NIH awards initial $68M for AI-driven Structure-enabled Antiviral Platform (ASAP) for open science discovery of oral antivirals for pandemic preparedness

We are excited to announce that the NIH has awarded an initial $68M of funding for the first three years of the AI-driven Structure-enabled Antiviral Platform (ASAP) as one of the NIAID-funded U19 Antiviral Drug Discovery (AViDD) Centers. Led by PIs John Chodera (MSKCC), Ben Perry (DNDi), and Alpha Lee (PostEra), ASAP builds on our earlier work with the COVID Moonshot, which delivered a SARS-CoV-2 oral antiviral preclinical candidate in 18 months, and will develop an open global oral antiviral pipeline with the goal of delivering medicines for globally equitable and affordable access in partnership with the Drugs for Neglected Diseases Initiative (DNDi).

[ASAP concept] [DNDi press release] [ASAP website]

NIH BISTI talk: Open science antiviral discovery with the covid moonshot 🌙 🚀 and the open source drug discovery ecosystem

I had the great pleasure of speaking to the NIH Biomedical Information Science and Technology Initiative (BISTI) community on 3 Feb 2022 about open science antiviral drug discovery with the COVID Moonshot and how the open source computer-aided drug discovery ecosystem functions as a fantastic mechanism to enable collaboration to address major challenges in drug discovery. Open source software communities such as the Open Molecular Software Foundation (OMSF), which sponsors the Open Force Field Initiative and the Open Free Energy Consortium, and OpenMM, play a major role in this. Community-wide blind challenges, such as D3R, SAMPL, and the new CACHE effort (a CASP for computational hit-finding), are also collaborative open science engines that drive progress.

A PDF version of the slides I presented can be found here: [PDF]

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.

COVID Moonshot seeks NIH funding

The COVID Moonshot—our patent-free, open science effort to discover an orally available inhibitor for SARS-CoV-2 main viral protease that could be used for treatment or prophylaxis following exposure—submitted a proposal to the NIH NIAID COVID-19 Emergency R01 program for funding to complete our task to deliver an inhibitor for IND-enabling studies! Award decisions should be made in Sep 2020, and funds beginning Oct 2020.

You can read the scientific component of the proposal (submitted 2020-08-14) here: [PDF]

UPDATE: NIH timelines have pushed back proposal review to Jan 2021.

UPDATE: We submitted a one-page supplement with additional preliminary data on 2020-12-14: [PDF]

UPDATE: Summary Statements have been made available on 2021-02-01: [PDF]

Securing sustainable funding to enable OpenMM to continue to power the next decade in biomolecular modeling and simulation

OpenMM is the most widely-used open source GPU-accelerated framework for biomolecular modeling and simulation. It has been cited more than 1300 times, downloaded over 280,000 times from conda-forge alone, and has run on more than one million distinct computers. Its Python API makes it widely popular as both an application (for modelers) and a library (for developers), while its C/C++/Fortran bindings enable major legacy simulation packages to use OpenMM to provide high performance on modern hardware. OpenMM has been used for probing biological questions that leverage the $16B global investment in structural data from the PDB at multiple scales, from detailed studies of single disease proteins to superfamily-wide modeling studies and large-scale drug development efforts in industry and academia.

Originally developed with NIH funding by the Pande lab at Stanford, we now aim to fully transition toward a community governance and sustainable development model and extend its capabilities to ensure OpenMM can power the next decade of biomolecular research, guided by the OpenMM Consortium. To fully exploit the revolution in QM-level accuracy with quantum machine-learning (QML) potentials, we also plan to add plug-in support for QML models augmented by GPU-accelerated kernels, enabling transformative science with QM-level accuracy. To enable high-productivity development of new ML models with training dataset sizes approaching 100 million molecules, we will develop a Python framework to enable OpenMM to be easily used within modern ML frameworks such as TensorFlow and PyTorch. Together with continued optimizations to exploit inexpensive GPUs, these advances will power a transformation within biomolecular modeling and simulation, much as deep learning has transformed computer vision.

Recently, we applied for federal funding to realize this vision via a new NIH Focused Technology Research & Development R01 proposal, with strong support from the biomolecular simulation community. You can read the scientific components of the proposal we submitted here: [PDF]

We welcome your feedback on how OpenMM can continue to serve the needs of the biomolecular simulation community over the next decade and beyond!

Response to NIH RFI on preprints

Credit: Jessica Polka, ASAPbio

The NIH recently issued a request for information (RFI) on the role of preprints in NIH applications. You can read my response here. Many others shared insightful responses publicly, and ASAPbio has indexed them here.