Deep Learning-driven research for drug discovery: Tackling Malaria
Deep Learning-driven research for drug discovery: Tackling Malaria
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DOI：
10.1371/journal.pcbi.1007025
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发表时间：
2020-02-01
影响因子：
4.3
通讯作者：
Andrade, Carolina Horta
中科院分区：
文献类型：
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作者：
Neves, Bruno J.;Braga, Rodolpho C.;Andrade, Carolina Horta
疟疾是一种传染病，影响全世界超过2.16亿人，每年造成超过44.5万患者死亡。由于寄生虫对当前抗疟药物的耐药性不断出现，发现新的候选药物是全球卫生的一个主要优先事项。为了使药物发现过程更快，更便宜，我们开发了二进制和连续的定量结构活性关系（QSAR）模型，实现深度学习，用于预测未经测试的化合物的抗疟原虫活性和细胞毒性。然后，我们将最佳模型应用于大型化合物数据库的虚拟筛选。对敏感和多药耐药恶性疟原虫菌株的无性血液阶段的最高计算预测进行了实验评估。其中，两种化合物LabMol-149和LabMol-152在低纳摩尔浓度（EC 50 < 500 nM）下显示出有效的抗疟原虫活性，并且在哺乳动物细胞中显示出低细胞毒性。因此，本文开发的采用深度学习的计算方法使我们能够发现两个新的潜在下一代抗疟药物家族，它们符合抗疟靶点候选者的指南和标准。
Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC50 < 500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates.