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...

Full description

Saved in:

Bibliographic Details
Published in:	Scientific reports Vol. 15; no. 1; pp. 19307 - 18
Main Authors:	Ahmed, Wakeel, Ashraf, Tamseela, Saleem, Maliha Tehseen, Mahmoud, Emad E., Ali, Kashif, Zaman, Shahid, Belay, Melaku Berhe
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 02.06.2025 Nature Publishing Group Nature Portfolio
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

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. 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. 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
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/40456832$$D View this record in MEDLINE/PubMed
BookMark	eNp9UsluFDEQbaEgspAf4IAsceHS4HXaPiE0ghApEvvZ8njp8dBjN7Y7Uf8DH41nJoSEA75U2fXeq2e7TpujEINtmmcIvkKQ8NeZIiZ4CzFrYc1oOz9qTjCkrMUE46N7-XFznvMG1sWwoEg8aY5prS04wSfNr2XcjlNRxcegBqDGMUWl1zYDH4BJUw_qZutzSTMY7LVNqvehB-Nc1jGAMd7YZA34_PXTF5DLZGYQHVCh-K0aVPIK6Cofp2AyWM1gyjuuSsU7r31tF-yU9qHcxPQjP20eOzVke34bz5rv7999W35orz5eXC7fXrWaClpaBzuKhUIEmW7FCaKYo84QxjB3xHKBXQetEoYgAalaMcF0x4ix2DqFHHHkrLk86JqoNnJM1W2aZVRe7g9i6uXOpB6sVMIKyqBGHaLUCrjSC4S0Y5DRhTOOVK03B61xWm2t0TaUeqUHog8rwa9lH68lwohAynFVeHmrkOLPyeYi63trOwwq2DhlSTBimHQQ8gp98Q90E6dUP26PopxxCneWnt-3dOflz69XAD4AdIo5J-vuIAjK3XTJw3TJOl1yP11yriRyIOUKDr1Nf3v_h_UbdXjUrA
Cites_doi	10.1111/pai.13835 10.1142/S0217984924502609 10.1007/978-3-662-70107-2_7 10.1021/ci00016a005 10.1007/s10910-022-01403-1 10.1142/S2424913024500115 10.3389/fphar.2024.1446774 10.1021/ja00856a001 10.3934/math.2021658 10.1080/00268976.2023.2212533 10.3390/math11102245 10.1002/qua.26778 10.1007/s10910-015-0480-z 10.1016/j.arabjc.2022.104160 10.1016/j.heliyon.2020.e04235 10.1080/10406638.2021.1993941 10.1140/epje/s10189-024-00446-3 10.33263/BRIAC123.41924199 10.1038/s41598-025-88044-x 10.22457/apam.v16n1a6 10.1080/03602532.2024.2405163 10.1093/bib/bbab159 10.1016/j.aej.2022.11.001 10.1002/qua.27391 10.33263/BRIAC131.071 10.1186/s13065-024-01266-4 10.1016/j.arr.2025.102665 10.1021/acsnano.4c05693 10.21275/ART20203995 10.1109/TQCEBT59414.2024.10545171 10.1038/s41598-024-62819-0 10.1080/10406638.2023.2217990 10.1080/10406638.2023.2196429 10.3390/math6100214
ContentType	Journal Article
Copyright	The Author(s) 2025 2025. The Author(s). Copyright Nature Publishing Group 2025 The Author(s) 2025 2025
Copyright_xml	– notice: The Author(s) 2025 – notice: 2025. The Author(s). – notice: Copyright Nature Publishing Group 2025 – notice: The Author(s) 2025 2025
DBID	C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88A 88E 88I 8FE 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BHPHI CCPQU COVID DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA
DOI	10.1038/s41598-025-01594-y
DatabaseName	Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest : Biological Science Collection journals [unlimited simultaneous users] ProQuest Central Natural Science Collection ProQuest One Community College Coronavirus Research Database ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Biological Science Collection Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Biology Journals (Alumni Edition) ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition Coronavirus Research Database ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic Publicly Available Content Database MEDLINE
