数字化学:探索计算与实验的交汇点--定义、现状和未来展望

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY
Stefan Bräse
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引用次数: 0

摘要

数字化学是一种变革性的方法,将计算方法、数字数据和自动化整合到化学科学中。其定义是利用数字工具包和算法来模拟、预测、加速和分析化学过程和性质,从而增强传统的实验方法。数字化学的现状以几个关键领域的快速发展为标志:高通量筛选、机器学习模型、量子化学和实验室自动化。这些技术以前所未有的速度发现和优化新分子、新材料和新反应。数字逆合成和结构活性预测工具为这些努力提供了支持。此外,在化学实验室中集成大型语言模型和机器人技术(例如在自动驾驶实验室中的应用),已开始实现常规任务和复杂决策过程的自动化。展望未来,在计算资源日益普及、化学数据库不断扩大以及人工智能算法日臻完善的推动下,数字化学和数字化化学的未来将实现大幅增长。这种演变有望加速药物发现、材料科学和可持续制造领域的创新,最终实现更高效、更具成本效益和更环保的化学研究与生产。我们面临的挑战在于推动技术本身的发展、促进跨学科合作,以及确保在化学研究中合乎道德地使用数字工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Digital chemistry: navigating the confluence of computation and experimentation – definition, status quo, and future perspective

Digital chemistry: navigating the confluence of computation and experimentation – definition, status quo, and future perspective

Digital chemistry: navigating the confluence of computation and experimentation – definition, status quo, and future perspective

Digital chemistry represents a transformative approach integrating computational methods, digital data, and automation within the chemical sciences. It is defined by using digital toolkits and algorithms to simulate, predict, accelerate, and analyze chemical processes and properties, augmenting traditional experimental methods. The current status quo of digital chemistry is marked by rapid advancements in several key areas: high-throughput screening, machine learning models, quantum chemistry, and laboratory automation. These technologies have enabled unprecedented speeds in discovering and optimizing new molecules, materials, and reactions. Digital retrosynthesis and structure–active prediction tools have supported these endeavors. Furthermore, integrating large-language models and robotics in chemistry labs (e.g. demonstrated in self-driving labs) have begun to automate routine tasks and complex decision-making processes. Looking forward, the future of digital and digitalized chemistry is poised for significant growth, driven by the increasing accessibility of computational resources, the expansion of chemical databases, and the refinement of artificial intelligence algorithms. This evolution promises to accelerate innovation in drug discovery, materials science, and sustainable manufacturing, ultimately leading to more efficient, cost-effective, and environmentally friendly chemical research and production. The challenge lies in advancing the technology itself, fostering interdisciplinary collaboration, and ensuring the ethical use of digital tools in chemical research.

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