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1.
ACS Med Chem Lett ; 15(8): 1386-1395, 2024 Aug 08.
Artigo em Inglês | MEDLINE | ID: mdl-39140064

RESUMO

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.

2.
Chem Res Toxicol ; 37(6): 910-922, 2024 Jun 17.
Artigo em Inglês | MEDLINE | ID: mdl-38781421

RESUMO

The human Ether-à-go-go-Related Gene (hERG) is a transmembrane protein that regulates cardiac action potential, and its inhibition can induce a potentially deadly cardiac syndrome. In vitro tests help identify hERG blockers at early stages; however, the high cost motivates searching for alternative, cost-effective methods. The primary goal of this study was to enhance the Pred-hERG tool for predicting hERG blockage. To achieve this, we developed new QSAR models that incorporated additional data, updated existing classificatory and multiclassificatory models, and introduced new regression models. Notably, we integrated SHAP (SHapley Additive exPlanations) values to offer a visual interpretation of these models. Utilizing the latest data from ChEMBL v30, encompassing over 14,364 compounds with hERG data, our binary and multiclassification models outperformed both the previous iteration of Pred-hERG and all publicly available models. Notably, the new version of our tool introduces a regression model for predicting hERG activity (pIC50). The optimal model demonstrated an R2 of 0.61 and an RMSE of 0.48, surpassing the only available regression model in the literature. Pred-hERG 5.0 now offers users a swift, reliable, and user-friendly platform for the early assessment of chemically induced cardiotoxicity through hERG blockage. The tool provides versatile outcomes, including (i) classificatory predictions of hERG blockage with prediction reliability, (ii) multiclassificatory predictions of hERG blockage with reliability, (iii) regression predictions with estimated pIC50 values, and (iv) probability maps illustrating the contribution of chemical fragments for each prediction. Furthermore, we implemented explainable AI analysis (XAI) to visualize SHAP values, providing insights into the contribution of each feature to binary classification predictions. A consensus prediction calculated based on the predictions of the three developed models is also present to assist the user's decision-making process. Pred-hERG 5.0 has been designed to be user-friendly, making it accessible to users without computational or programming expertise. The tool is freely available at http://predherg.labmol.com.br.


Assuntos
Canais de Potássio Éter-A-Go-Go , Relação Quantitativa Estrutura-Atividade , Humanos , Canais de Potássio Éter-A-Go-Go/antagonistas & inibidores , Canais de Potássio Éter-A-Go-Go/metabolismo , Medição de Risco , Análise de Regressão , Bloqueadores dos Canais de Potássio/farmacologia , Bloqueadores dos Canais de Potássio/química
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