Using AI Approaches to Target Undruggable Cancer Targets (Speaker Bios)

Mohammed AlQuraishi is an Assistant Professor in the Department of Systems Biology and a member of Columbia’s Program for Mathematical Genomics, where he works at the intersection of machine learning, biophysics, and systems biology. The AlQuraishi Lab focuses on two biological perspectives: the molecular and systems levels. On the molecular side, the lab develops machine learning models for predicting protein structure and function, protein-ligand interactions, and learned representations of proteins and proteomes. On the systems side, the lab applies these models in a proteome-wide fashion to investigate the organization, combinatorial logic, and computational paradigms of signal transduction networks, how these networks vary in human populations, and how they are dysregulated in human diseases, particularly cancer. Dr. AlQuraishi holds undergraduate degrees in Biology, Computer Science, and Mathematics. He earned an M.S. in Statistics and a Ph.D. in Genetics from Stanford University, and was a Fellow in Systems Biology at Harvard Medical School prior to joining the Columbia Faculty.

Regina Barzilay is a School of Engineering Distinguished Professor of AI & Health in the Department of Computer Science and the AI Faculty Lead at MIT Jameel Clinic. She develops machine learning methods for drug discovery and clinical AI. In the past, she worked on natural language processing. Her research has been recognized with the MacArthur Fellowship, an NSF Career Award, and the AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity. Regina is a member of the National Academy of Engineering, National Academy of Medicine, and the American Academy of Arts and Sciences.

Ann leads Biology at Dewpoint, and is an expert in gene control, cancer biology, and condensate biology. She is passionate about advancing basic biomedical science into medicine to alter the course of disease and improve human health. Ann led pioneering research focused on the role of condensates in gene control and cancer therapeutic drug activity during her tenure at Whitehead Institute of the Massachusetts Institute of Technology as a Postdoctoral Fellow and Senior Researcher in the laboratory of Dewpoint co-founder, Richard A. Young. Her work led to several seminal discoveries in the field. One was the realization that the transcription factors’ propensity to phase separate, encoded in intrinsically disordered regions, contributes to gene transcription regulation. A second was that partitioning of anti-cancer drugs into condensates influences their activity. Together, these two foundational discoveries, paved the way on how Dewpoint approaches drugging ‘undruggable’ targets and optimizes condensate-modifying drugs (c-mods) for efficiency and safety.

The Chodera Lab is developing the infrastructure to enable fully autonomous small molecule drug discovery powered by next-generation hybrid physical/machine learning models to predict potency, selectivity, resistance, and other properties relevant to drug discovery in a data-efficient manner. We develop advanced predictive computational methodologies for drug discovery in frontier areas where these models are lacking, working with industry collaborators to deploy those solutions in real drug discovery programs. We build open source tools---such as OpenMM, which has been downloaded over 1.2 million times, run on millions of machines, and powers advances such as AlphaFold---that enable these algorithms to have impact across the drug discovery and biomolecular modeling fields. We create open science communities---such as (1) the Open Force Field Initiative [http://openforcefield.org], (2) the COVID Moonshot (which delivered a patent-free COVID antiviral preclinical candidate into an IND-enabling preclinical program funded by the Wellcome Trust) [https://dndi.org/research-development/portfolio/covid-moonshot], (3) the Folding@home Consortium [http://www.foldingathome.org] which harnesses hundreds of thousands of volunteer computers around the world to accelerate biomolecular simulation and discovery, and (4) the AI-driven Structure-enabled Antiviral Program that received $68M in initial funding from the NIH for open science antiviral drug discovery [http://asapdiscovery.org]. We apply these tools to cancer, where we develop models for (1) designing small molecule kinase inhibitors with targeted polypharmacology, (2) predicting drug sensitivity or resistance of clinical mutations, and (3) understanding the detailed structural mechanisms underlying oncogenic mutations. In the laboratory, we aim to train the next generation of interdisciplinary scientists equipped to tackle the most challenging problems in health and human disease for industrial drug discovery.

More information can be found at http://choderalab.org

Anna Cichońska holds a PhD in Information and Computer Science from Aalto University, Finland, where she collaborated with the Institute for Molecular Medicine Finland (FIMM). Her PhD work focused on developing machine learning methods and tools for biomedical research within the 'multiple genes - multiple diseases - multiple drugs' paradigm. Anna co-organized the IDG-DREAM Drug-Kinase Binding Prediction Challenge, a global competition to predict compound-kinase interactions. After her postdoctoral research at FIMM, Anna worked as a Senior Data Scientist at the biotechnology company Nightingale Health, leading scientific collaborations with some of the largest biobanks in the world. Currently, Anna heads Data Science at Harmonic Discovery, a biotechnology startup that is building advanced artificial intelligence to design novel kinase drugs.

Dr. Marzyeh Ghassemi is an Associate Professor at MIT in Electrical Engineering and Computer Science (EECS) and Institute for Medical Engineering & Science (IMES). She holds MIT affiliations with the Jameel Clinic, LIDS, the Broad and CSAIL. Professor Ghassemi was named a CIFAR Azrieli Global Scholar and one of MIT Tech Review’s 35 Innovators Under 35. In 2024, she received an NSF CAREER award, and Google Research Scholar Award. Prior to her PhD in Computer Science at MIT, she received an MSc. degree in biomedical engineering from Oxford University as a Marshall Scholar, and B.S. degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University.

