https://medicine.yale.edu/news-article/yale-co-leads-new-national-consortium-for-rare-skin-disorders/ QT{{” Choate serves as associate director of the administrative core and leads Project 3 with co-investigator Mark Gerstein, PhD, of Yale who will direct biomarker data analysis and integration. Hongyu Zhao, PhD, of Yale will direct statistics and data analytics. “}}
Archive for the 'x78qt' Category
Yale Co-Leads New National Consortium for Rare Skin Disorders | Yale School of Medicine
October 14, 2026Can an AI-designed drug slow aging?
September 28, 2026https://ctrk.klclick.com/l/01M2ZDVD112D4GPK2S1Z8EH3BR_23
QT:{{”
Elysium Abstract
…
An AI-designed drug sent six aging clocks backward
In a 12-week phase 2a trial, an AI-designed drug for idiopathic pulmonary fibrosis reduced predicted biological age across six protein-based aging clocks in 42 participants. The findings don’t prove the drug slows aging, but they point to a new way to study it. “This is one of the few concrete examples of a drug largely designed with generative AI,” says Mark Gerstein, Ph.D., Albert L. Williams Professor of Biomedical Informatics at Yale and a member of Elysium’s Scientific Advisory Board. “Moreover, it’s impressive to see how AI was used throughout the whole drug-development process, from upstream target finding to analyzing the downstream trial for results related to longevity.” He adds: “This last use has great promise for aging research, showing how conventional disease trials can be made into dual-purpose designs suitable for studying longevity.” (Nature Biotechnology)
“}}
(55) Top 100 AI Leaders in Healthcare | LinkedIn
July 29, 2026https://www.linkedin.com/pulse/top-100-ai-leaders-drug-discovery-advanced-healthcare-colangelo/ 2019 list
To unsubscribe from this group and stop receiving emails from it, send an email to x77s+unsubscribe@gersteinlab.org.
Journal of Medical Internet Research – From Virtual Molecules to Clinical Trials: How AI Is Reshaping Preclinical Drug Discovery
May 31, 2026https://www.jmir.org/2026/1/e101366
QT:{{”
Mark Gerstein, PhD—professor of biomedical informatics, molecular biophysics, and computer science at Yale University—said that this paradox helps explain why enthusiasm around AI should be tempered with realism. “There is a lot of excitement with AI in drug discovery,” Gerstein said, “but the reality is that we have not been as successful at discovering drugs as we would have hoped.”
…Gerstein also points to equivariant neural networks as a promising advance in AI-driven drug discovery. Since molecules obey physical laws and can rotate, stretch, or change their conformation, predictive models must account for these transformations. Equivariant
architectures are designed to preserve these relationships, making them especially valuable for molecular modeling and binding prediction “}}
Cuffari, B. (2026). From virtual molecules to clinical trials: How AI is reshaping preclinical drug discovery. Journal of Medical Internet Research, 28, e101366. https://doi.org/10.2196/101366
Profs study deadly germ seen in Iraq | Yale Daily News
April 11, 2026https://yaledailynews.com/articles/profs-study-deadly-germ-seen-in-iraq
Profs study deadly germ seen in Iraq | Yale Daily News. (n.d.). Yale
Daily News. https://yaledailynews.com/articles/profs-study-deadly-germ-seen-in-iraq
Quick comment request on AlphaGenome for IBM Think
February 11, 2026Brodsky, S. (2026, February 10). AI models at IBM and DeepMind are pushing DNA toward a GPT era. IBM Think.
https://www.ibm.com/think/news/ibm-deepmind-ai-models-pushing-dna-toward-gpt-era
QT:{{
“What I found most novel about AlphaGenome was its multimodal nature,” Mark Gerstein, the Albert L Williams Professor of Biomedical Informatics at Yale University, and who was not involved in the research, told IBM Think in an interview. “The fact that it is trained on data from many different genomic modalities—for instance, RNA-seq, ATAC-seq and Hi-C—and predicts effects across these modalities is particularly notable.”
