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机器学习赋能药物研发:应用、机遇与挑战。

Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges.

机构信息

School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China.

School of Software, Shandong University, Jinan 250101, China.

出版信息

Molecules. 2024 Feb 18;29(4):903. doi: 10.3390/molecules29040903.

Abstract

Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.

摘要

药物发现通过开发新的药物和治疗方法来对抗疾病,在促进人类健康方面发挥着关键作用。如何加速新药发现的步伐并降低成本,长期以来一直是制药行业关注的焦点。幸运的是,通过利用先进的算法、计算能力和生物大数据,人工智能(AI)技术,特别是机器学习(ML),有望使新药的研发更加高效。最近,在自然语言处理方面取得革命性突破的基于转换器的模型,引发了它们在药物发现中应用的新时代。在此,我们介绍了 ML 在药物发现中的最新应用,强调了先进的基于转换器的 ML 模型的潜力,并讨论了该领域的未来前景和挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8ea/10892089/10071fb3ae77/molecules-29-00903-g001.jpg

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