Coordination Funds

协调基金

• 批准号: 497274830
• 负责人: Professor Dr. Frank Glorius
• 金额: --
• 依托单位: Organisch-Chemisches Institut
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/497274830?language=en
• 关键词: Coordination; Funds

Artificial intelligence is indisputably among the fastest developing and most demanded topics of our time. This technology makes everyday life easier and changes society as well as the workplace. While IT companies, and academic groups from the fields of computer science and mathematics rapidly adopted the new field, natural sciences such as biochemistry or chemistry only now begin to gradually explore the potential of machine learning (ML) methods. Our goal is to develop and apply modern ML algorithms in their entire range to molecular problems. While current approaches already help, for example, to determine molecular properties and to screen molecules virtually, future molecular machine learning should use generative models to suggest molecules with specific properties and activities, develop and optimize reactions independently, and evaluate and interpret analytical data within seconds. The first step is the design of molecular representations that increase the understanding of ML and enable robust and comparable applications. In clever combination with state-of-the-art machine learning algorithms, problems such as small data sets, highly complex questions and large experimental errors can be overcome, and previously unknown molecular relationships can be found. Ultimately, applications that are highly valuable in everyday laboratory work should be converted in easy-to-use software suites and experimental scientists should be trained on them. Thus, this priority program will help to modernize an entire subject area. To achieve this, it is necessary to unite existing innovative efforts in the fields of biochemistry, chemistry, computer science, mathematics and pharmacy in order to use all available knowledge on the one hand and to combine the most modern methods of the theoretical and practical world to develop advanced machine learning models and methods on the other. This program will fulfill the AI strategy of the Bundesregierung and can establish Germany internationally as a leading location for molecular machine learning.The requested coordination funds and the underlying will help to bring together the individual research groups, to foster strong and beneficial relationships and collaborations, to train and enable the doctoral students, to connect the PP with the international community and also to reach out to the general public. I will work hard to ensure that this PP will become a success story and a scientific highlight.

人工智能无疑是我们这个时代发展最快、需求最多的话题之一。这项技术使日常生活变得更容易，并改变了社会和工作场所。虽然IT公司以及计算机科学和数学领域的学术团体迅速采用了这一新领域，但生物化学或化学等自然科学才刚刚开始逐步探索机器学习(ML)方法的潜力。我们的目标是开发和应用现代最大似然算法的整个范围内的分子问题。虽然目前的方法已经有助于例如确定分子性质和虚拟筛选分子，但未来的分子机器学习应该使用产生式模型来建议具有特定性质和活动的分子，独立开发和优化反应，并在几秒钟内评估和解释分析数据。第一步是设计分子表示，以增加对ML的理解，并使稳健和可比较的应用成为可能。巧妙地结合最先进的机器学习算法，可以克服数据集小、问题高度复杂、实验误差大等问题，并可以发现以前未知的分子关系。归根结底，在日常实验室工作中非常有价值的应用程序应该转换成易于使用的软件套件，并对实验科学家进行培训。因此，这一优先计划将有助于使整个学科领域现代化。为了实现这一目标，有必要联合生物化学、化学、计算机科学、数学和药学领域的现有创新努力，以便一方面利用所有可用的知识，另一方面结合理论和实践世界最现代的方法来开发先进的机器学习模型和方法。该计划将实现德国联邦储备委员会的人工智能战略，并可以在国际上将德国确立为分子机器学习的领先地点。所需的协调资金和基础设施将有助于将各个研究小组聚集在一起，建立牢固而有益的关系和合作，培养和使博士生能够，将PP与国际社会联系起来，并与普通公众接触。我将努力确保这次PP成为一个成功的故事和科学的亮点。