Predicting efficacy of drug-carrier nanoparticle designs for cancer treatment: a machine learning-based solution
Abstract Molecular Dynamic (MD) simulations are very effective in the discovery of nanomedicines for treating cancer, but these are computationally expensive and time-consuming. Existing studies integrating machine learning (ML) into MD simulation to enhance the process and enable efficient analysis...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-27729-7 |
| _version_ | 1797952596965588992 |
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| author | Md Raisul Kibria Refo Ilmiya Akbar Poonam Nidadavolu Oksana Havryliuk Sébastien Lafond Sepinoud Azimi |
| author_facet | Md Raisul Kibria Refo Ilmiya Akbar Poonam Nidadavolu Oksana Havryliuk Sébastien Lafond Sepinoud Azimi |
| author_sort | Md Raisul Kibria |
| collection | DOAJ |
| description | Abstract Molecular Dynamic (MD) simulations are very effective in the discovery of nanomedicines for treating cancer, but these are computationally expensive and time-consuming. Existing studies integrating machine learning (ML) into MD simulation to enhance the process and enable efficient analysis cannot provide direct insights without the complete simulation. In this study, we present an ML-based approach for predicting the solvent accessible surface area (SASA) of a nanoparticle (NP), denoting its efficacy, from a fraction of the MD simulations data. The proposed framework uses a time series model for simulating the MD, resulting in an intermediate state, and a second model to calculate the SASA in that state. Empirically, the solution can predict the SASA value 260 timesteps ahead 7.5 times faster with a very low average error of 1956.93. We also introduce the use of an explainability technique to validate the predictions. This work can reduce the computational expense of both processing and data size greatly while providing reliable solutions for the nanomedicine design process. |
| first_indexed | 2024-04-10T22:48:49Z |
| format | Article |
| id | doaj.art-fdabd8d8a50f4c068b1ae9ae462053d5 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-10T22:48:49Z |
| publishDate | 2023-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-fdabd8d8a50f4c068b1ae9ae462053d52023-01-15T12:10:32ZengNature PortfolioScientific Reports2045-23222023-01-0113111310.1038/s41598-023-27729-7Predicting efficacy of drug-carrier nanoparticle designs for cancer treatment: a machine learning-based solutionMd Raisul Kibria0Refo Ilmiya Akbar1Poonam Nidadavolu2Oksana Havryliuk3Sébastien Lafond4Sepinoud Azimi5Faculty of Science and Engineering, Åbo Akademi UniversityFaculty of Science and Engineering, Åbo Akademi UniversityFaculty of Science and Engineering, Åbo Akademi UniversityFaculty of Science and Engineering, Åbo Akademi UniversityFaculty of Science and Engineering, Åbo Akademi UniversityFaculty of Science and Engineering, Åbo Akademi UniversityAbstract Molecular Dynamic (MD) simulations are very effective in the discovery of nanomedicines for treating cancer, but these are computationally expensive and time-consuming. Existing studies integrating machine learning (ML) into MD simulation to enhance the process and enable efficient analysis cannot provide direct insights without the complete simulation. In this study, we present an ML-based approach for predicting the solvent accessible surface area (SASA) of a nanoparticle (NP), denoting its efficacy, from a fraction of the MD simulations data. The proposed framework uses a time series model for simulating the MD, resulting in an intermediate state, and a second model to calculate the SASA in that state. Empirically, the solution can predict the SASA value 260 timesteps ahead 7.5 times faster with a very low average error of 1956.93. We also introduce the use of an explainability technique to validate the predictions. This work can reduce the computational expense of both processing and data size greatly while providing reliable solutions for the nanomedicine design process.https://doi.org/10.1038/s41598-023-27729-7 |
| spellingShingle | Md Raisul Kibria Refo Ilmiya Akbar Poonam Nidadavolu Oksana Havryliuk Sébastien Lafond Sepinoud Azimi Predicting efficacy of drug-carrier nanoparticle designs for cancer treatment: a machine learning-based solution Scientific Reports |
| title | Predicting efficacy of drug-carrier nanoparticle designs for cancer treatment: a machine learning-based solution |
| title_full | Predicting efficacy of drug-carrier nanoparticle designs for cancer treatment: a machine learning-based solution |
| title_fullStr | Predicting efficacy of drug-carrier nanoparticle designs for cancer treatment: a machine learning-based solution |
| title_full_unstemmed | Predicting efficacy of drug-carrier nanoparticle designs for cancer treatment: a machine learning-based solution |
| title_short | Predicting efficacy of drug-carrier nanoparticle designs for cancer treatment: a machine learning-based solution |
| title_sort | predicting efficacy of drug carrier nanoparticle designs for cancer treatment a machine learning based solution |
| url | https://doi.org/10.1038/s41598-023-27729-7 |
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