This is a news story, published by Phys Org, that relates primarily to Stanford news.
For more biology news, you can click here:
more biology newsFor more news from Phys Org, you can click here:
more news from Phys OrgOtherweb, Inc is a public benefit corporation, dedicated to improving the quality of news people consume. We are non-partisan, junk-free, and ad-free. We use artificial intelligence (AI) to remove junk from your news feed, and allow you to select the best science news, business news, entertainment news, and much more. If you like biology news, you might also like this article about
better antibody drugs. We are dedicated to bringing you the highest-quality news, junk-free and ad-free, about your favorite topics. Please come every day to read the latest Proteins news, Protein news, biology news, and other high-quality news about any topic that interests you. We are working hard to create the best news aggregator on the web, and to put you in control of your news feed - whether you choose to read the latest news through our website, our news app, or our daily newsletter - all free!
better proteinsPhys Org
•89% Informative
Stanford scientists have developed a new machine learning-based method to more quickly and accurately predict the molecular changes that will lead to better antibody drugs.
The approach combines the 3D structure of the protein backbone with large language models based on amino acid sequence.
Their approach resulted in a 25-fold improvement against a recently discontinued SARS-CoV-2 antibody therapy.
Stanford University's model points scientists to tens of proteins and, on average, half are better than the starting point.
This tool could be useful to quickly respond to emerging or evolving diseases.
It also lowers the barrier to making more effective medicines.
Stronger medicines mean lower doses are necessary, which means that a given quantity could benefit more patients.
VR Score
94
Informative language
95
Neutral language
64
Article tone
informal
Language
English
Language complexity
57
Offensive language
not offensive
Hate speech
not hateful
Attention-grabbing headline
not detected
Known propaganda techniques
not detected
Time-value
long-living
External references
5
Affiliate links
no affiliate links