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Sabina (née Dylan) N. (They/Them)

@kpmanstheorem.bsky.social

Postdoc in Filizola Lab @ ISMMS | Voelz lab + Folding@home alum | Muay Thai

44 Posts123 Follower236 Folgt
Vincent Voelz@voelzlab.bsky.social · last mo.

Congrats to Guangfeng Zhou, Ph.D., who joins the Wistar Institute in Philly as an assistant professor! Guangfeng was one of the first grad students to join my lab at Temple in 2011 when *I* was an assistant prof; he went on to trailblaze AI-based drug discovery at the IPD at Univ. of Washington

The Wistar Institute Strengthens AI-Driven Drug Discovery with Recruitment of Computational Biophysicist Guangfeng Zhou

The Wistar Institute Strengthens AI-Driven Drug Discovery with Recruitment of Computational Biophysicist Guangfeng Zhou Press Releases

wistar.org

Sukrit Singh@sukritsingh92.bsky.social · 3mo ago

Now out in the Special Issue on "New Concepts in Drug Discovery: Drug residence time!" Edited by Markus Seeliger & Ruth Nussinov, the reviews cover new computational and experimental methodologies for predicting ligand affinity & kinetics. Link: www.sciencedirect.com/special-issu...

Sukrit Singh@sukritsingh92.bsky.social · 5mo ago

More protein-ligand data are needed for AlphaFold-like models (& AI/ML) to enable prospective design! Check out our piece in "Current Opinion in Structural Biology" – Equal parts a thank-you-letter to the PDB & summarizes how datasets will enable task-focused models! Link: doi.org/10.1016/j.sb...

Diagram titled ‘Protein–Drug Prediction Task Breakdown’ showing a hierarchical tree of modeling tasks. Top branches include apo monomer and protein–protein interactions, with sub-branches such as ensembles, induced-fit models, multimers, folded–unfolded complexes, and protein–ligand complexes with mutation effects on binding. Additional branches include potency, ADMET properties (e.g., solubility and blood–brain barrier penetration), and kinetics (e.g., koff). A legend indicates model maturity levels: solid lines (established), dashed lines (promising), and dotted lines (untapped).
emily sunshine@threnody.northsky.social · 4mo ago

gwen does fantastic work as usual on some of the 401,000 trans people who’ve had to flee hostile territory in their own country in the past 18 months.

Poster which shows color photo scene of moving, a room with boxes and clothes sitting on the floor, a chair, and the end of a bed (white rails forming a headrest and a mattress with a red cover). The text of the poster is: "Impact (small logo top left)," "According to a new survey by the Movement Advancement Project, approximately 401,000 transgender people have relocated from one U.S. state to another since the 2024 presidential election." "The mass relocations in 2025 due to safety concerns, political conditions, and anti-trans laws nd policies have largely impacted conservative states across the country."
gwen howerton@kissphoria.bsky.social · 4mo ago

NEW: Seattle might declare a civil emergency to help LGBTQ (mostly trans) people fleeing red states, so I looked for a Texas angle. An organizer told me that Texas is #1 among trans people looking to relocate: "The baseline hostility to their existence is unbearable." www.chron.com/culture/arti...

Kresten Lindorff-Larsen@lindorfflarsen.bsky.social · 4mo ago

If you missed the preprint, now is a good time to read the journal version of @grocklin.bsky.social et al’s fantastic paper on multiplexed HDX measurements Large-scale discovery, analysis and design of protein energy landscapes doi.org/10.1038/s415...

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bioRxiv Biophysics@biorxiv-biophys.bsky.social · last yr.

Large-scale discovery, analysis, and design of protein energy landscapes https://www.biorxiv.org/content/10.1101/2025.03.20.644235v1

Marta Filizola@martafilizola.bsky.social · 8mo ago

After the most surreal peer review process we are out in the wild with BOLD-GPCRs: A Transformer-Powered App for Predicting Ligand Bioactivity and Mutational Effects across Class A GPCRs | Journal of Chemical Information and Modeling pubs.acs.org/doi/full/10....

BOLD-GPCRs: A Transformer-Powered App for Predicting Ligand Bioactivity and Mutational Effects across Class A GPCRs

G Protein-Coupled Receptors (GPCRs) are important targets for drug discovery owing to their ability to respond to a broad range of stimuli and their involvement in numerous pathologies. Although traditional ligand-based and structure-based approaches have facilitated the development of effective therapeutics for many GPCRs, these approaches often fall short when applied to receptors with limited ligand or structural data. This limitation highlights the critical need for advanced strategies capable of accurately predicting ligand bioactivity across the entire GPCR family, especially for understudied receptor subtypes. In this study, we introduce BOLD-GPCRs (BERT-Optimized Ligand Discovery for GPCRs), a deep learning framework designed to enhance the prediction of ligand bioactivity across class A GPCRs. Accessible via a user-friendly web interface, BOLD-GPCRs employs transfer learning and leverages curated data sets of known class A GPCR ligands, receptor sequences, and signaling-relevant mutations. By integrating dense neural network classifiers with transformer-based protein language models, BOLD-GPCRs captures complex relationships between receptor sequence/function and ligand activity. Our results demonstrate that BOLD-GPCRs achieves robust predictive performance for both ligand bioactivity and mutational effects across a broad range of class A GPCRs, underscoring its potential as a valuable tool for ligand discovery, especially for poorly characterized receptors.

pubs.acs.org

Open Free Energy@openfree.energy · 9mo ago

OpenFE is ready for production! chemrxiv.org/engage/chemr... In collaboration with our industry partners, we ran benchmarking simulations of our hybrid-topology RBFE protocol on a large collection of both public and private protein-ligand binding datasets. #compchem

Large-scale collaborative assessment of binding free energy calculations for drug discovery using OpenFE

Accurately measuring compound binding affinities is key to driving the pharmaceutical development process. Rigorous physics-based in silico approaches, particularly alchemical free energy methods, hav...

chemrxiv.org