Advancing a new drug to market requires substantial investments in time as well as financial resources. Crucial bioactivities for drug candidates, including their efficacy, pharmacokinetics (PK), and adverse effects, need to be investigated during drug development. With advancements in chemical synthesis and biological screening technologies over the past decade, a large amount of biological data points for millions of small molecules have been generated and are stored in various databases. These accumulated data, combined with new machine learning (ML) approaches, such as deep learning, have shown great potential to provide insights into relevant chemical structures to predict in vitro, in vivo, and clinical outcomes, thereby advancing drug discovery and development in the big data era.

将新药推向市场需要大量的时间和财政资源投资。候选药物的关键生物活性，包括其功效、药代动力学 (PK) 和不良反应，需要在药物开发过程中进行研究。随着过去十年化学合成和生物筛选技术的进步，数百万个小分子的大量生物数据点已经生成并存储在各种数据库中。这些积累的数据与深度学习等新的机器学习 (ML) 方法相结合，显示出巨大的潜力，可以深入了解相关化学结构，以预测体外、体内和临床结果，从而推进药物发现和开发。大数据时代。