes. In this context, reliable datasets are of utmost importance. We herein present CidalsDB a novel web server for AI-assisted DD against infectious pathogens, namely Leishmania parasites and Coronaviruses. We performed a literature search on molecules with validated anti-pathogen effects. Then, we consolidated these data with bioassays from PubChem. Finally, we constructed a database to store these datasets and make them accessible and ready-to-use for the scientific community through CidalsDB, a web-based interface. In a second step, we implemented and optimized four machine learning (ML) and three deep learning (DL) algorithms that optimally predicted the biological activity of molecules. Random Forests (RF), Multi-Layer Perceptron (MLP) and ChemBERTa were the best classifiers of anti-Leishmania molecules, while Gradient Boosting (GB), Graph-Convolutional Network (GCN) and ChemBERTa achieved the best performances on the Coronaviruses dataset. All six models were optimized and deployed through CidalsDB as anti-pathogen activity prediction models. Scientific contribution CidalsDB is an open access web-based tool that allows browsing and access to ready-to-use datasets of anti-pathogen molecules, alongside best performing AI models for biological activity prediction. It offers a democratized no-code platform for AI-based CADD, which shall foster innovation and collaboration within the DD community. CidalsDB is accessible through https://cidalsdb.streamlit.app/ .https://doi.org/10.1186/s13321-024-00929-7Machine learningDeep learningDrug discoveryDatabase |
| spellingShingle | Emna Harigua-Souiai Ons Masmoudi Samer Makni Rafeh Oualha Yosser Z. Abdelkrim Sara Hamdi Oussama Souiai Ikram Guizani cidalsDB: an AI-empowered platform for anti-pathogen therapeutics research Journal of Cheminformatics Machine learning Deep learning Drug discovery Database |
| title | cidalsDB: an AI-empowered platform for anti-pathogen therapeutics research |
| title_full | cidalsDB: an AI-empowered platform for anti-pathogen therapeutics research |
| title_fullStr | cidalsDB: an AI-empowered platform for anti-pathogen therapeutics research |
| title_full_unstemmed | cidalsDB: an AI-empowered platform for anti-pathogen therapeutics research |
| title_short | cidalsDB: an AI-empowered platform for anti-pathogen therapeutics research |
| title_sort | cidalsdb an ai empowered platform for anti pathogen therapeutics research |
| topic | Machine learning Deep learning Drug discovery Database |
| url | https://doi.org/10.1186/s13321-024-00929-7 |
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