Postdoctoral Fellow Maria A. Castellanos wins poster prize at Computational Medicinal Chemistry School for AlphaFold-based prediction of antiviral spectrum

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Chodera lab Postdoctoral Fellow Maria A. Castellanos was awarded a poster prize at the Computational Medicinal Chemistry School held Oct 28-30, 2024 at the Novartis Institutes for Biomedical Research in Cambridge, MA.

Working with ASAP Antiviral Discovery Consortium computational chemistry lead Jenke Scheen and senior graduate student Alexander Payne, Maria has developed a pipeline aimed at predicting the breadth of antiviral activity for direct-acting small molecule antivirals, with the goal of prioritizing molecules for synthesis that maximize breadth of activity within a viral family. This pipeline leverages AlphaFold-like methods and high-throughput crystallographic data generated by the Diamond Light Source along with machine-learned affinity prediction to predict and score potential binding modes to the viral target across the viral family. The resulting pipeline will aid the $68M NIH-funded ASAP Consortium in developing broad-spectrum direct-acting antivirals to prevent future pandemics.

You can download the poster here: [PDF]
All code is open source: https://github.com/asapdiscovery/asapdiscovery

To learn more about Maria’s work, check out her LinkedIn.

2024 NCATS Assay Guidance Manual in silico drug discovery workshop

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I was honored to speak at the NCATS Assay Guidance Manual in silico drug discovery workshop, hosted virtually 23-24 Oct 2024. I presented an overview of modern physics-based free energy calculations for drug discovery, highlighting the open source ecosystem from Open Free Energy, Open Force Field, and other Open Molecular Software Foundation (OMSF) projects.

My slides are available via Google Slides (CC-BY 4.0)

OpenEye CUP XXIII talk: ML/MM REPEX/ATM FEP/MBAR RBFEs and You

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I had the privilege of being called on to give the final talk of this year’s OpenEye CUP XXIII to close an engaging meeting full of excitement for structure-based drug discovery. In this talk, I highlight the rapid work going on in machine learned (ML) potentials and their growing role in alchemical free energy calculations for structure-enabled drug discovery, and close with a provocative opportunity for the future of computer-aided drug discovery. [PDF]

Barcelona MMSML talk: Teaching free energy calculations to learn

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I was excited to attend the Barcelona MMSML workshop, which brought together a fantastic set of folks to discuss the future of machine learning in molecular simulation.

The slides to my talk, Teaching free energy calculations to learn, are available here: [PDF]

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

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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]

Kate Holloway award symposium talk

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I’m thrilled to have the opportunity to speak at the ACS Award for Computers in Chemical & Pharmaceutical Research in honor of Kate Holloway, whose incredible career in drug discovery to date involves contributions to the invention of over sixty ligands for different therapeutic targets, including successful HIV inhibitors that have significantly changed patient outcomes for those living with AIDS. Kate’s tireless work to improve the field and visionary view on what computational chemistry is capable of are an inspiration to us all.

You can find a PDF copy of my slides here: [PDF]

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

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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

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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

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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.