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wilzzw.bsky.social
@wilzzw.bsky.social
8 followers 130 following 2 posts
https://github.com/wilzzw
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wilzzw.bsky.social
House and Senate appropriators endorse NIH budget increase, reject Trump’s proposed cuts www.statnews.com/2026/01/20/n... via @statnews.com
House and Senate appropriators endorse NIH budget increase, reject Trump’s proposed cuts
Congressional appropriations committees offered a near-total rebuke of President Trump’s proposed downsizing and reshaping of the NIH.
www.statnews.com
January 21, 2026 at 10:39 AM
Reposted
profmarciniak.bsky.social
#Pubmed problem fix?

Resetting DNS to Cloudfare 1.1.1.1 worked

I could use Pubmed on phone but not computer, but fixed Mac by:

settings > network > details > DNS > press “+” and type 1.1.1.1

🎉
profmarciniak.bsky.social Stefan Marciniak @profmarciniak.bsky.social · Mar 2
#Pubmed problems being reported

Please say if it’s working for you, your country and search engine

(perhaps retweet for larger sample size

🤷‍♂️ fine for me, UK & Safari
March 2, 2025 at 12:24 PM
Reposted
jchodera.bsky.social
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.
February 19, 2025 at 7:30 PM
wilzzw.bsky.social
What a strange time to start a scientific twee.. erm, bluesky
#helloworld
February 17, 2025 at 7:02 AM