Artificial Intelligence (AI) relies upon a convergence of technologies with further synergies with life science technologies to capture the value of massive multi-modal data in the form of predictive models supporting decision-making. AI and machine learning (ML) enhance drug design and development by improving our understanding of disease heterogeneity, identifying dysregulated molecular pathways and therapeutic targets, designing and optimizing drug candidates, as well as evaluating in silico clinical efficacy. By providing an unprecedented level of knowledge on both patient specificities and drug candidate properties, AI is fostering the emergence of a computational precision medicine allowing the design of therapies or preventive measures tailored to the singularities of individual patients in terms of their physiology, disease features, and exposure to environmental risks.

人工智能 (AI) 依赖于技术的融合以及与生命科学技术的进一步协同作用，以支持决策的预测模型的形式获取大量多模态数据的价值。人工智能和机器学习 (ML) 通过提高我们对疾病异质性的理解、识别失调的分子途径和治疗靶点、设计和优化候选药物以及计算机评估来增强药物设计和开发临床疗效。通过提供关于患者特异性和候选药物特性的前所未有的知识水平，人工智能正在促进计算精准医学的出现，允许根据个体患者的生理、疾病特征、和暴露于环境风险。