化学信息学
深度学习
人工智能
计算机科学
药物发现
机器学习
数量结构-活动关系
药物开发
药物重新定位
卷积神经网络
过程(计算)
人工神经网络
制药工业
数据科学
药品
化学
医学
药理学
操作系统
生物化学
计算化学
作者
Ali Raza,Talha Ali Chohan,Manal Buabeid,El-Shaima A Arafa,Tahir Ali Chohan,Batool Fatima,Kishwar Sultana,Malik Saad Ullah,Ghulam Murtaza
标识
DOI:10.1080/07391102.2022.2136244
摘要
Artificial intelligence (AI) development imitates the workings of the human brain to comprehend modern problems. The traditional approaches such as high throughput screening (HTS) and combinatorial chemistry are lengthy and expensive to the pharmaceutical industry as they can only handle a smaller dataset. Deep learning (DL) is a sophisticated AI method that uses a thorough comprehension of particular systems. The pharmaceutical industry is now adopting DL techniques to enhance the research and development process. Multi-oriented algorithms play a crucial role in the processing of QSAR analysis, de novo drug design, ADME evaluation, physicochemical analysis, preclinical development, followed by clinical trial data precision. In this study, we investigated the performance of several algorithms, including deep neural networks (DNN), convolutional neural networks (CNN) and multi-task learning (MTL), with the aim of generating high-quality, interpretable big and diverse databases for drug design and development. Studies have demonstrated that CNN, recurrent neural network and deep belief network are compatible, accurate and effective for the molecular description of pharmacodynamic properties. In Covid-19, existing pharmacological compounds has also been repurposed using DL models. In the absence of the Covid-19 vaccine, remdesivir and oseltamivir have been widely employed to treat severe SARS-CoV-2 infections. In conclusion, the results indicate the potential benefits of employing the DL strategies in the drug discovery process.Communicated by Ramaswamy H. Sarma.
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