Computational approaches in drug chemistry leveraging python powered QSPR study of antimalaria compounds by using artificial neural networks

The application of Machine Learning has become a revolutionary instrument in the domain of pharmaceutical research. Machine learning enables the modelling of Quantitative Structure Property Relationship, a crucial task in forecasting the physiochemical characteristics of drugs. In this study we util...

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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 19307 - 18
Hauptverfasser:	Ahmed, Wakeel, Ashraf, Tamseela, Saleem, Maliha Tehseen, Mahmoud, Emad E., Ali, Kashif, Zaman, Shahid, Belay, Melaku Berhe
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 02.06.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	631/154 631/45 639/638 692/699 Algorithms Anti-malaria drugs Antimalarial agents Antimalarials - chemistry Antimalarials - pharmacology Antiparasitic agents Artificial Neural Networks Drug development Enzymes Humanities and Social Sciences Humans Learning algorithms Machine Learning Malaria multidisciplinary Neural networks Neural Networks, Computer Observational learning Pharmacokinetics Physicochemical properties Python Algorithm QSPR analysis Quantitative Structure-Activity Relationship Random Forest Regression analysis Science Science (multidisciplinary) Vector-borne diseases QSPR analysis Python Algorithm Artificial Neural Networks Machine Learning Anti-malaria drugs Random Forest
ISSN:	2045-2322, 2045-2322
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Abstract	The application of Machine Learning has become a revolutionary instrument in the domain of pharmaceutical research. Machine learning enables the modelling of Quantitative Structure Property Relationship, a crucial task in forecasting the physiochemical characteristics of drugs. In this study we utilized machine learning algorithms namely Artificial Neural Networks and Random Forest to predict physiochemical characteristics of Anti-malaria drugs. These models utilize several topological indices global variables quantifying the connectivity and geometric characteristics of molecules to estimate the ability of prospective antimalarial compounds to interact with the target enzyme and other physicochemical parameters. Molecular descriptors such as size, shape, and electronic structure indices are a way of mapping molecular properties into a set of quantitative data that can be analyzed by Machine Learning techniques. By carrying out regression analysis with the help of Artificial Neural Networks and Random Forest, the corresponding changes in the molecular structures and their effects on effectiveness and properties of the potential drugs can be predicted, thereby supporting the search for new therapeutic compounds. Machine learning not only observe the drug development process but also facilitates to look at chemical datasets with respect to high order non-linear relationship, which are essential to improve antimalarial drug candidates and pharmacokinetic properties.
AbstractList	The application of Machine Learning has become a revolutionary instrument in the domain of pharmaceutical research. Machine learning enables the modelling of Quantitative Structure Property Relationship, a crucial task in forecasting the physiochemical characteristics of drugs. In this study we utilized machine learning algorithms namely Artificial Neural Networks and Random Forest to predict physiochemical characteristics of Anti-malaria drugs. These models utilize several topological indices global variables quantifying the connectivity and geometric characteristics of molecules to estimate the ability of prospective antimalarial compounds to interact with the target enzyme and other physicochemical parameters. Molecular descriptors such as size, shape, and electronic structure indices are a way of mapping molecular properties into a set of quantitative data that can be analyzed by Machine Learning techniques. By carrying out regression analysis with the help of Artificial Neural Networks and Random Forest, the corresponding changes in the molecular structures and their effects on effectiveness and properties of the potential drugs can be predicted, thereby supporting the search for new therapeutic compounds. Machine learning not only observe the drug development process but also facilitates to look at chemical datasets with respect to high order non-linear relationship, which are essential to improve antimalarial drug candidates and pharmacokinetic properties. The application of Machine Learning has become a revolutionary instrument in the domain of pharmaceutical research. Machine learning enables the modelling of Quantitative Structure Property Relationship, a crucial task in forecasting the physiochemical characteristics of drugs. In this study we utilized machine learning algorithms namely Artificial Neural Networks and Random Forest to predict physiochemical characteristics of Anti-malaria drugs. These models utilize several topological indices global variables quantifying the connectivity and geometric characteristics of molecules to estimate the ability of prospective antimalarial compounds to interact with the target enzyme and other physicochemical parameters. Molecular descriptors such as size, shape, and electronic structure indices are a way of mapping molecular properties into a set of quantitative data that can be analyzed by Machine Learning techniques. By carrying out regression analysis with the help of Artificial Neural Networks and Random Forest, the corresponding changes in the molecular structures and their effects on effectiveness and properties of the potential drugs can be predicted, thereby supporting the search for new therapeutic compounds. Machine learning not only observe the drug development process but also facilitates to look at chemical datasets with respect to high order non-linear relationship, which are essential to improve antimalarial drug candidates and pharmacokinetic