We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City (virtual), June 15-17, 2022. Fifteen lectures were presented during a virtual and public event with speakers from industry, academia, and non-for-profit organizations. During the meeting, applications, challenges, and opportunities in drug discovery, de novo drug design, ADME-Tox (Absorption, Distribution, Metabolism, Excretion and Toxicity) property predictions, organic chemistry and peptides, and antibiotic resistance were discussed.

In this video from the UK HPC Conference, Peter Dueben from ECMWF presents: Progress and Challenges for the Use of Deep Learning to Improve Weather Forecasts. I will present recent studies that use deep learning to learn the equations of motion of the atmosphere, to emulate model components of weather forecast models and to enhance usability of weather forecasts. I will then talk about the main challenges for the application of deep learning in cutting-edge weather forecasts and suggest approaches to improve usability in the future. Peter Dueben is a Royal Society University Research Fellow at the European Centre for Medium-Range Weather Forecasts (ECMWF). He is contributing to the development and optimization of weather and climate models for modern supercomputers.

A Duke team trained a computer to identify up to 200 species of birds from just a photo. Given a photo of a mystery bird (top), the A.I. spits out heat maps showing which parts of the image are most similar to typical species features it has seen before It can take years of birdwatching experience to tell one species from the next. But using an artificial intelligence technique called deep learning, Duke University researchers have trained a computer to identify up to 200 species of birds from just a photo. The real innovation, however, is that the A.I. tool also shows its thinking, in a way that even someone who doesn't know a penguin from a puffin can understand. The team trained their deep neural network -- algorithms based on the way the brain works -- by feeding it 11,788 photos of 200 bird species to learn from, ranging from swimming ducks to hovering hummingbirds.