Drug Combination Therapy for Malaria using Deep Learning

Siva Prasad Pinnamaneni, G. K. tej, D. Kartheek, B. Dayamani, Ch. Geya, A. Surendra
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Abstract

Drug Combination has been effective for treating complex disorders like cancer and infectious diseases. Malaria remains a major global health challenge, with millions of cases and hundreds of thousands of deaths reported annually. While several drugs are available for malaria treatment, drug resistance has emerged as a significant problem. Combination therapy is now recommended as the first-line treatment for malaria. Due to the impossibility and high cost of considering every possible drug combination, it is necessary to put a lot of work into screening new drug combinations. Recently, deep learning techniques have shown promising results in discovering synergistic combinations. We present synergistic drug combinations for malaria and compare and analyze various models that predict effective drug combinations using deep learning techniques
基于深度学习的疟疾药物联合治疗
药物联合治疗对于治疗癌症和传染病等复杂疾病是有效的。疟疾仍然是一项重大的全球健康挑战,每年报告有数百万病例和数十万人死亡。虽然有几种药物可用于治疗疟疾,但耐药性已成为一个重大问题。现在建议将联合疗法作为疟疾的一线治疗方法。考虑到每种可能的药物组合是不可能的,而且成本很高,因此有必要投入大量的工作来筛选新的药物组合。最近,深度学习技术在发现协同组合方面显示出有希望的结果。我们提出了针对疟疾的协同药物组合,并比较和分析了使用深度学习技术预测有效药物组合的各种模型
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