Cova Tânia, Vitorino Carla, Ferreira Márcio, Nunes Sandra, Rondon-Villarreal Paola, Pais Alberto
Coimbra Chemistry Centre, Department of Chemistry, University of Coimbra, Coimbra, Portugal.
Faculty of Pharmacy, University of Coimbra, Coimbra, Portugal.
Methods Mol Biol. 2022;2390:321-347. doi: 10.1007/978-1-0716-1787-8_14.
Artificial intelligence (AI) consists of a synergistic assembly of enhanced optimization strategies with wide application in drug discovery and development, providing advanced tools for promoting cost-effectiveness throughout drug life cycle. Specifically, AI brings together the potential to improve drug approval rates, reduce development costs, get medications to patients faster, and help patients complying with their treatments. Accelerated pharmaceutical development and drug product approval rates can further benefit from the quantum computing (QC) technology, which will ultimately enable larger profits from patent-protected market exclusivity.Key pharma stakeholders are endorsing cutting-edge technologies based on AI and QC , covering drug discovery, preclinical and clinical development, and postapproval activities. Indeed, AI-QC applications are expected to become standard in the pharma operating model over the next 5-10 years. Generalizing scalability to larger pharmaceutical problems instead of specialization is now the main principle for transforming pharmaceutical tasks on multiple fronts, for which systematic and cost-effective solutions have benefited in areas such as molecular screening, synthetic pathway design, and drug discovery and development.The information generated by coupling the life cycle of drugs and AI and/or QC through data-driven analysis, neural network prediction, and chemical system monitoring will enable (1) better understanding of the complexity of process data, (2) streamlining the design of experiments, (3) discovering new molecular targets and materials, and also (4) planning or rethinking upcoming pharmaceutical challenges The power of AI-QC makes accessible a range of different pharmaceutical problems and their rationalization that have not been previously addressed due to a lack of appropriate analytical tools, demonstrating the breadth of potential applications of these emerging multidimensional approaches. In this context, creating the right AI-QC strategy often involves a steep learning path, especially given the embryonic stage of the industry development and the relative lack of case studies documenting success. As such, a comprehensive knowledge of the underlying pillars is imperative to extend the landscape of applications across the drug life cycle.The topics enclosed in this chapter will focus on AI-QC methods applied to drug discovery and development, with emphasis on the most recent advances in this field.
人工智能（AI）由增强优化策略的协同组合构成，在药物发现和开发中有着广泛应用，为提升药物全生命周期的成本效益提供了先进工具。具体而言，人工智能具备提高药物获批率、降低研发成本、更快地将药物提供给患者以及帮助患者遵医嘱治疗的潜力。加速药物研发和药品获批率可进一步受益于量子计算（QC）技术，这最终将从专利保护的市场独占权中获取更大利润。关键的制药利益相关者正在认可基于人工智能和量子计算的前沿技术，涵盖药物发现、临床前和临床开发以及获批后活动。事实上，预计在未来5至10年，人工智能 - 量子计算应用将成为制药运营模式的标准。将可扩展性推广到更大的制药问题而非专注于特定领域，如今是在多个方面转变制药任务的主要原则，在分子筛选、合成途径设计以及药物发现与开发等领域，系统性且具成本效益的解决方案已从中受益。通过数据驱动分析、神经网络预测以及化学系统监测，将药物生命周期与人工智能和/或量子计算相结合所产生的信息，将能够（1）更好地理解过程数据的复杂性，（2）简化实验设计，（3）发现新的分子靶点和材料，并且（4）规划或重新思考即将到来的制药挑战。人工智能 - 量子计算的力量使得一系列不同的制药问题及其合理化得以实现，而这些问题由于缺乏合适的分析工具此前未得到解决，这展示了这些新兴多维方法潜在应用的广度。在此背景下，制定正确的人工智能 - 量子计算策略通常涉及一条陡峭的学习路径，特别是考虑到该行业发展的初期阶段以及记录成功案例的相对匮乏。因此，全面了解其基础支柱对于扩展药物全生命周期的应用范围至关重要。本章所涵盖的主题将聚焦于应用于药物发现和开发的人工智能 - 量子计算方法，重点关注该领域的最新进展。
