BigHat Biosciences

We are excited to share an important leadership update at BigHat Biosciences. Peyton Greenside has stepped into the role of CEO, bringing deep scientific expertise, strategic vision, and a strong commitment to our mission to develop better biologics through ML-guided design. We also want to express our sincere gratitude to Mark DePristo – his leadership and partnership have been instrumental in shaping BigHat into what it is today. We look forward to his continued support as an advisor in this next chapter. ? Read more here: https://lnkd.in/gwKFxMns?

BigHat’s machine learning team is excited to share its latest paper, in collaboration with the Gordon Wilson Lab at New York University, “Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences,” which was accepted for publication at the prestigious International Conference on Learning Representations (ICLR). This work aims to model the natural antibody affinity maturation process to guide Bayesian optimization of synthetic antibodies. This is accomplished by building large language models of putative clonal families of antibody sequences and then combining these models with BigHat-generated lab data to design mutations which improve both binding and stability. This work builds on the contributions of Alan Amin, Nate Gruver, Yilun Kuang, and Lily Le from Andrew Gordon Wilson’s lab, as well as Peyton Greenside, Hunter Elliott, Aniruddh Raghu, and Calvin McCarter at BigHat. Access to the paper can be found at https://lnkd.in/gC5-jngF

Hunter Elliott (Sr. Director, Machine Learning) will give his invited talk “Generative and Predictive Machine Learning for Iterative Multi-Objective Therapeutic Antibody Optimization” at the Biologic Summit 2025 in San Diego on January 15, 2025. In this talk, Hunter explores how BigHat's innovative Milliner? platform seamlessly integrates predictive and generative models with high-throughput wet labs to address complex protein engineering challenges in antibody therapeutics. He will share several case studies showcasing novel predictive and generative ML methods deployed on Milliner? to solve real-world therapeutic engineering problems. https://lnkd.in/gZf8ReRM ?

BigHat is excited to kick off 2025 with a productive J.P. Morgan Healthcare Conference 2025! On Tuesday, January 14, 2025 our CEO, Mark DePristo, will speak on the Next in Tech Bio | Computational Strategies for Drug Discovery panel (? 7:00PM @ ?? Benchling) and CBO, Elizabeth Schwarzbach, is thrilled to be hosting an Executive Roundtable at WuXi AppTec's #WuXiGlobalForum (? 1:30PM @??Continental Ballrooms (4-6) | Hilton Union Square) https://lnkd.in/eJ5m3X_j We can’t wait to see you there! ??

This holiday we’re thankful for the expertise and hard work of our talented employees, advisors, partners, board of directors and investors. Please know we appreciate each and every one of your contributions. Thank you for being a BigHatter! BigHat Biosciences will be completely shut down over the holidays, starting December 23rd, in order to foster a healthy work environment. We will reopen on December 30th, 2024, fully committed to advancing our mission of developing better biologics faster through ML-guided design. Wishing you a healthy and happy new year, The BigHat Team

BigHat’s VP of Discovery Medicine, Ryan Henrici MD PhD, will be giving his invited talk "Machine Learning-guided Design of Logic Gated and Avidity-driven T Cell Engagers for Solid Malignancies” at the Antibody Engineering & Therapeutics Conference on December 17th at 11 AM PST in San Diego. The talk will cover how machine learning supports efficient T cell engager (TCE) design to target co-expressed tumor antigens while sparing healthy tissues, even at high receptor densities. This approach integrates AI/ML with rapid wet-lab validation to advance safer and more effective therapies for solid tumors. Join the session to explore how data-driven methods are improving TCE development: https://lnkd.in/gFKs7-Fb In addition, Ryan shared his insights with AE&T last week into his journey from medical training to leading cutting-edge antibody engineering at BigHat, all fueled by a mission to create safer therapies. To watch the full interview: https://lnkd.in/gzGsyZzV?

Hunter Elliott, BigHat’s Sr. Director of Machine Learning, as well as Alan Amin, from the Gordon-Wilson lab at New York University, will be presenting a paper titled, “Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences” at the NeurIPS 2024 AIDrugX workshop, in Vancouver from December 10-15 This work aims to model the natural antibody affinity maturation process to guide Bayesian optimization of synthetic antibodies. This is accomplished by building large language models of putative clonal families of antibody sequences and then combining these models with BigHat-generated lab data to design mutations which improve both binding and stability. Read paper: https://lnkd.in/gz32tja5 Conference: https://neurips.cc/ Workshop: https://lnkd.in/gchP8dhG?

We welcome Sebastian Ober, Fah Sathirapongsasuti and Nicholas Young. Sebastian joins BigHat’s Machine Learning team as Senior Machine Learning Scientist I. Sebastian will focus on improving BigHat’s integration of multimodal experimental data in Bayesian Optimization for antibody design. Sebastian completed his PhD in the Machine Learning Group at the University of Cambridge, where he focused on representing uncertainty in deep learning and active learning. Prior to that, he completed his undergraduate and Masters degree in engineering, also at Cambridge. Before joining BigHat, Sebastian worked at AstraZeneca and Secondmind. Fah joins as BigHat's new Senior Director of Data Science. Fah brings over a decade of biopharma experience, specializing in computational biology for target and drug discovery. At 23andMe’s Therapeutics Division, Fah was a founding scientist and pioneered the use of human genetics for drug target identification and prioritization. He has also led bioinformatics and data science teams as Director of Bioinformatics, Genetic Medicine at MEDGENOME and Director of Computational Biology at Alloy Therapeutics, Inc., focusing on data organization and tool development for next-generation therapeutics. Fah holds a PhD in Biostatistics from Harvard University and BS/MS degrees in Mathematical and Computational Science/Computer Science from Stanford University. Nicholas Young joins as Scientist I, to our External Programs. Nick will be performing and developing assays to support our internal and partnered programs. Nicholas is a chemical biologist with an interest in dissecting sequence-function landscapes of proteins. During his graduate work, he developed methods for the high-throughput screening and functional characterization of proteases, small molecules, and antibodies. He earned his PhD in Chemistry and Chemical Biology from the University of California, San Francisco, and a BS in Chemistry from the Penn State University. In addition, Nicholas previously worked as a Postbaccalaureate Fellow at the National Institute of Mental Health (NIMH) and as a Protein Biochemistry Intern at Denali Therapeutics. Welcome to the team, Nicholas, Fah and Sebastian! https://lnkd.in/grt3Vjx9?

BigHat Biosciences is featured in STAT News today’s “Who to Know: 7 companies to watch in AI protein design.” Read more about our work and how we’re advancing the field of next-generation biologics: https://lnkd.in/gV3A-v7T?

Today, we’re thrilled to announce that BigHat is developing a best-in-class ADC that combines our cutting-edge ML-guided antibody design platform with Synaffix BV (“Synaffix”), a Lonza company’s (SWX:LONN) site-specific linker-payload technology. With these next-gen ADCs, we are excited to improve patient outcomes and deliver safer and more effective therapies to patients in need. Learn more about this license agreement and its potential impact: https://lnkd.in/gvMggA2S?