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Biology
EISSN	2045-2322
EndPage	18
ExternalDocumentID	oai_doaj_org_article_a9e9450c17144e90bc611cf50546fdf3 PMC12130482 40456832 10_1038_s41598_025_01594_y
Genre	Journal Article
GroupedDBID	0R~ 4.4 53G 5VS 7X7 88E 88I 8FE 8FH 8FI 8FJ AAFWJ AAJSJ AAKDD AASML ABDBF ABUWG ACGFS ACUHS ADBBV ADRAZ AENEX AEUYN AFKRA AFPKN ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK DWQXO EBD EBLON EBS ESX FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE KQ8 LK8 M1P M2P M7P M~E NAO OK1 PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO RNT RNTTT RPM SNYQT UKHRP AAYXX AFFHD CITATION CGR CUY CVF ECM EIF NPM 3V. 7XB 88A 8FK COVID K9. M48 PKEHL PQEST PQUKI PRINS Q9U 7X8 PUEGO 5PM
ID	FETCH-LOGICAL-c494t-f07429a131d7b83142817d35528f3e892f70ea9d31904ab595c753de2efa1f3f3
IEDL.DBID	DOA
ISICitedReferencesCount	2
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001501594400033&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2045-2322
IngestDate	Fri Oct 03 12:52:43 EDT 2025 Tue Nov 04 02:01:50 EST 2025 Fri Sep 05 15:56:03 EDT 2025 Tue Oct 07 07:35:24 EDT 2025 Mon Jul 21 06:01:07 EDT 2025 Thu Nov 20 01:46:50 EST 2025 Mon Jul 21 06:07:29 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	1
Keywords	QSPR analysis Python Algorithm Artificial Neural Networks Machine Learning Anti-malaria drugs Random Forest
Language	English
License	2025. The Author(s). Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c494t-f07429a131d7b83142817d35528f3e892f70ea9d31904ab595c753de2efa1f3f3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
OpenAccessLink	https://doaj.org/article/a9e9450c17144e90bc611cf50546fdf3
PMID	40456832
PQID	3214858403
PQPubID	2041939
PageCount	18
ParticipantIDs	doaj_primary_oai_doaj_org_article_a9e9450c17144e90bc611cf50546fdf3 pubmedcentral_primary_oai_pubmedcentral_nih_gov_12130482 proquest_miscellaneous_3215237008 proquest_journals_3214858403 pubmed_primary_40456832 crossref_primary_10_1038_s41598_025_01594_y springer_journals_10_1038_s41598_025_01594_y
PublicationCentury	2000
PublicationDate	2025-06-02
PublicationDateYYYYMMDD	2025-06-02
PublicationDate_xml	– month: 06 year: 2025 text: 2025-06-02 day: 02
PublicationDecade	2020
PublicationPlace	London
PublicationPlace_xml	– name: London – name: England
PublicationTitle	Scientific reports
PublicationTitleAbbrev	Sci Rep
PublicationTitleAlternate	Sci Rep
PublicationYear	2025
Publisher	Nature Publishing Group UK Nature Publishing Group Nature Portfolio
Publisher_xml	– name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio
References	W Ahmed (1594_CR3) 2024; 18 S Zaman (1594_CR21) 2024; 44 A Ahmad (1594_CR35) 2023; 121 GK Jayanna (1594_CR33) 2021; 12 1594_CR14 AR Katritzky (1594_CR23) 1993; 33 S Hayat (1594_CR17) 2023; 11 GK Jayanna (1594_CR29) 2023; 23 QY Zhang (1594_CR9) 2024; 18 W Gao (1594_CR30) 2018; 6 W Ahmed (1594_CR13) 2024; 09 L Wang (1594_CR11) 2024; 15 N Awan (1594_CR48) 2024; 124 I Gutman (1594_CR37) 2012 W Ahmed (1594_CR12) 2024; 14 W Zhao (1594_CR40) 2021; 2021 A Ali (1594_CR44) 2022; 60 VR Kulli (1594_CR24) 2018; 16 V Ravi (1594_CR36) 2022; 42 S Vujošević (1594_CR45) 2021; 391 PS Ranjini (1594_CR47) 2013; 1 E Estrada (1594_CR41) 1998; 37A S Hayat (1594_CR16) 2014; 59 W Gao (1594_CR42) 2016; 48 J Pei (1594_CR8) 2025; 104 M Naeem (1594_CR28) 2024; 44 CT Martınez-Martınez (1594_CR38) 2021; 85 M Randic (1594_CR43) 1975; 97 JB Liu (1594_CR25) 2021; 121 Z Wu (1594_CR6) 2024; 56 W Ahmed (1594_CR18) 2024; 38 W Ahmed (1594_CR20) 2024; 13 MC Shanmukha (1594_CR22) 2020; 6 AN Koam (1594_CR32) 2021; 6 M Arockiaraj (1594_CR19) 2025; 15 A Khan (1594_CR4) 2023; 66 P Poojary (1594_CR26) 2022; 13 I Gutman (1594_CR46) 2017; 42 1594_CR2 1594_CR1 B Mahesh (1594_CR7) 2020; 9 AN Koam (1594_CR31) 2021; 2021 AN Koam (1594_CR34) 2022; 15 B Furtula (1594_CR39) 2015; 53 Y Zhou (1594_CR10) 2022; 33 W Ahmed (1594_CR15) 2024; 09 T Gaudelet (1594_CR5) 2021; 22 M Arockiaraj (1594_CR27) 2024; 47
References_xml	– volume: 33 issue: 8 year: 2022 ident: 1594_CR10 publication-title: Pediatr. Allergy Immunol. doi: 10.1111/pai.13835 – volume: 38 start-page: 2450260 issue: 27 year: 2024 ident: 1594_CR18 publication-title: Mod. Phys. Lett. B doi: 10.1142/S0217984924502609 – ident: 1594_CR2 doi: 10.1007/978-3-662-70107-2_7 – volume: 33 start-page: 835 issue: 6 year: 1993 ident: 1594_CR23 publication-title: J. Chem. Inf. Comput. Sci. doi: 10.1021/ci00016a005 – volume: 60 start-page: 2081 issue: 10 year: 2022 ident: 1594_CR44 publication-title: J. Math. Chem. doi: 10.1007/s10910-022-01403-1 – volume: 09 start-page: 77 issue: 02n03 year: 2024 ident: 1594_CR13 publication-title: J. Micromech. Mol. Phys. doi: 10.1142/S2424913024500115 – volume: 42 start-page: 1 year: 2017 ident: 1594_CR46 publication-title: Bulletin (Académie serbe des sciences et des arts. Classe des sciences mathématiques et naturelles. Sciences mathématiques). – volume: 15 start-page: 1446774 year: 2024 ident: 1594_CR11 publication-title: Front. Pharmacol. doi: 10.3389/fphar.2024.1446774 – ident: 1594_CR14 – volume-title: Mathematical concepts in organic chemistry year: 2012 ident: 1594_CR37 – volume: 97 start-page: 6609 issue: 23 year: 1975 ident: 1594_CR43 publication-title: J. Am. Chem. Soc. doi: 10.1021/ja00856a001 – volume: 6 start-page: 11330 issue: 10 year: 2021 ident: 1594_CR32 publication-title: AIMS Math. doi: 10.3934/math.2021658 – volume: 121 issue: 14 year: 2023 ident: 1594_CR35 publication-title: Mol. Phys. doi: 10.1080/00268976.2023.2212533 – volume: 11 start-page: 2245 issue: 10 year: 2023 ident: 1594_CR17 publication-title: Mathematics doi: 10.3390/math11102245 – volume: 121 issue: 22 year: 2021 ident: 1594_CR25 publication-title: Int. J. Quantum Chem. doi: 10.1002/qua.26778 – volume: 53 start-page: 1184 issue: 4 year: 2015 ident: 1594_CR39 publication-title: J. Math. Chem. doi: 10.1007/s10910-015-0480-z – volume: 1 start-page: 116 issue: 4 year: 2013 ident: 1594_CR47 publication-title: Int. J. Graph Theory – volume: 15 issue: 10 year: 2022 ident: 1594_CR34 publication-title: Arab. J. Chem. doi: 10.1016/j.arabjc.2022.104160 – volume: 09 start-page: 77 issue: 02n03 year: 2024 ident: 1594_CR15 publication-title: J. Micromech. Mol. Phys. doi: 10.1142/S2424913024500115 – volume: 6 start-page: e04235 issue: 6 year: 2020 ident: 1594_CR22 publication-title: Heliyon doi: 10.1016/j.heliyon.2020.e04235 – volume: 42 start-page: 6932 issue: 10 year: 2022 ident: 1594_CR36 publication-title: Polycyclic Aromat. Compd. doi: 10.1080/10406638.2021.1993941 – volume: 47 start-page: 53 issue: 8 year: 2024 ident: 1594_CR27 publication-title: Eur. Phys. J. E doi: 10.1140/epje/s10189-024-00446-3 – volume: 12 start-page: 4192 issue: 3 year: 2021 ident: 1594_CR33 publication-title: Biointerface Res. Appl. Chem. doi: 10.33263/BRIAC123.41924199 – volume: 2021 start-page: 1 year: 2021 ident: 1594_CR40 publication-title: J. Math. – volume: 2021 start-page: 4540276 issue: 1 year: 2021 ident: 1594_CR31 publication-title: J. Math. – volume: 59 start-page: 113 issue: 4 year: 2014 ident: 1594_CR16 publication-title: Stud. Univ. Babes-Bolyai, Chem. – volume: 15 start-page: 3639 issue: 1 year: 2025 ident: 1594_CR19 publication-title: Sci. Rep. doi: 10.1038/s41598-025-88044-x – volume: 16 start-page: 47 issue: 1 year: 2018 ident: 1594_CR24 publication-title: Ann. Pure Appl. Math. doi: 10.22457/apam.v16n1a6 – volume: 56 start-page: 349 issue: 4 year: 2024 ident: 1594_CR6 publication-title: Drug Metab. Rev. doi: 10.1080/03602532.2024.2405163 – volume: 85 start-page: 395 year: 2021 ident: 1594_CR38 publication-title: MATCH Commun. Math. Comput. Chem – volume: 37A start-page: 849 year: 1998 ident: 1594_CR41 publication-title: Indian J. Chem. – volume: 22 start-page: bbab159 issue: 6 year: 2021 ident: 1594_CR5 publication-title: Brief. Bioinform. doi: 10.1093/bib/bbab159 – volume: 66 start-page: 957 year: 2023 ident: 1594_CR4 publication-title: Alex. Eng. J. doi: 10.1016/j.aej.2022.11.001 – volume: 124 issue: 11 year: 2024 ident: 1594_CR48 publication-title: Int. J. Quantum Chem. doi: 10.1002/qua.27391 – volume: 13 start-page: 71 issue: 1 year: 2022 ident: 1594_CR26 publication-title: Biointerface Res. Appl. Chem doi: 10.33263/BRIAC131.071 – volume: 18 start-page: 1 issue: 1 year: 2024 ident: 1594_CR3 publication-title: BMC Chem. doi: 10.1186/s13065-024-01266-4 – volume: 104 start-page: 102665 year: 2025 ident: 1594_CR8 publication-title: Ageing Res. Rev. doi: 10.1016/j.arr.2025.102665 – volume: 18 start-page: 33032 issue: 48 year: 2024 ident: 1594_CR9 publication-title: ACS Nano doi: 10.1021/acsnano.4c05693 – volume: 9 start-page: 381 issue: 1 year: 2020 ident: 1594_CR7 publication-title: Int. J. Sci. Res. (IJSR) doi: 10.21275/ART20203995 – volume: 23 start-page: 175 issue: 1 year: 2023 ident: 1594_CR29 publication-title: Appl. Math. E-Notes – volume: 391 year: 2021 ident: 1594_CR45 publication-title: Appl. Math. Comput. – ident: 1594_CR1 doi: 10.1109/TQCEBT59414.2024.10545171 – volume: 13 start-page: 1 year: 2024 ident: 1594_CR20 publication-title: J. Mol. Eng. Mater. – volume: 14 start-page: 12264 issue: 1 year: 2024 ident: 1594_CR12 publication-title: Sci. Rep. doi: 10.1038/s41598-024-62819-0 – volume: 44 start-page: 2458 issue: 4 year: 2024 ident: 1594_CR21 publication-title: Polycyclic Aromat. Compd. doi: 10.1080/10406638.2023.2217990 – volume: 44 start-page: 1452 issue: 3 year: 2024 ident: 1594_CR28 publication-title: Polycyclic Aromat. Compd. doi: 10.1080/10406638.2023.2196429 – volume: 48 start-page: 543 issue: 3 year: 2016 ident: 1594_CR42 publication-title: Bul. Chem. Commun. – volume: 6 start-page: 214 issue: 10 year: 2018 ident: 1594_CR30 publication-title: Mathematics doi: 10.3390/math6100214
SSID	ssj0000529419
Score	2.4623003
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...
