Avner Schlessinger @SchlessingerLab
Computational biologist. Professor. Co-Director of Disease Mechanisms and Therapeutics Training Area @IcahnMountSinai. Opinions are mine. #DrugDesign #AI schlessingerlab.org New York, NY Joined September 2017-
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AI accurately captured a known kinase conformation, but missed the cryptic pocket observed experimentally. We also found that subtle chemical changes can shift molecules between distinct binding modes, highlighting the need to combine computation and experiments in drug discovery
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New paper from the lab. Using AI-guided modeling and experiments, we identified a hidden allosteric pocket in the cancer kinase PKMYT1. Led by @NHerrington24 in collaboration with @LazarusLab pubs.acs.org/doi/full/10.10…
Great The Scientist piece: two harmful mutations can restore protein function—challenging how we view genetic risk. I share my take on the structural logic behind this and relevance to drug discovery. My perspective the-scientist.com/two-harmful-va… Original study pnas.org/doi/10.1073/pn…
My interview with Technology Networks on rethinking target-based drug discovery. We discuss why target-first approaches often fail and how AI and structure-guided methods can better link targets to therapeutic impact. tinyurl.com/5evzw7h8 #DrugDiscovery #AIDrugDiscovery 🔗
Avner Schlessinger retweeted
V2P (variant-to-phenotype) is live: nature.com/articles/s4146… To our knowledge, first genomewide SNVs+indels (coding+noncoding incl. intergenic) model jointly predicting pathogenicity + disease domain (23 HPO groups; e.g. cardiac/immune/metabolic). @david_stientist @SchlessingerLab
Check out our new paper introducing V2P — a method that predicts both variant pathogenicity and disease phenotype across 23 HPO categories. With @ItanLab and @david_stientist and others nature.com/articles/s4146… Server: v2p.ai
Amazing collaboration. So glad to see it published!
V2P (variant-to-phenotype) is live: nature.com/articles/s4146… To our knowledge, first genomewide SNVs+indels (coding+noncoding incl. intergenic) model jointly predicting pathogenicity + disease domain (23 HPO groups; e.g. cardiac/immune/metabolic). @david_stientist @SchlessingerLab
Avner Schlessinger retweeted
Do you know what a PhD in Biomedical Sciences or Neuroscience can do for your career? Join us tomorrow from 4-5pm ET for a special information session featuring alumni from the Graduate School of Biomedical Sciences: mshs.co/4eWFLxw
Avner Schlessinger retweeted
Our latest work: As it turns out different positive allosteric modulators bind to different sites at the mu opioid receptor. Find out more in our @biorxivpreprint. biorxiv.org/content/10.110…
Avner Schlessinger retweeted
Artificial intelligence and machine learning are being embedded in every aspect of the drug discovery and development process. Despite AI’s use at each step, the billion-dollar question is whether it can bring a larger number of novel drugs to market. cen.acs.org/physical-chemi…
Interesting read: Structural insights into brain thyroid hormone transport via MCT8 and OATP1C1: Cell cell.com/cell/fulltext/…
@IgorUlitsky Congratulations, Igor!
Avner Schlessinger retweeted
Official announcement: this September 1, the Osman laboratory will be opening its doors in the biological sciences department at SUNY, Albany! Anyone interested in joining the lab, please contact me. [email protected] Lab website coming soon! @ualbany
@SchlessingerLab lab retreat 2025!
@RolandDunbrack Happy birthday!🎉
Avner Schlessinger retweeted
A postdoc position is open in my lab to develop AI/ML models for predicting functional effects of genetic variants and to lead large-scale PheWAS in major biobanks. Strong programming skills, a solid publication record, and a background in human genetics are required.
@DeborahSchecht1 @IcahnMountSinai @xiaofei_lin @GENbio Thank you!
Excited to see our new AI Small Molecule Drug Discovery Center @IcahnMountSinai featured in GEN! 🧬🧠 Read more: genengnews.com/topics/artific… Thank you @xiaofei_lin and @GENbio #AI #DrugDiscovery #Genetics
Avner Schlessinger retweeted
Today, @NIH announced a new policy to cap how much publishers can charge NIH-supported scientists to make their work publicly accessible. This reflects our broader effort to restore public trust in public health by creating an open, honest, and transparent research atmosphere. Read more ➡️ bit.ly/4nCdIbl
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