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.

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.

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

Celebrating six years of the Chodera lab!

The Chodera lab turns six years old on 1 Nov 2018! It’s been an exciting six years, having submitted or published thirty-five papers and having twenty-four trainees pass into or through the lab since then. To celebrate, John Chodera was asked to give a Sloan Kettering Institute (SKI) talk highlighting some of the exciting accomplishments and future directions. You can find the video of the whole talk here: [YouTube]

Check out more videos at the Chodera lab YouTube Channel.

Congratulations to TPCB graduate student Chaya Stern for being selected for a 2018 MolSSI Phase I Fellowship!

Congratulations to Tri-Institutional PhD Program in Chemical Biology (TPCB) graduate student and NSF GRFP recipient Chaya Stern on receiving a 2018 MolSSI Phase I Fellowship to support her work in developing new algorithms and open source software for Bayesian inference of force field parameters from experimental and quantum chemical data! You can learn more about Chaya's work in this area by listening to her PyData NYC 2017 talk or reading her MolSSI Fellowship Proposal, and hear more about what Chaya is up to by following her twitter feed.

2018 Workshop on Free Energy Methods, Kinetics and Markov State Models in Drug Design: Videos are up!


The 2018 Workshop on Free Energy Methods, Kinetics and Markov State Models in Drug Design held at the Novartis NIBR campus in Cambridge, MA was a huge success! You can read some of the livetweeting by following #drugalchemy and #drugmsm on twitter, and catch up on talks you may have missed by going through our YouTube video playlist featuring recordings of all talks where the speakers consented to being recorded.

Special thanks to lead organizers Michael Schnieders and Greg Bowman, the fantastic panel of organizers, postdoc Levi Naden and Novartis Investigator Callum Dickson for wrangling A/V and venue details, Jose Duca for graciously allowing us to use the NIBR site, the Novartis A/V team, keynote speaker Mark Murcko, our hosts at Novartis, and all our wonderful speakers and participants for making this a success.

Seeking a joint Open Force Field Consortium / XtalPi Distinguished Postdoctoral Fellow

The Open Force Field Consortium (OpenFF, and XtalPi, Inc. ( seeks a Distinguished Postdoctoral Fellow to perform cutting-edge research in a unique academic-industrial joint effort.

OpenFF is an academic collaboration (based at UC Irvine / UC Davis / UC San Diego / Univ. Colorado Boulder / Sloan Kettering Institute in New York) that seeks to develop next-generation molecular mechanics force fields and associated parameterization software and data infrastructure. XtalPi is a pharmaceutical technology company founded in 2014 that is introducing revolutionary advances in drug research and development, and has established strategic partnerships with several major pharmaceutical companies and recently completed a Series B funding round with Sequoia, Tencent, and Google.

The Postdoctoral Fellow will work with OpenFF and XtalPi to improve the accuracy of molecular mechanics force fields for predicting crystal structures and binding free energies. The work spans the disciplines of force field development/validation and Bayesian inference, and seeks to answer the fundamental scientific questions: 1) What regions of chemical space are critical failures for current force fields? 2) What are the fundamental limitations of (a) current functional forms and (b) parameterization methods?

The position spans a two-year project period that involves a gradual transition from the OpenFF side to the XtalPi side. In Year 1, the Fellow will be hosted for ~9 months in one of the academic groups (to be determined by the OpenFF PIs, XtalPi, and the Fellow) and ~3 months at XtalPi in Shenzhen, China or Boston, MA. In Year 2, the time division will be 3-6 months academic / 6-9 months XtalPi. The Fellow will be considered for full-time employment at XtalPi after Year 2.

For more information, see the full job posting.

The Open Forcefield Consortium seeks a software scientist

The Open Forcefield Consortium [http://openforcefield.orgseeks a Lead Software Scientist to coordinate open source software development efforts for an interdisciplinary academic team developing next-generation molecular mechanics forcefields and associated parameterization infrastructure.

See the full job ad here.

Postdoc Sonya Hanson joins Nobel Laureate Joachim Frank's lab

Congratulations to Postdoctoral Fellow Dr. Sonya Hanson, who has joined the laboratory of Joachim Frank at Columbia University to learn the exciting new technique of cryo-EM spectroscopy coupled with manifold embedding to reveal the dynamic conformational landscapes of TRP channels, an important class of integrative sensing proteins Sonya extensively studied as a graduate student. Frank was recently awarded the Nobel Prize in Chemistry (along with Jacques Dubochet and Richard Henderson) for his seminal contributions to advancing the technology of cryo-electron microscopy to become a powerful technique for imaging the conformations of biomolecules. Sonya will maintain strong links with the laboratory and the Folding@home Consortium as she progresses toward an independent faculty position.

You can see more fantastic work from Dr. Hanson at her Google Scholar page, check out her recent paper on the heat activation of TRPV1 in PNAS, and find out more about her career trajectory at her website.

Postdoc Gregory Ross joins Schrödinger as a Senior Scientist

We're excited to announce that Postdoctoral Fellow Dr. Gregory Ross has joined Schrödinger as a Senior Scientist, where he will be working to bring his expertise in statistical mechanics and semigrand canonical methods to their suite of molecular modeling and simulation tools.