Gerstein said AlphaGenome stands out because it tries to predict multiple genomic signals simultaneously and treats them as connected rather than independent. Changes in chromatin state upstream, for example, can shape gene expression downstream—and models have long recognized those links. What’s new, in his view, is the scale at which AlphaGenome tries to fold those relationships directly into
sequence-to-function prediction.
He also highlighted how much DNA the model can “see” in one pass. The window, he said, is unusually large, on the order of a megabase. It’s a span big enough to capture regulatory effects that can sit far from the genes they influence.
…
Gerstein’s reaction comes with an asterisk: he called the results promising, but stressed that performance on curated benchmarks doesn’t always translate to messy real-world biology.
AlphaGenome, as he sees it, is powerful at describing what a single change might do within a genome model. But real genomes do not change one letter at a time. They come as whole, inherited packages, full of variants that shape one another’s effects. “In terms of limitations, one major issue is that the model predicts the effect of only a single variant and does not take into account the full genetic background of an individual’s personal genome,” he said. “Background genetics can substantially influence the impact of a particular variant,
particularly by strongly affecting how a gene is expressed in response to a mutation.”
He thinks the next step is imaginable, even if it is harder: a future version of this kind of work could move beyond scoring a single mutation in isolation and instead operate directly on personal genomes. “One could imagine extending AlphaGenome by building large models that operate directly on personal genomes,” he said.
Medicine demands forms of evidence that many model developers simply do not have access to, Gerstein noted.
“With respect to translation into clinical practice, the main requirement is the accumulation of many use cases in which the effects of particular mutations are documented, followed by downstream validation showing that the predictions are accurate and clinically useful,” Gerstein said. “There is no substitute in the medical world for experimental data and actual clinical validation, and this will be necessary before outputs from tools like this are accepted.” ….
He also stressed what AlphaGenome does not claim to do: “It is important to remember that this tool provides the molecular
consequences of specific mutations, not downstream phenotypic or disease-level effects,” he said. “As a result, additional work would be required to bridge that gap.”
“With respect to translation into clinical practice, the main requirement is the accumulation of many use cases in which the effects of particular mutations are documented, followed by downstream validation showing that the predictions are accurate and clinically useful,” Gerstein said. “In the medical world, there is no substitute for experimental data and actual clinical validation.”
“}}
With AlphaGenome, Researchers Are Using A.I. to Decode the Human Blueprint – The New York Times
January 28, 2026https://www.nytimes.com/2026/01/28/science/alphagenome-ai-deepmind-genetics.html
QT:{{”
But Dr. Koo and other outside experts cautioned that it represented just one step on a long road ahead. “This is not AlphaFold, and it’s not going to win the Nobel Prize,” said Mark Gerstein, a computational biologist at Yale.
AlphaGenome will be useful. Dr. Gerstein said that he would probably add it to his toolbox for exploring DNA, and others expect to follow suit. But not all scientists trust A.I. programs like AlphaGenome to help them understand the genome.
…
In 2021, Dr. Avsec and his colleagues unveiled a preliminary A.I. called Enformer, which they have since expanded into AlphaGenome. They trained the program on an even greater expanse of biological data. “It’s really an industrial scale,” Dr. Gerstein said
“}}
YSM Researchers Recognized with Yale Faculty Innovation Awards | Yale School of Medicine
December 23, 2025From Lab to Launch: Eleven Faculty Innovators Recognized for Turning Research into Real-World Impact | Yale Ventures
November 23, 2025QT:{{”
The 2025 Yale Faculty Innovation Awards honor academic founders whose startups—rooted in Yale research—are advancing breakthroughs in health, sustainability, and engineering.
Date: 11/20/2025
Yale University has recognized eleven faculty innovators with the 2025 Yale Faculty Innovation Awards for translating breakthrough research into ventures that address some of the world’s most pressing challenges. These academic founders are transforming discoveries made in Yale labs into technologies that improve human health, advance sustainability, and shape the future of science and society.
Spanning AI-powered drug discovery and precision health, novel therapeutics for neurodegenerative and autoimmune diseases, advanced materials and microLED engineering, and next-generation tools for molecular imaging and genomic privacy, this year’s awardees exemplify how Yale research drives real-world impact.
…
Advancing DNA analysis and privacy protection with cutting-edge data-science approaches.
“}}
