COVID Moonshot proposes new Antiviral Drug Discovery (AViDD) Center embracing open science and open-IP for global, equitable access

The COVID Moonshot has shown our open science, structure-enabled AI-driven approach can go from fragment screen to preclinical phase in just 18 months spending less than $1M. We think our model is capable of changing antiviral discovery for pandemics for good.

Drug discovery for pandemics is broken. Patents don't make sense for future pandemics with uncertain timelines or for diseases that don't yet exist. The profit motive failed to deliver antivirals after SARS and MERS, and millions died of COVID.

We show there is an alternative: By building a robust, open pipeline of oral antivirals, we can prevent future pandemics, and bring a swift end to this one. There is a better way.

The first-generation oral antiviral from the COVID Moonshot is rapidly progressing toward the clinic under the Drugs for Neglected Diseases Initiative (DNDi), with the World Health Organization Access to COVID Tools Accelerator (ACT-A) funding our work under an open IP model that will ensure true global, equitable access for a true global health threat.

All pandemics are global health threats. Our best defense is a healthy global antiviral discovery community with a robust pipeline of open discovery tools. We have a plan to make this happen ASAP: with the AI-driven Structure-enabled Antiviral Platform.

We're thrilled to have had the opportunity to submit our ASAP concept to the recent NIH call to fund multiple Antiviral Drug Discovery (AViDD) Centers, which aim to prevent the US from being caught without clinic-ready antivirals before the next pandemic.

The best use of public funds to build a pipeline of clinic-ready antivirals is to ensure everyone can get them, so that we won't need them here at home.

Drug discovery for pandemics must be focused on global, equitable access from the very start.

We’ve assembled an incredible team for ASAP: From the identification of resistance-robust targets to high-throughput structural biology at Diamond Light Source to AI-driven hit and lead optimization leveraging the talents and capabilities of MedChemica, PostEra, Folding@home, embracing open science throughout.

With the Drugs for Neglected Diseases Initiative (DNDi) as a full partner in ASAP, we would aim to generate clinic-ready drugs under an open-IP model that could achieve true global, equitable access. 

Read more about our concept here: [PDF]

OpenMM secures federal funding though an NIH NIGMS R01 grant

Recently, OpenMM applied for NIH funding to seek a sustainable federal source of support to continue to serve and adapt to the changing needs of the molecular simulation community by providing a fast, flexible, and extensible platform for advanced biomolecular simulations.

We’re thrilled to report that the NIH has awarded us funding through Mar 2025 via NIH grant R01GM140090. Funding will continue to support lead OpenMM developer Peter Eastman, as well as new developers based in the computational biophysics lab headed by Gianni de Fabritiis. Together, this will enable us to not only continue to support, optimize, and maintain OpenMM, but to also extend it to take advantage of the unfolding revolution in quantum machine learning potentials that continue to transform our field.

With a newly redesigned website, a newly-established OpenMM Consortium helping steer scientific directions, recruitment of science communicator Joshua Mitchell to lead a major effort to refine our documentation and materials, added support for GPU-accelerated pytorch and tensorflow based potentials, and migration to the conda-forge ecosystem (with 158K downloads from conda-forge this year already), we’re off to a great start!

To read more about our plans to continue to extend OpenMM to tightly integrate OpenMM with modern ML frameworks such as TensorFlow, PyTorch, and JAX; allow machine learning potentials or collective variables defined in these machine learning frameworks to be easily used within OpenMM; and and Python libraries to make it easy to build next-generation hybrid quantum machine learning / molecular mechanics (QML/MM) models within these frameworks, check out our NIH research proposal here.

Thanks to all of you who submitted letters of support! Your support means the world to us.

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.