SourceID	doaj pubmedcentral proquest pubmed crossref springer
SourceType	Open Website Open Access Repository Aggregation Database Index Database Publisher
StartPage	19307
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
SummonAdditionalLinks	– databaseName: Science Database dbid: M2P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Jj9MwFLZgAInLsA4TGJCRuEE0TmI39gkBYsQBRmXV3CzHS4kESWlapPwHfjTvOUlHZbtwipqkld232u_5-wh5BJ7PVVKYVFYOj-RYkcJHnnJhmPOemTKerfr0ujw9lWdnaj5uuHVjW-XkE6Ojdq3FPfJjJNSREC1Z8XT5LUXWKKyujhQaF8klyGwybOl6k8-3eyxYxeKZGs_KsEIedxCv8ExZjh1rQvG034lHEbb_T7nm7y2Tv9RNYzg6ufa_E7lO9sdElD4bNOcGueCbm-TKQE3Z3yI_BrqHcauQTtDjvqN1Q91qs6B2ooqjXzzYQ2Q7ossesQjoErnXvKNv38_f0YhgS9tAQYj1VwNL6dpQbGVHRqeOVj3F5vsFRSUe8CwoomzGS-xR726TjycvP7x4lY7MDanliq_TgCtuZbIic2UlC0R1y0oHqU0uQ-GlykPJvFEO7J9xUwklLCybnM99MFkoQnFA9pq28YeE5tZAEDVMGPjlTAQTXOlyWc04A6WSMiGPJ_np5QDQoWNhvZB6kLYGaesobd0n5DmKePsmgmvHG-1qoUdb1UZ5xQWzyA3PvWKVnWWZDZAr8llwoUjI0SRZPVp8p8_FmpCH28cgByzAmMa3m_gOrPtLSLsScmfQp-1IOObW4F4TInc0bWeou0-a-nPEA0dUPnDE8NUnk1Kej-vv_8Xdf0_jHrmaRzvBJvUjsrdebfx9ctl-X9fd6kE0tJ9czjNv priority: 102 providerName: ProQuest
Title	Computational approaches in drug chemistry leveraging python powered QSPR study of antimalaria compounds by using artificial neural networks
URI	https://link.springer.com/article/10.1038/s41598-025-01594-y https://www.ncbi.nlm.nih.gov/pubmed/40456832 https://www.proquest.com/docview/3214858403 https://www.proquest.com/docview/3215237008 https://pubmed.ncbi.nlm.nih.gov/PMC12130482 https://doaj.org/article/a9e9450c17144e90bc611cf50546fdf3
Volume	15
WOSCitedRecordID	wos001501594400033&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M7P dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: 7X7 dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M2P dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lj9MwELZgFyQuiOcSWCojcYNoncSp7SOLdgUSW4XloXKKnNheIkFaNS1S_gM_mhk7KVse4sLFVeu0Gs2MPTP1-PsIeQo7n6lkrmNZGbySU-cxvOUxzzUz1jIt_N2qj2_EbCbnc1VcovrCnrAADxwUd6SVVTxnNRJ1c6tYVU-TpHYQuPnUGedxPplQl4qpgOqdKp6o4ZYMy-RRB5EKb5Ol2KuWKx73O5HIA_b_Kcv8vVnylxNTH4hOb5GbQwZJXwTJb5Mrtr1DrgdOyf4u-R54Gob_-OiIGW472rTUrDYXtB453ugXC47saYroskcQAbpE0jRr6Nt3xTn10LN04Shov_mqoQZuNMUedKRi6mjVU-yav6CoxABEQREe07_45vLuHvlwevL-5at4oFyIa674OnZYKiudZIkRlcwQji0RBnKSVLrMSpU6waxWBhYu47rKVV5DvWNsap1OXOay-2SvXbT2AaFprSH6aZZr-OUkd9oZYVJZTTkDb5AyIs9G9ZfLgKxR-hPxTJbBWCUYq_TGKvuIHKOFtk8iKrb_AHylHHyl_JevRORwtG85LNWuRKYmkIczmH6ynQY74MmJbu1i45-Bgl1AvhSRg-AOW0k4JsWwL0ZE7jjKjqi7M23z2QN5I5we7KDw1eejT_2U6--6ePg_dPGI3Ej9YsAe9EOyt15t7GNyrf62brrVhFwVc-FHOSH7xyez4nziVxiMZ2mBo4Bxv3h9Vnz6AewFLCQ
