Prof. Abigail Doyle - Development of ML methods for reaction optimization and prediction скачать в хорошем качестве

Prof. Abigail Doyle - Development of ML methods for reaction optimization and prediction

In this webinar, Prof. Abigail Doyle talks about Machine learning (ML), the development and study of computer algorithms that learn from data, is increasingly important across a wide array of applications, from virtual personal assistants to social media and product recommendation systems. ML methods have also driven key developments in the natural sciences: virtual screening of drug-like molecules for medical applications, rapid prediction of physical data, and computer-aided synthesis planning have all been facilitated by ML. The development of ML tools for synthetic methodology development and catalysis could enable chemists to make data-efficient choices and learn from that data in the course of reaction prediction, reaction condition optimization, and mechanistic interrogation. This lecture will describe my group’s efforts to develop and apply open-source data science tools to numerous aspects of synthetic methodology development, including substrate scope design, ligand discovery, reaction optimization and mechanistic elucidation.