Drug discovery and the development of safe and effective therapeutics is an intricate procedure, further complicated in the context of cancer research by the inherent heterogeneity and complexity of the disease. To address the difficulties of identifying, validating, and pursuing a promising drug target, artificial intelligence (AI) technologies including machine learning (ML) have been adopted at all stages throughout the drug development pipeline. Various methods are widely employed to efficiently process and learn from experimental data sets, with agent-based models garnering thorough interest due to their ability to model individual cell populations with aberrant phenotypes. The predictive power of artificial intelligence modelling techniques founded in comprehensive datasets and automated decision-making generates an obvious avenue of interest for application in the drug discovery pipeline.

中文翻译：

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计算机能否设想出治愈癌症的复杂性？在人工智能建模和药物发现中使用人工智能技术

药物的发现以及安全有效治疗方法的开发是一个复杂的过程，在癌症研究的背景下，疾病固有的异质性和复杂性使其更加复杂。为了解决识别，验证和追求有希望的药物靶标的困难，整个药物开发流程的所有阶段都采用了包括机器学习（ML）在内的人工智能（AI）技术。多种方法被广泛采用以有效地处理实验数据并从实验数据集中学习，基于代理的模型由于具有建模异常表型的单个细胞群的能力而引起了广泛关注。