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lj9MwELaWBQQX3o_AAkaCE0SbOE5jHxDitdrVlqrAgvZmnNgulZakNC0o_4Hfwm9kxkm6Kq_bHjhVaR5ynG88Y3vm-wh5ACOfyUWqQ5EbLMkp0hAOechTHRlrI5352qoPw2w0EoeHcrxBfvS1MJhW2Y-JfqA2VYFr5NsoqCPAW0bJ09mXEFWjcHe1l9BoYbFvm28wZauf7L2E7_uQsZ1XBy92w05VICy45IvQ4WxQ6jiJTZaLBBnH4syA22XCJVZI5rLIamkAmxHXeSrTAkJ6Y5l1OnaJS-C5p8hpjsximCrIxqs1Hdw147HsanOiRGzX4B-xho1hhlwqedis-T8vE_Cn2Pb3FM1f9mm9-9u5-L913CVyoQu06bPWMi6TDVteIWdb6c3mKvneyll0S6G0p1a3NZ2W1MyXE1r0Unj0yIK9ezUnOmuQa4HOUFvOGvrm3fgt9Qy9tHIUQDr9rI802DTFVH1UrKpp3lAsLphQNNKWr4Mii6j_8Tn49TXy_kS64jrZLKvS3iSUFRqCBB2lGp4cp047kxkm8gGPwGiECMijHi9q1hKQKJ84kAjVoksBupRHl2oC8hwhtboSycP9H9V8orqxSGlpJU-jIs5gNm1llBeDOC4cxMJ84IxLArLVI0l1I1qtjmEUkPur0_AdcINJl7Za-mtSlmQQVgbkRovfVUs4zh3AfQRErCF7ranrZ8rpJ893jqyD4Gjg1se9ERy36-99cevfr3GPnNs9eD1Uw73R_m1ynnkbxYT8LbK5mC_tHXKm-LqY1vO73sgp-XjSxvETI7KOgA
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3JjtNAEC0NGUBc2BfDAI0EJ7DipR23DwgBQ0Q0QxSWQcPJtN3dIdJghzgB-R_4Ir6OqradUdhuc-AUJXastv1q6e6q9wDuo-dTmYikKzJFLTl55OJX7vJIekprT8a2t-r9fjwei8PDZLIFP7peGCqr7HyiddSqzGmNvE-COgKjpRf2TVsWMdkdPpl_cUlBinZaOzmNBiJ7uv6G07fq8WgX3_WDIBi-ePf8pdsqDLg5T_jSNTQzTKQf-irOREjsY36sMAQHwoRaJIGJPS0ThTj1uMyiJMoxvVc60Eb6JjQhXvcUbGNKzoMebE9GryYf1is8tIfG_aTt1PFC0a8wWlJHW0D1clHC3XojGlrRgD9lur8XbP6ya2uD4fDC__wYL8L5NgVnTxubuQRburgMZxpRzvoKfG-ELtpFUtaRruuKzQqmFqspyzuRPHak0RNYnSc2r4mFgc1JdU4r9vrt5A2z3L2sNAzhO_ssjyRaO6MiftKyqlhWM2o7mDIy34bJgxG_qP2w1fnVVTg4kUdxDXpFWegbwIJcYvogvUjilf3ISKNiFYhswD00JyEceNhhJ5031CSpLSkIRdogLUWkpRZpae3AM4LX-kyiFbc_lItp2nqpVCY64ZGX-zHOs3XiZfnA93ODWTIfGGVCB3Y6VKWtr6vSY0g5cG99GN8DbT3JQpcre04UhDEmnA5cb7C8HgmnWQUGFgfEBso3hrp5pJh9skzoxEeIIQj_-qgziONx_f1Z3Pz3bdyFs2gT6f5ovHcLzgXWXKlSfwd6y8VK34bT-dflrFrcaS2ewceTto6fa16YyQ
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Computational+approaches+in+drug+chemistry+leveraging+python+powered+QSPR+study+of+antimalaria+compounds+by+using+artificial+neural+networks&rft.jtitle=Scientific+reports&rft.au=Ahmed%2C+Wakeel&rft.au=Ashraf%2C+Tamseela&rft.au=Saleem%2C+Maliha+Tehseen&rft.au=Mahmoud%2C+Emad+E&rft.date=2025-06-02&rft.issn=2045-2322&rft.eissn=2045-2322&rft.volume=15&rft.issue=1&rft.spage=19307&rft_id=info:doi/10.1038%2Fs41598-025-01594-y&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-2322&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-2322&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-2322&client=summon