In Silico Approach for Early Antimalarial Drug Discovery: De Novo Design of Virtual Multi-Strain Antiplasmodial Inhibitors
<i>Plasmodium falciparum</i> is the causative agent of malaria, a parasitic disease that affects millions of people in terms of prevalence and is associated with hundreds of thousands of deaths. Current antimalarial medications, in addition to exhibiting moderate to serious adverse react...
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MDPI AG
2025-07-01
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| author | Valeria V. Kleandrova M. Natália D. S. Cordeiro Alejandro Speck-Planche |
| author_facet | Valeria V. Kleandrova M. Natália D. S. Cordeiro Alejandro Speck-Planche |
| author_sort | Valeria V. Kleandrova |
| collection | DOAJ |
| description | <i>Plasmodium falciparum</i> is the causative agent of malaria, a parasitic disease that affects millions of people in terms of prevalence and is associated with hundreds of thousands of deaths. Current antimalarial medications, in addition to exhibiting moderate to serious adverse reactions, are not efficacious enough due to factors such as drug resistance. In silico approaches can speed up the discovery and design of new molecules with wide-spectrum antimalarial activity. Here, we report a unified computational methodology combining a perturbation theory machine learning model based on multilayer perceptron networks (PTML-MLP) and the fragment-based topological design (FBTD) approach for the prediction and design of novel molecules virtually exhibiting versatile antiplasmodial activity against diverse <i>P. falciparum</i> strains. Our PTML-MLP achieved an accuracy higher than 85%. We applied the FBTD approach to physicochemically and structurally interpret the PTML-MLP, subsequently extracting several suitable molecular fragments and designing new drug-like molecules. These designed molecules were predicted as multi-strain antiplasmodial inhibitors, thus representing promising chemical entities for future synthesis and biological experimentation. The present work confirms the potential of combining PTML modeling and FBTD for early antimalarial drug discovery while opening new horizons for extended computational applications for antimicrobial research and beyond. |
| format | Article |
| id | doaj-art-e49ee4e67356459fb29a26bcb11e8bc0 |
| institution | Kabale University |
| issn | 2076-2607 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Microorganisms |
| spelling | doaj-art-e49ee4e67356459fb29a26bcb11e8bc02025-08-20T03:35:38ZengMDPI AGMicroorganisms2076-26072025-07-01137162010.3390/microorganisms13071620In Silico Approach for Early Antimalarial Drug Discovery: De Novo Design of Virtual Multi-Strain Antiplasmodial InhibitorsValeria V. Kleandrova0M. Natália D. S. Cordeiro1Alejandro Speck-Planche2LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, PortugalLAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, PortugalLAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal<i>Plasmodium falciparum</i> is the causative agent of malaria, a parasitic disease that affects millions of people in terms of prevalence and is associated with hundreds of thousands of deaths. Current antimalarial medications, in addition to exhibiting moderate to serious adverse reactions, are not efficacious enough due to factors such as drug resistance. In silico approaches can speed up the discovery and design of new molecules with wide-spectrum antimalarial activity. Here, we report a unified computational methodology combining a perturbation theory machine learning model based on multilayer perceptron networks (PTML-MLP) and the fragment-based topological design (FBTD) approach for the prediction and design of novel molecules virtually exhibiting versatile antiplasmodial activity against diverse <i>P. falciparum</i> strains. Our PTML-MLP achieved an accuracy higher than 85%. We applied the FBTD approach to physicochemically and structurally interpret the PTML-MLP, subsequently extracting several suitable molecular fragments and designing new drug-like molecules. These designed molecules were predicted as multi-strain antiplasmodial inhibitors, thus representing promising chemical entities for future synthesis and biological experimentation. The present work confirms the potential of combining PTML modeling and FBTD for early antimalarial drug discovery while opening new horizons for extended computational applications for antimicrobial research and beyond.https://www.mdpi.com/2076-2607/13/7/1620PTMLtopological indicesmultilayer perceptronfragmentfragment-based topological designmalaria |
| spellingShingle | Valeria V. Kleandrova M. Natália D. S. Cordeiro Alejandro Speck-Planche In Silico Approach for Early Antimalarial Drug Discovery: De Novo Design of Virtual Multi-Strain Antiplasmodial Inhibitors Microorganisms PTML topological indices multilayer perceptron fragment fragment-based topological design malaria |
| title | In Silico Approach for Early Antimalarial Drug Discovery: De Novo Design of Virtual Multi-Strain Antiplasmodial Inhibitors |
| title_full | In Silico Approach for Early Antimalarial Drug Discovery: De Novo Design of Virtual Multi-Strain Antiplasmodial Inhibitors |
| title_fullStr | In Silico Approach for Early Antimalarial Drug Discovery: De Novo Design of Virtual Multi-Strain Antiplasmodial Inhibitors |
| title_full_unstemmed | In Silico Approach for Early Antimalarial Drug Discovery: De Novo Design of Virtual Multi-Strain Antiplasmodial Inhibitors |
| title_short | In Silico Approach for Early Antimalarial Drug Discovery: De Novo Design of Virtual Multi-Strain Antiplasmodial Inhibitors |
| title_sort | in silico approach for early antimalarial drug discovery de novo design of virtual multi strain antiplasmodial inhibitors |
| topic | PTML topological indices multilayer perceptron fragment fragment-based topological design malaria |
| url | https://www.mdpi.com/2076-2607/13/7/1620 |
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