We have a new preprint up on our “Binding Modes of Ligands Using Enhanced Sampling” (BLUES) approach to sampling ligand binding modes. This is work with the Chodera lab, and applies the nonequilibrium candidate Monte Carlo technique to hop between different potential ligand binding modes, allowing us to compute the occupancy of ligand binding modes substantially more efficiently than with standard molecular dynamics or Monte Carlo. [More…]
We had a great last year with four graduate students joining the lab and four graduating! Welcome Hannah, Martin, Chris and Tran! Congratulations to Guilherme, Camilla, Caitlin and Nathan!
Interested in predicting binding affinities? The SAMPL6 challenge is now live!
The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) series of blind challenges focuses on driving improvements in molecular modeling; in this sixth iteration of the challenge we have two new sets of host-guest binding data (from Gibb and Isaacs) for a new set of host-guest binding challenges. Additionally, we should shortly (thanks to Merck and the Chodera lab!) have new log D data for distribution coefficients. [More…]
I’m very excited that we’re going to be working with startup company Silicon Therapeutics on open source software development relating to binding free energy calculations. Silicon seeks to apply molecular simulations and free energy calculations to actually help design drugs, and is making major efforts to helping advance open source software in this area. Previously, Silicon announced their first Open Science Fellowship to Patrick Grinaway in John Chodera’s lab at MSKCC, supporting work on open source software for relative free energy calculations. [More…]
We’ve been helping to run the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) series of blind challenges for some time now, but the initiative is still unfunded. I’m excited that our revised grant proposal for the future of SAMPL has just been submitted to the NIH! While I normally wouldn’t post grant proposals, SAMPL is really a community resource, fueled by the community, so here’s a link to our plan for SAMPL’s future. [More…]
We’re super excited about the Statistical Assessment of Modeling of Proteins and Ligand (SAMPL) series of blind challenges, and the potential these have for driving real advancements in modeling in our field. These challenges not only provide an opportunity for blind, prospective testing of our methods, but they also provide a crowdsourcing model to drive innovation. Such approaches have a well-documented history of spurring progress in specific areas, such as in the XPrize, the Netflix Prize, or, closer to our field, the DREAM challenges or CASP for protein structure prediction. [More…]