Slides from Labsgiving group meeting on software development best practices

It’s become a tradition in the lab to have a Labsgiving group meeting the week of Thanksgiving! This year, I was on the group meeting rotation, and we were lucky enough to have our wonderful collaborators from the Andrea Volkamer lab at the Charité in Berlin join as well!

I covered some topics on why we practice open science and open source software, and our current best practices for doing so. Best practices are ever evolving, and we are always learning ways to better practice our craft and share our work with the world. In that spirit, we have tried to capture much of our current practices in a GitHub repo to make it easier to gather feedback and continually refine our practices: [software development best practices]

I put together a short talk covering some of the highlights of our best practices. In case it’s useful, you can find the PDF slides here: [PDF]

Happy Thanksgiving, all!

~ John

The Chodera lab teams up with Andrea Volkamer to explore the interface of machine learning and free energy calculations

Professor Andrea Volkamer, Charité (Berlin) works at the frontier of structure-guided machine learning for drug discovery and kinase inhibition.

The Einstein Foundation and Stiftung Charité have awarded a BIH Einstein Visiting Fellowship to a new collaboration between the Chodera lab and Professor Andrea Volkamer of the Charité in Berlin to develop new approaches that meld structure-informed machine learning with free energy calculations to predict and design kinase polypharmacology. We will be hiring a postdoc and PhD student to be embedded within the Volkamer group within the exciting human health research environment at the Charité in Berlin. John Chodera will be making four extended visits to Berlin each year, and a significant travel budget will allow research personnel to make extended visits to both the Volkamer group at the Charité and the Chodera lab at the Memorial Sloan Kettering Cancer Center (MSKCC). [Job Postings]

An example workflow for utilizing structure-informed machine learning and free energy calculations to predict kinase polypharmacology.