Discovering new molecules with desired functions or activities is crucial for human well-being by providing new medicines, securing the world’s food supply via agrochemicals, or enabling a sustainable energy conversion and storage to counter or mitigate climate change. However, the discovery of new molecules or molecular materials that are optimized for a particular purpose can often take up to a decade and is highly cost-intensive. Machine-learning (ML) methods can accelerate molecular discovery, which is of considerable importance generally, but especially in light of the COVID-19 crisis and future pandemics. To reach this goal of speeding up the discovery of new functional molecules, it is necessary to establish a dialogue between domain experts and ML researchers to ensure that ML positively impacts real world scenarios. The importance of this field has been acknowledged also by Stanford’s 2021 Artificial Intelligence index report which states that “Drugs, Cancer, Molecular, Drug Discovery” received the greatest amount of private AI investment in 2020, with more than USD 13.8 billion, 4.5 times higher than 2019. In this workshop, we are bringing together the expertise of excellent researchers in the field of ML and its applications to molecular discovery.
Please go here for more information: https://moleculediscovery.github.io/workshop2021/