近日，施普林格·自然旗下学术期刊《自然 – 通讯》发表了一篇化学论文，称研究人员研发出一种机器学习模型，该模型能部分重现职业化学家在工作中积累的集体知识，即“化学直觉”。该研究认为，这或使今后的药物研发更高效。
据了解，这种机器学习模型名为“化学知识图谱”，它通过分析化学文献、数据库和职业化学家的经验，学习了化学知识、概念和反应机制等。它可以识别和预测化学反应，帮助研究人员更快速、准确地设计和筛选药物。
此次研究的发现引起了业界的高度关注。药物研发是一个漫长而复杂的过程，而机器学习的加入有望大大缩短这一周期。通过利用化学直觉，机器学习模型可以更高效地识别药物研发中的瓶颈，从而为研究人员提供更精确的目标。
此次研究的作者表示，他们希望通过这种机器学习模型，为药物研发提供一个更加高效、精确的指导。未来，这种技术有望在化学和生物领域得到广泛应用，为全球健康事业作出更大的贡献。
新闻翻译：
Title: Machine learning accelerates drug development
Keywords: machine learning, drug development, chemical intuition
news content:
Recently, the academic journal Nature Communications published a chemical paper, which claims that researchers have developed a machine learning model that can partially reproduce the collective knowledge of professional chemists in their work, known as “chemical intuition.” This study suggests that this could make drug development more efficient in the future.
据了解，这种机器学习模型名为“化学知识图谱”，它通过分析化学文献、数据库和职业化学家的经验，学习了化学知识、概念和反应机制等。它可以识别和预测化学反应，帮助研究人员更快速、准确地设计和筛选药物。
This study’s findings have attracted a lot of attention in the field. Drug development is a long and complex process, and the addition of machine learning is expected to significantly shorten this cycle. By using chemical intuition, the machine learning model can more efficiently identify the bottlenecks in drug development, providing researchers with more precise goals.
The authors of this study said that they hope this machine learning model will provide a more efficient and precise guidance for drug development in the chemical and biological fields. In the future, this technology is expected to be widely applied in chemistry and biology, and contribute to global health issues.
【来源】http://www.chinanews.com/gj/2023/11-01/10104557.shtml
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