It is not insane IMO and can be about a lot of other things. You’ll remember that a decade ago the NIH proposed something similar, the grant support index, that was meant to also achieve the same: broader distribution of research dollars and higher funding rates instead of a Matthew effect of more grants to fewer investigators with diminishing returns. Many young PIs were in support. Back then, I submitted a letter in support of it on behalf of many signatories newpislack.wordpress.com/ope… The NIH-wide initiative had its roots in related capping policies that resulted in significantly increased funding rates at NIGMS relative to other ICs. The few and powerful at rich institutions (massive outliers in the grant recipient distribution) killed the initiative before it could even be thoroughly discussed. Back when the GSI was proposed, ~6% of NIH funded researchers had the support equivalent to 3 R01s. If going by RPGs not total dollars, I’d expect that to be an even smaller percentage of affected researchers now given the proliferation of R35 as a mechanism that consolidates multiple projects into a single award. IMO, the over-proliferation of completely unsustainable soft money positions is not a slam dunk justification to act as a sink for public research dollars distributed by an agency beholden to more than those researchers and institutions. This is not really my fight anymore, other than a vested interest in thriving federal funding for scientific research, responsibly deployed to support new ideas and institutions not just concentrate them … but I do think seeing this as entirely about a hostile administration threatens to dismiss a serious policy proposal that should be thoroughly considered on its merits

Today we're announcing ESMFold2, an open scientific engine to power prediction, design, and discovery across protein biology. The new model delivers state of the art performance on protein interactions, especially antibodies, a critical modality for therapeutics. We have designed and validated miniprotein binders and single chain antibodies across five therapeutic targets that are important in cancer and immunology. We are seeing very high success rates, and affinities at levels consistent with therapeutic activity. We’re also releasing an atlas of 6.8 billion proteins, and 1.1 billion predicted structures. ESMFold2 is built on a state of the art language model that has been trained on billions of protein sequences. A world model of protein biology emerges through language modeling. We’ve used the techniques of mechanistic interpretability developed to understand large language models to understand the concepts ESM uses to represent proteins. The model’s representation space has a compositional organization of features across scales, levels of complexity, and abstraction, that reflects and mirrors the understanding of protein biology developed through a century of empirical science. This understanding emerges without prior knowledge, just from language modeling of protein sequences. Language models are becoming a powerful substrate to understand and program biology. The design of protein interactions is one of the most fundamental problems in biophysics, and has critical implications for the discovery of new medicines. A simple gradient based search with the model was able to discover high-affinity protein binders. I'm excited by the potential this has to accelerate basic science and the understanding of proteins. And especially for the new avenues it opens up for therapeutic design and medicine.

hey @meetgranola i think you should do an ad that goes granola the app for cereal entrepreneurs