Towards Artificial Intelligence in Chemistry via the Rational Design of Chemical Compounds and Automated Discovery

Курс призван отразить современную химию на языке, доступном специалистам из других областей. В этом курсе мы обсудим приложения машинного обучения к поиску новых веществ и материалов с оптимальными свойствами на основе имеющихся научных данных (рациональному дизайну), а также рассмотрим работы, в которых используются роботизированные системы для автоматического исследования новых химических реакций.

Язык проведения курса: английский

Для участия в курсе требуется знание языка на уровне (upper) intermediate или выше.

The course is designed to reflect state-of-the-art chemistry in a language accessibleto specialists from other fields. The renaissance of machine learning and the advancement ofdata science have created the possibility for rational design, the targeted design of chemicalsubstances with optimal properties using available data, without often-expensive discovery bytrial-and-error and serendipity. Intensive automation of chemical research drives us towards

the discovery of new compounds outsourced to robots (“chemputer”), as was demonstratedwith several proof-of-concept devices.

In this course, we will discuss applications of classic machine learning (ML) andartificial neural networks (ANN) in chemistry, basics of cheminformatics, chemical databases(with their applications and deficiencies). We will briefly review the latest notable research inmedicinal chemistry, catalysis, materials science, and organic synthesis to highlight theapplications of classic ML and ANN. Successful examples of automated discovery of chemicalcompounds by specialized robots will also be discussed, as well as applications of artificialintelligence in the prediction of chemical syntheses.

Jupyter Notebook, freely available Python libraries and open-source datasets will beused for interactive demonstrations. To fully understand the course, you should be able towrite simple scripts in Python using Jupyter Notebook and be familiar with NumPy and scikitlearn. Although the course is most relevant for chemists andstudents in the fields related tochemistry as materials scientists, biologists, physicists, med students, geologists, other STEMstudents can find this course interesting

МАТЕРИАЛЫ К КУРСУ: https://drive.google.com/drive...