David Mobley
@davidlmobley
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Professor, PharmSci & Chem @UCIrvine. Grad program director. Editor @LiveCoMS. Christian, dad of 6. @[email protected]
Irvine, CA
Joined February 2010
I really enjoyed this Nature Communications article on whether co-folding models are learning the physics of molecular interactions (spoiler: no, or not enough yet).
nature.com
Nature Communications - This study shows that deep-learning-based co-folding models often produce physically unrealistic structures and are unsusceptible in their predictions to significant binding...
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Certainly the tighter transmissions at the top is going to result in good students having to go to lower level institutions.
After listening to some US chemistry professors talk about graduate admissions for next year, if you are a senior this year, you need to be thinking about non-academic post-graduation plans RIGHT NOW
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In my group's journal club, we just read a paper on massively parallel free energy calculation for affinity maturation of miniproteins; it's a nice study and worth the read; from the Voelz group:
pubs.acs.org
Computational protein design efforts continue to make remarkable advances, yet the discovery of high-affinity binders typically requires large-scale experimental screening of site-saturated mutant...
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Check out our new protein review, "an overview of structurally oriented experimental datasets that can be used to benchmark protein force fields, focusing on data generated by NMR spectroscopy and room temperature protein crystallography." https://t.co/SbXBbPQOxd
#compchem
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It's come to my attention that ACS has developed a brilliant scheme to reduce manuscript processing time at its journals: Articles must be rejected and resubmitted if the revision would take too long. See attached image for full quote from actual ACS editorial office.
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I enjoyed this paper, from Phil Biggin's lab, "Are we fitting data or noise?" which looks at whether ML models exceed the maximum performance that could be expected given experimental error. I think there are some important insights here!
pubs.rsc.org
Data-driven techniques for establishing quantitative structure property relations are a pillar of modern materials and molecular discovery. Fuelled by the recent progress in deep learning methodology...
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Want more flexible relative binding free energy calculations? Open Free Energy's new release includes a SepTop implementation -- see release notes: https://t.co/helIktrk0E Method described in my group's paper here: https://t.co/2vuUfkHJFf
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New preprint, on active learning to balance exploration of multiple properties -- e.g. avoiding getting stuck in a region of chemical space that's good for potency but bad for solubility, etc. https://t.co/EcMdwGM4bb
#compchem In industry with problems in this space? Reach out.
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Need high quality force fields for your work? Interested in a future where you can pick a force field for your specific problem and know what level of quality to expect? We are too. Reach out if you're interested in helping @openforcefield accelerate discovery and design.
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Datasets/example uses cases needed for multiobjective active learning! Can you help us out? See description. #compchem
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Big news at @openmsf - Pat Walters has joined to lead OpenADMET!
patwalters.github.io
Starting today, September 15, 2025, I will assume a new role as Chief Scientist at OpenADMET, an open science initiative that combines high-throughput experimentation, computation, and structural...
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We're working on ways of handling slow rearrangement of waters in relative binding calculations, and we have a new approach out for dealing with slow water rearrangements using nonequilibrium switching:
pubs.acs.org
The formation of protein–ligand complexes involves displacement of water molecules that were previously occupying the protein’s binding site. In some cases, however, some water molecules may not be...
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Stumbled across this blog post on data contamination while looking for something else, and I really enjoyed it. I'm not advertising their model, but there are some interesting insights here.
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Are cofolding models moving beyond memorization? My research group's journal club is discussing a preprint on it this week, and it's worth a look.
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I need to find someone trustworthy to run an errand tomorrow morning in the Washington, DC area; does anyone have experience with gig economy options like TaskRabbit, Thumbtack or similar, or have other suggestions?
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I really like this preprint on thermodynamics of water in binding sites (specifically host-guest binding); it's super insightful and clarifies a lot of things for me. From Jeff Setiadi, Mike Gilson and others. https://t.co/3IWn5manAs
#compchem
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I contributed a bit to this one; we DO still need benchmarks in this area, and desperately so!
Excited to share our latest Perspective @JCIM_JCTC ! #SBDD predictions need reliable benchmarks - diverse targets, high-quality affinity & structural data, and blinded validation. Let’s make it happen! 🔗 Read more: https://t.co/BsLDAkCPxm
#DrugDiscovery #CompChem 🧵(1/6)
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New blog post out from @livecomsj on article statistics; I find it super cool to see this type of info in the open.
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Software tips sought: We're needing to switch some ring conformations; can anyone recommend an (OSS) tool to invert the boat/chair configuration of a given 6-membered ring?
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OMSF project's keynotes are online! Check out Open Force Field's at the link below: https://t.co/g5HAZwKoxu
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