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Mark Polk

@markpolk.io

Biophysics for cancer therapies in the Chodera Lab at Memorial Sloan Kettering Cancer Center. Views are my own.

7 Posts221 Follower470 Folgt
Jess White@jessbwhite.bsky.social · 6mo ago

Pleased to share our preprint from the Tansey lab at @mskcancercenter.bsky.social: the Pan-PreClinical (PPC) project. To mitigate biases in immortalized cell line screens, we curated an atlas of >2M experiments in ~2k patient-derived ex vivo constructs using 3k+ drugs. Interactive web portal here

bioRxiv Cancer Bio@biorxiv-cancer.bsky.social · 6mo ago

A Pan-Cancer Ex Vivo Drug Screen Atlas for Functional Precision Oncology https://www.biorxiv.org/content/10.64898/2026.02.14.705918v1

OMSF@omsf.io · last yr.

the longest journey begins with a single step. luckily for us, @arianaclerkin.bsky.social is writing hers down! read the first PLUMB blab of many below, and follow along as Ariana builds an open comp.chem project from the ground up.

PLUMB blab #1: We need better benchmarks for computer-aided drug design

Written by: Ariana Brenner Clerkin, PhD Welcome to a new series on the OMSF blog: PLUMB blab! This series will take you behind the scenes of PLUMB (Protein-Ligand Unified Metrics Benchmark), an open-...

omsf-blog.ghost.io

Sukrit Singh@sukritsingh92.bsky.social · last yr.

This work is now published in J Phys Chem B! Check out our work showing that simulations predict the impact of distal mutations on kinase-inhibitor binding, and our experimental NanoBRET dataset of 94 kinase mutations that provide a benchmark for future methods. Link: pubs.acs.org/doi/full/10....

Illustration showing a pipeline for predicting the impact of ABL1A kinase domain mutations on inhibitor binding. On the left, icons labeled 'Physics-based simulations' and 'Data-driven methods' feed into 'Prospective ΔΔG Predictions.' On the right, a 3D ribbon model of the ABL1A kinase domain (gray) highlights clinically observed mutations in orange and an inhibitor in blue.
Sukrit Singh@sukritsingh92.bsky.social · 2y ago

New preprint! We prospectively evaluate structure-based methods and measure how well they classify clinical kinase mutations as inhibitor–resistant or –sensitizing when compared against our NanoBRET assay. Alchemical methods are well-suited to modeling distal mutants! Link: doi.org/10.1101/2024...

Shao-Liang Zheng@shaoliangzheng.bsky.social · 2y ago

"The #undergraduates usually bring samples from the projects they are working on. In this case, the data they obtain from this trip may become the last piece of data they need before submitting their #research for #publication"🥳🥳 @actacrystc.iucr.org @actacryste.iucr.org #crystallography #education

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Shao-Liang Zheng@shaoliangzheng.bsky.social · 2y ago

📢 Proud to share our journey in #crystallography #education over past 15 years, spanning #K12 initiatives, #undergraduate #graduate education & groundbreaking #research #methodologies 😀https://doi.org/10.1107/S1600576724011373 🌟 @japplcryst.iucr.org @acastructuralsci.bsky.social #STEM #Innovation

John Chodera@jchodera.bsky.social · 2y ago

Everything is chaos, but I wanted to share some awesome recent science from the lab that hints at where the future of biomolecular simulation is headed: Foundation simulation models that can be fine-tuned to experimental free energy data to produce systematically more accurate predictions.

Figure 1 from arXiv preprint https://doi.org/10.1101/2025.01.06.631610 Fig. 1 Espaloma is an end-to-end differentiable molecular mechanics parameter assignment scheme for arbitrary organic molecules. Espaloma (extensible surrogate potential optimized by message-passing) is a modular approach for directly computing molecular mechanics force field parameters FFF from a chemical graph G such as a small molecule or biopolymer via a process that is fully differentiable in the model parameters FNN. In Stage 1, a graph neural network is used to generate continuous latent atom embeddings describing local chemical environments from the chemical graph. In Stage 2, these atom embeddings are transformed into feature vectors that preserve appropriate symmetries for atom, bond, angle, and proper/improper torsion inference via Janossy pooling.54 In Stage 3, molecular mechanics parameters are directly predicted from these feature vectors using feed-forward neural networks. This parameter assignment process is performed once per molecular species, allowing the potential energy to be rapidly computed using standard molecular mechanics or molecular dynamics frameworks thereafter. The collection of parameters FNN describing the espaloma model can be considered as the equivalent complete specification of a traditional molecular mechanics force field such as GAFF38,39/AM1-BCC55,56 in that it encodes the equivalent of traditional typing rules, parameter assignment tables, and even partial charge models. Reproduced from ref. 49 with permission from the Royal Society of Chemistry.
Jess White@jessbwhite.bsky.social · 2y ago

Happy to have been a member of the BATCHIE team who worked on this recent publication featured by @MSKLibrary: A Bayesian active learning platform for scalable combination drug screens dx.doi.org/10.1038/s414...

A Bayesian active learning platform for scalable combination drug screens - Nature Communications

Large-scale combination drug screens in cancer are extremely challenging because of the immense number of possible combinations. Here, the authors develop BATCHIE, a Bayesian active learning platform ...

dx.doi.org

Mark Polk@markpolk.io · 2y ago

So grateful to have had the opportunity to chat science in Berlin with our collaborators in the Volkamer Lab and in the UK at the Oxford Centre for Medicines Discovery and the Diamond Light Source. Many thanks to DLS for the tour!

I’m standing on a walkway in the Diamond Light Source facility. The hall I am standing in is in the shape of a ring, and a portion of it — with exposed ducts and pipes — is visible behind me. A yellow beamline station can be seen behind me, on a level below the one I am on.