At present, every nation is focused on meeting sustainable development goals (SDGs) by 2030 for social, economic, and environmental sustainability. Automation of drug discovery, reliable research and innovation, green chemistry, and a lean discovery process are all critical components, where sustainability implementation is applicable. The current pandemic crisis has contributed to making society more familiar with the significance of advancements in this area. According to prior studies, the adoption of Industry 4.0 enabling technologies could contribute to sustainability across all areas. It is also observed that there are limited studies that discuss industry 4.0 enabling technologies implementation for drug discovery in the progress of attaining sustainability. To overcome previous studies' limitations, this study presented a detailed discussion of the significance of industry 4.0 enabling technologies such as the Internet of Things (IoT), big data, machine learning, deep learning, metaverse, and digital twin. After advancing through the review, researchers not only get enlightened with various developments in the field of drug discovery using industrial 4.0 technologies but also understand the gaps for further research in this area. Finally, the study has suggested vital recommendations for future research such as digital twins will make the clinical trial process easier by reducing the requirement of volunteer subjects, Artificial intelligence and metaverse will deliver extraordinary in the field of medical education and drug discovery by giving an in-depth vision of drug-target interaction and effect of further modification on quantitative structure-activity relationships (QSAR).

目前，每个国家都致力于到 2030 年实现可持续发展目标 (SDG)，以实现社会、经济和环境的可持续发展。药物发现的自动化、可靠的研究和创新、绿色化学和精益的发现过程都是适用可持续实施的关键组成部分。当前的大流行危机有助于让社会更加了解这一领域进步的重要性。根据之前的研究，采用工业 4.0 支持技术可以促进所有领域的可持续发展。我们还发现，讨论工业 4.0 在实现可持续发展过程中药物发现的支持技术实施的研究非常有限。为了克服以往研究的局限性，本研究详细讨论了物联网（IoT）、大数据、机器学习、深度学习、元宇宙和数字孪生等工业4.0支持技术的重要性。通过评审后，研究人员不仅可以了解到利用工业4.0技术进行药物发现领域的各种进展，还可以了解该领域进一步研究的差距。最后，该研究为未来的研究提出了重要建议，例如数字孪生将通过减少志愿者受试者的要求来简化临床试验过程，人工智能和元宇宙将通过提供一种在医学教育和药物发现领域提供非凡的成果。 -药物-靶标相互作用的深度洞察以及进一步修饰对定量结构-活性关系（QSAR）的影响。