You can see more fantastic work from Dr. Ross at his Google Scholar page, and check out his recent preprint on semigrand canonical methods for simulating realistic biomolecular salt concentrations on bioRxiv.

Chodera lab awarded NIH R01 to study role of conformational reorganization energy in selective kinase inhibition

The Chodera lab was awarded an NIH R01 research grant to study the role of conformational reorganization energy in selective kinase inhibition. Because even evolutionarily related kinase can have vastly different energetic costs to adopt inhibitor-bound conformations, these difference can be exploited to design new selective inhibitors, but only with computational approaches to elucidate hidden conformational states and their energetic penalties.

You can read more about our research on selective kinase inhibitor design, or download the entire NIH R01 proposal we submitted here.

Chodera lab awarded NSF grant to study new techniques for Bayesian forcefield parameterization

The Chodera lab has been awarded an NSF grant funding Data-Driven Discovery Science in Chemistry (D3SC) for a collaborative project with the laboratory of Michael Shirts (University of Colorado) that explores the use of advanced Bayesian methodologies for parameterization in molecular mechanics forcefields of small molecular liquids.

Congratulations to Rafal Wiewiora on receiving a Cancer Research Horizon Award!

Rafal Wiewiora, a graduate student in the Tri-Institutional Program in Chemical Biology, has been awarded a DoD Peer Reviewed Cancer Research Horizon Award.  This two-year grant supports exceptional junior-level scientists pursuing research in cancer with the guidance of a faculty mentor. Rafal’s project investigates the conformational heterogeneity of histone methyltransferases using molecular dynamics simulations on the Folding@home platform, a worldwide distributed computing project where hundreds of thousands of people around the world contribute their computing power toward the understanding of cancer targets. These models of conformational dynamics will be vital in aiding the in silico design of selective chemical probes to understand the roles that these methyltransferases play in cancer, as well as to develop new strategies to inhibit them. A native of Poland, Rafal received his MChem degree from the University of Oxford. 

Combined computational and experimental approach to discovering selective inhibitors of EZH2 and SETDB1 that exploit differences in conformational reorganization energies to achieve selectivity.

Combined computational and experimental approach to discovering selective inhibitors of EZH2 and SETDB1 that exploit differences in conformational reorganization energies to achieve selectivity.

Congratulations to Patrick Grinaway on being funded by an Open Science Fellowship from Silicon Therapeutics!

PBSB graduate student Patrick Grinaway will be funded by the first Open Science Fellowship from Silicon Therapeutics to develop new open source methodologies and software for scalable relative free energy calculations, building the foundation for truly automated small molecule design. This code will be part of the growing OpenMM software ecosystem, which makes use of OpenMM's GPU acceleration to achieve high performance while still being easy to build complex molecular modeling applications in Python.

Read more about the science here.

[press release]

Congratulations to Chaya Stern for receiving a Diversity Scholarship to attend SciPy 2017!

Congratulations to TPCB graduate student Chaya Stern for receiving a Diversity Scholarship to attend SciPy 2017 in Austin, TX July 10-16. SciPy is a conference focusing on scientific computing with Python, and brings together a community of open source software developers and users from industry, academia, and government to show off their projects, learn from each other, and collaborate to develop better code.

We're grateful to JumpTrading and NumFocus for providing the funds for this fellowship.

Postdoc Gregory Ross awarded Postdoctoral MolSSI Fellowship

Congratulations to postdoc Gregory Ross for being awarded an inaugural MolSSI Postdoctoral Software Science Fellowship! Greg's project focuses on building a toolbox of self-tuning Monte Carlo methods for use alongside molecular dynamics based Markov chain Monte Carlo (MCMC) sampling to greatly enhance the efficiency of molecular simulations and facilitate the construction of new sampling algorithms. Read on for a more detailed description of his project.


An automatic sampling toolbox for molecular simulation

Gregory Ross, DPhil
In the biomolecular simulation community, molecular dynamics (MD) is the most popular method for sampling molecular configurations. MD is well suited to capturing global, concerted movements, but it struggles to sample configurations that are separated by large energetic barriers. As a result, configurations taken from MD simulations tend to be from the same local region. An equivalent problem is encountered in Bayesian inference when drawing samples from a high-dimensional, multimodal posterior distribution. MD-related techniques are a small subset of a large class of Markov chain Monte Carlo (MCMC) techniques, and practitioners in both the biomolecular simulation and Bayesian inference communities have, in principle, a great deal of choice on which sampling method to use. However, it is difficult to know which MCMC method will the most efficient at generating uncorrelated samples for a particular system. In my project, I will develop a toolbox for MCMC that is agnostic with regard to what is being sampled, whether a protein conformation or posterior distribution. I will focus on using adaptive MCMC methods to help select the sampling technique that works best for a given problem. The general idea is to write a library of MCMC tools and a software interface that allows for the construction of MCMC “blocks” which can be mixed together and easily applied. The goal is to simplify sampling from difficult distributions and, ultimately, to widen the range of phenomena currently accessible by molecular simulation.