Autor(es):
Borba, Joyce V. B. ; Salazar-Alvarez, Luis Carlos ; Ferreira, Letícia Tiburcio ; Silva-Mendonça, Sabrina ; Silva, Meryck Felipe Brito da ; Sanches, Igor H. ; Clementino, Leandro da Costa ; Magalhães, Marcela Lucas ; Rimoldi, Aline ; Calit, Juliana ; Santana, Sofia ; Prudêncio, Miguel ; Cravo, Pedro V. ; Bargieri, Daniel Y. ; Cassiano, Gustavo C. ; Costa, Fabio T. M. ; Andrade, Carolina Horta
Data: 2024
Identificador Persistente: http://hdl.handle.net/10362/172781
Origem: Repositório Institucional da UNL
Assunto(s): Antimalarial; Artificial Intelligence; Blood stage; Hits; Liver stage; Plasmodium; QSAR; Sexual stage; Biochemistry; Drug Discovery; Organic Chemistry; SDG 3 - Good Health and Well-being
Descrição
Funding Information: The Article Processing Charge for the publication of this research was funded by the Coordination for the Improvement of Higher Education Personnel - CAPES (ROR identifier: 00x0ma614). The authors would like to thank the funding agencies CNPq (441038/2020-4), CAPES-STINT (88881.304811/2018-01), FAPEG (202010267000272), FAPESP (Grants 2017/18611-7, 2021/06769-0, and 2021/06769-0), Instituto Serrapilheira (grant G-1709-16618), the Swedish Research Council (grants 2016-05627 and 2021-03667). J.V.B.B., L.T.F., L.C.S.A., M.L.M., J.C. received FAPESP fellowships (grants 2019/21854-4, 2019/02171-3, 2021/13809-9, 2023/07805-6, 2020/11060-8, 2018/24878-9). L.C.S.A. also received CNPq fellowship (Grants 162117/2018-3). M.P. acknowledges funding from the \u201Cla Caixa Foundation, grant HR21-848, and the European Union Horizon Europe programme (grant 101080744). CHA thanks the \u201CL\u2019Ore\u0301al-UNESCO-ABC Para Mulheres na Cie\u0301ncia\u201D and \u201CL\u2019Ore\u0301al-UNESCO International Rising Talents\u201D for the awards and fellowships received, which partially funded this work. C.H.A. and F.T.M.C. are CNPq research fellows. Publisher Copyright: © 2024 The Authors. Published by American Chemical Society.
Malaria presents a significant challenge to global public health, with around 247 million cases estimated to occur annually worldwide. The growing resistance of Plasmodium parasites to existing therapies underscores the urgent need for new and innovative antimalarial drugs. This study leveraged artificial intelligence (AI) to tackle this complex challenge. We developed multistage Machine Learning Quantitative Structure-Activity Relationship (ML-QSAR) models to effectively analyze large datasets and predict the efficacy of chemical compounds against multiple life cycle stages of Plasmodium parasites. We then selected 16 compounds for experimental evaluation, six of which showed at least dual-stage inhibitory activity and one inhibited all life cycle stages tested. Moreover, explainable AI (XAI) analysis provided insights into critical molecular features influencing model predictions, thereby enhancing our understanding of compound interactions. This study not only empowers the development of advanced predictive AI models but also accelerates the identification and optimization of potential antiplasmodial compounds.
