Uploaded May 2026 | Updated September 2026, 1 week ago
Computer Science & Biomedical Engineering Seminar Series
February 5, 2026
“Proteomics at Genome Scale: Connecting Molecules to Systems with Biological Foundation Models”
Samuel Sledzieski, Flatiron Institute Center for Computational Biology
Understanding the mechanisms linking genetic sequence to cellular function remains a central challenge in biology. Existing computational approaches are often computationally expensive or fail to generalize beyond well-studied model organisms and protein families. Samuel Sledzieski's work leverages protein language models to connect molecular structure to systems biology at genome scale. He will highlight three applications where protein language modeling unlocks new capabilities: training on molecular dynamics simulations to predict protein conformational dynamics, enabling an analysis of the allosteric behavior of KRAS; building de novo protein-protein interaction networks in non-model organisms, revealing previously uncharacterized proteins involved in coral stress response; and high-throughput screening of massive small molecule libraries for drug-target interactions, identifying novel kinase inhibitors with experimentally validated nanomolar affinity. Sledzieski will discuss how recent advances in contrastive learning, parameter-efficient fine-tuning, and multimodal representation learning address key computational barriers to effective genome-scale modeling. Finally, he proposes a research plan to model heterogeneity in protein structure and molecular interactions across cellular contexts.
Samuel Sledzieski is a Flatiron Research Fellow at the Flatiron Institute Center for Computational Biology and a visiting researcher at the Lewis-Sigler Institute for Integrative Genomics at Princeton University. His research uses protein language models to integrate molecular biophysics with systems genomics, with the ultimate goal of mapping the mechanisms of cellular behavior and complex disease. He has developed and released several open-source machine learning models, including D-SCRIPT, ConPLex, and RocketSHP, and has held research positions at Microsoft Research, Cellarity, Serinus Biosciences, and the Centre Scientifique de Monaco. A recipient of the NSF Graduate Research Fellowship, Sledzieski received his PhD (2024) and MS (2021) in computer science from the Massachusetts Institute of Technology, and his BS (2019) from the University of Connecticut.
Computer Science & Biomedical Engineering Seminar Series
February 5, 2026
“Proteomics at Genome Scale: Connecting Molecules to Systems with Biological Foundation Models”
Samuel Sledzieski, Flatiron Institute Center for Computational Biology
Understanding the mechanisms linking genetic sequence to cellular function remains a central challenge in biology. Existing computational approaches are often computationally expensive or fail to generalize beyond well-studied model organisms and protein families. Samuel Sledzieski's work leverages protein language models to connect molecular structure to systems biology at genome scale. He will highlight three applications where protein language modeling unlocks new capabilities: training on molecular dynamics simulations to predict protein conformational dynamics, enabling an analysis of the allosteric behavior of KRAS; building de novo protein-protein interaction networks in non-model organisms, revealing previously uncharacterized proteins involved in coral stress response; and high-throughput screening of massive small molecule libraries for drug-target interactions, identifying novel kinase inhibitors with experimentally validated nanomolar affinity. Sledzieski will discuss how recent advances in contrastive learning, parameter-efficient fine-tuning, and multimodal representation learning address key computational barriers to effective genome-scale modeling. Finally, he proposes a research plan to model heterogeneity in protein structure and molecular interactions across cellular contexts.
Samuel Sledzieski is a Flatiron Research Fellow at the Flatiron Institute Center for Computational Biology and a visiting researcher at the Lewis-Sigler Institute for Integrative Genomics at Princeton University. His research uses protein language models to integrate molecular biophysics with systems genomics, with the ultimate goal of mapping the mechanisms of cellular behavior and complex disease. He has developed and released several open-source machine learning models, including D-SCRIPT, ConPLex, and RocketSHP, and has held research positions at Microsoft Research, Cellarity, Serinus Biosciences, and the Centre Scientifique de Monaco. A recipient of the NSF Graduate Research Fellowship, Sledzieski received his PhD (2024) and MS (2021) in computer science from the Massachusetts Institute of Technology, and his BS (2019) from the University of Connecticut.