properties.The application of Machine Learning has become a revolutionary instrument in the domain of pharmaceutical research. Machine learning enables the modelling of Quantitative Structure Property Relationship, a crucial task in forecasting the physiochemical characteristics of drugs. In this study we utilized machine learning algorithms namely Artificial Neural Networks and Random Forest to predict physiochemical characteristics of Anti-malaria drugs. These models utilize several topological indices global variables quantifying the connectivity and geometric characteristics of molecules to estimate the ability of prospective antimalarial compounds to interact with the target enzyme and other physicochemical parameters. Molecular descriptors such as size, shape, and electronic structure indices are a way of mapping molecular properties into a set of quantitative data that can be analyzed by Machine Learning techniques. By carrying out regression analysis with the help of Artificial Neural Networks and Random Forest, the corresponding changes in the molecular structures and their effects on effectiveness and properties of the potential drugs can be predicted, thereby supporting the search for new therapeutic compounds. Machine learning not only observe the drug development process but also facilitates to look at chemical datasets with respect to high order non-linear relationship, which are essential to improve antimalarial drug candidates and pharmacokinetic properties. Abstract The application of Machine Learning has become a revolutionary instrument in the domain of pharmaceutical research. Machine learning enables the modelling of Quantitative Structure Property Relationship, a crucial task in forecasting the physiochemical characteristics of drugs. In this study we utilized machine learning algorithms namely Artificial Neural Networks and Random Forest to predict physiochemical characteristics of Anti-malaria drugs. These models utilize several topological indices global variables quantifying the connectivity and geometric characteristics of molecules to estimate the ability of prospective antimalarial compounds to interact with the target enzyme and other physicochemical parameters. Molecular descriptors such as size, shape, and electronic structure indices are a way of mapping molecular properties into a set of quantitative data that can be analyzed by Machine Learning techniques. By carrying out regression analysis with the help of Artificial Neural Networks and Random Forest, the corresponding changes in the molecular structures and their effects on effectiveness and properties of the potential drugs can be predicted, thereby supporting the search for new therapeutic compounds. Machine learning not only observe the drug development process but also facilitates to look at chemical datasets with respect to high order non-linear relationship, which are essential to improve antimalarial drug candidates and pharmacokinetic properties.
ArticleNumber	19307
Author	Ashraf, Tamseela Saleem, Maliha Tehseen Ahmed, Wakeel Mahmoud, Emad E. Ali, Kashif Belay, Melaku Berhe Zaman, Shahid
Author_xml	– sequence: 1 givenname: Wakeel surname: Ahmed fullname: Ahmed, Wakeel email: wakeelahmed784@gmail.com organization: Department of Mathematics, COMSATS University Islamabad, Lahore Campus, Department of Mathematics, University of Sialkot – sequence: 2 givenname: Tamseela surname: Ashraf fullname: Ashraf, Tamseela organization: Department of Mathematics, COMSATS University Islamabad, Lahore Campus – sequence: 3 givenname: Maliha Tehseen surname: Saleem fullname: Saleem, Maliha Tehseen organization: Department of Mathematics, University of Sialkot – sequence: 4 givenname: Emad E. surname: Mahmoud fullname: Mahmoud, Emad E. organization: Department of Mathematics and Statistics, Collage of Science, Taif University – sequence: 5 givenname: Kashif surname: Ali fullname: Ali, Kashif organization: Department of Mathematics, COMSATS University Islamabad, Lahore Campus – sequence: 6 givenname: Shahid surname: Zaman fullname: Zaman, Shahid email: zaman.ravian@gmail.com organization: Department of Mathematical and Physical Sciences, College of Arts and Sciences, University of Nizwa – sequence: 7 givenname: Melaku Berhe surname: Belay fullname: Belay, Melaku Berhe email: melaku.berhe@aastu.edu.et organization: Nanotechnology Center of Excellence, Addis Ababa Science and Technology University, Mathematics, Physics and Statistics Division, Addis Ababa Science and Technology University
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Keywords	QSPR analysis Python Algorithm Artificial Neural Networks Machine Learning Anti-malaria drugs Random Forest
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Snippet	The application of Machine Learning has become a revolutionary instrument in the domain of pharmaceutical research. Machine learning enables the modelling of... Abstract The application of Machine Learning has become a revolutionary instrument in the domain of pharmaceutical research. Machine learning enables the...
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SubjectTerms	631/154 631/45 639/638 692/699 Algorithms Anti-malaria drugs Antimalarial agents Antimalarials - chemistry Antimalarials - pharmacology Antiparasitic agents Artificial Neural Networks Drug development Enzymes Humanities and Social Sciences Humans Learning algorithms Machine Learning Malaria multidisciplinary Neural networks Neural Networks, Computer Observational learning Pharmacokinetics Physicochemical properties Python Algorithm QSPR analysis Quantitative Structure-Activity Relationship Random Forest Regression analysis Science Science (multidisciplinary) Vector-borne diseases
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Title	Computational approaches in drug chemistry leveraging python powered QSPR study of antimalaria compounds by using artificial neural networks
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Volume	15
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