Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab 140401, India.
Department of Pharmacology, Unit of Biochemistry, Faculty of Medicine, Universiti Sultan Zainal Abidin, Kuala Terengganu 20400, Malaysia.
Curr Pharm Des. 2022;28(33):2690-2703. doi: 10.2174/1381612828666220608141049.
Due to the heap of data sets available for drug discovery, modern drug discovery has taken the shape of big data. Usage of Artificial intelligence (AI) can help to modify drug discovery based on big data to precised, knowledgeable data. The pharmaceutical companies have already geared their departments for this and started a race to search for new novel drugs. The AI helps to predict the molecular structure of the compound and its in-vivo vs. in-vitro characteristics without hampering life, thus saving time and economic loss. Clinical studies, electronic records, and images act as a helping hand for the development. The data mining and curation techniques help explore the data with a single click. AI in big data analysis has paved the red carpet for future rational drug development and optimization. This review's objective is to familiarise readers with various advances in the AI field concerning software, firms, and other tools working in easing out the labor of the drug discovery journey.
由于可供药物发现使用的数据集很多，现代药物发现已经呈现出大数据的形态。人工智能 (AI) 的使用可以帮助根据大数据修改药物发现，使之成为更精确、更有见识的数据。制药公司已经为这一目标调整了他们的部门，并开始竞相寻找新的新型药物。人工智能有助于预测化合物的分子结构及其体内与体外特性，而不会对生命造成损害，从而节省时间和经济损失。临床研究、电子记录和图像为开发提供了帮助。数据挖掘和策展技术有助于一键探索数据。大数据分析中的人工智能为未来的合理药物开发和优化铺平了道路。本篇综述的目的是让读者熟悉人工智能领域的各种进展，包括软件、公司和其他工具，这些进展有助于减轻药物发现之旅的劳动量。
