Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
Drug Discov Today. 2021 Apr;26(4):887-901. doi: 10.1016/j.drudis.2021.01.013. Epub 2021 Jan 20.
Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.
在过去的二十年中，制药行业的研究与开发（R&D）生产力一直受到密切关注，尤其是考虑到淘汰率报告和药物开发的巨大成本。表型和基于靶点的两种主要药物发现方法各自的优点在研究界引起了争议，因为每种方法在识别具有成功市场途径的新型分子实体方面都有不同的优势。然而，它们在临床上的转化能力都很低。人工智能（AI）和机器学习（ML）工具的采用有望彻底改变药物开发，并克服药物发现管道中的障碍。在这里，我们评估了基于靶点和表型的方法的潜力，并从科学和行业的角度全面描述了该领域的现状。随着人工智能/机器学习与制药业之间的新兴合作仍处于起步阶段，我们特别关注表型药物发现，研究其潜在和当前的局限性。最后，我们强调公私合作伙伴关系（PPP）和跨学科合作的价值，以促进创新并促进高效的药物发现计划。
