Welcome to the Isayev Lab News
Introducing our new lab news section where we share research updates, publication announcements, software releases, and developments from our group at Carnegie Mellon University.
I am pleased to introduce the news section of my research website. This space will serve as a central hub for sharing updates about our work at Carnegie Mellon University, where my group develops machine learning methods for chemistry and drug discovery.
Over the years, I have found that some of the most interesting aspects of research—the context behind a publication, the story of how a project evolved, or the practical lessons learned from developing software—often get lost in the formal constraints of academic publishing. This news section provides an opportunity to share those stories and to communicate our work in a more accessible format.
Here you will find announcements when our papers are published, along with explanations of the key findings and their broader implications for the field. I will also post updates about our open-source software projects, including AIMNet2, TorchANI, and other tools we develop for the computational chemistry community. Beyond publications and code, I plan to share research highlights that offer deeper perspectives on methodological advances, as well as general lab news covering awards, conference presentations, and updates about our team.
My research sits at the intersection of artificial intelligence and chemistry, and I believe strongly in open science and making our methods accessible to the broader community. All of our major software tools are released as open source, and I hope this news section will complement those resources by providing context that helps researchers make the most effective use of our work.
For those who want to follow our research through other channels, our publications are indexed on Google Scholar, our code repositories are hosted on GitHub, and I share quick updates on Twitter/X. This news section will offer more detailed perspectives than social media allows, while remaining more timely and informal than peer-reviewed publications.
I look forward to sharing our journey as we work to advance the capabilities of machine learning in chemistry and to develop tools that accelerate scientific discovery.