Wengong Jin is an assistant professor in Khoury College of Computer Science at Northeastern University. Previously, he obtained his Ph.D. in computer science at MIT and finished his postdoc training in the Eric and Wendy Schmidt Center at Broad Institute. His research focuses on machine learning for drug discovery. He is particularly interested in developing geometric deep learning and generative AI models for drug discovery. His work was published in leading AI conferences and biology journals like ICML, NeurIPS, ICLR, Nature, Science, and Cell. He is the recipient of the BroadIgnite Award, Dimitris N. Chorafas Prize, and MIT EECS Outstanding Thesis Award.

Nevan Krogan, PhD, is a molecular biologist, UC San Francisco professor, and director of the intensely interdisciplinary Quantitative Biosciences Institute (QBI) under the UCSF School of Pharmacy. He is also a senior investigator at the Gladstone Institutes.

He led the work to create the SARS-CoV-2 interactome and assembled the QBI Coronavirus Research Group (QCRG), which includes hundreds of scientists from around the world. His research focuses on developing and using unbiased, quantitative systems approaches to study a wide variety of diseases with the ultimate goal of developing new therapeutics.

Nevan serves as Director of The HARC Center, an NIH-funded collaborative group that focuses on the structural characterization of HIV-human protein complexes. Nevan is also the co-Director of four Cell Mapping initiatives: the Cancer Cell Mapping Initiative (CCMI), the Host Pathogen Map Initiative (HPMI), the Psychiatric Cell Map Initiative (PCMI), and the (QCRG AViDD) NIH grant which is the largest grant awarded in the University of California history. These initiatives map the gene and protein networks in healthy and diseased cells with these maps being used to better understand disease and provide novel therapies to fight them.

Nevan has authored over 350 papers in the fields of genetics and molecular biology and has given over 400 lectures and seminars around the world. He is a Searle Scholar, a Keck Distinguished Scholar, a recipient of the Roddenberry Prize for Biomedical Research, and was recently elected to EMBO (European Molecular Biology Organization) membership. In recognition of his collaborative work bringing scientists across the globe to work together on SARS-CoV-2, Nevan Krogan was awarded the French Legion of Honor in 2021, the Louis Pasteur Medal in 2022, and the Research!America Discovery|Innovation|Health Prize in 2023.

Michael V. LeVine is a senior drug discovery scientist at D. E. Shaw Research (DESRES), where he works on computation-driven, early-stage drug discovery. Prior to DESRES, Michael was a faculty member in the Institute for Computational Biomedicine at Weill Cornell Medicine, the medical college of Cornell University. Michael received his Ph.D. from the Physiology, Biophysics, and Systems Biology program at Weill Cornell Medicine and received his B.A. from Wesleyan University, where he majored in Chemistry, Molecular Biology & Biochemistry, and Neuroscience & Behavior.

Dr. Ma’ayan is a Mount Sinai Endowed Professor in Bioinformatics, Director of the Mount Sinai Center for Bioinformatics, Professor in the Department of Pharmacological Sciences, Professor in the Department of Artificial Intelligence and Human Health, and faculty member of the Icahn Genomics Institute. Dr. Ma'ayan is also a Principal Investigator of the NIH Common Fund Data Resource Center (DRC) for the Common Fund Data Ecosystem (CFDE), a NCI-funded ITCR resource center, a NIDDK-funded diabetes hypothesis platform, and the NCI-funded Mount Sinai Proteogenomic Data Analysis Center. The Ma'ayan Laboratory applies computational methods to study the inner workings of regulatory networks in mammalian cells. His research team applies machine learning and other statistical mining techniques to study how intracellular regulatory systems function as networks to control cellular processes such as differentiation, dedifferentiation, apoptosis and proliferation. The Ma'ayan Laboratory develops bioinformatics software applications to enable experimental biologists to form novel hypotheses from high-throughput omics datasets, while aiming to better understand the structure and function of regulatory networks in mammalian cellular and multi-cellular complex systems.

Wade is the director of the Proactive Health Office at ARPA-H where he manages a portfolio of R&D programs that aim to dramatically increase the health span of Americans by increasing their capacity to avoid disease and the symptoms of disease for as long as possible. Wade comes to ARPA-H with extensive experience in academic R&D, startups and in public service.  In government, he served as Director of the National AI Initiative Office (NAIIO) at the White House in the Office of Science and Technology Policy (OSTP) and as program manager at DARPA where he ran a portfolio of AI/data science and hardware for AI programs.  Wade was cofounder of Actuate, a nonprofit organization dedicated to bringing the ARPA model of innovation to societal applications outside of national security.  Wade was also a researcher and manager at MIT’s Lincoln Laboratory, where he worked on and oversaw research in AI and natural language understanding.  Wade was co-founder and CTO of Vocentric Corporation, an embedded speech and speaker recognition startup.

Dr. Stuart is a physician scientist with over 20 years of experience in immunology, global health, and product development and acting Professor in Biochemistry at the University of Washington. As the Executive Director of the Institute for Protein Design (IPD), she oversees translational research, institute operations, and corporate and foundation collaborations. The IPD’s mission is to use computational and AI approaches to create proteins that solve modern challenges in medicine, technology, and sustainability. She also currently serves on the Scientific Advisory Committee of CEPI (Coalition for Epidemic Preparedness and Innovation) and is a member of the Scientific Technical Expert Group for the International Pandemic Preparedness Secretariat supporting the 100 Days Mission.

As Deputy Director for Vaccines & Biologics at the Gates Foundation from 2013 to 2022, she oversaw the pre-clinical development of vaccines, including mRNA vaccines, and antibody therapies to address urgent global health challenges. In the wake of Ebola she contributed to the conceptualization of “just-in-time” platform approaches for pandemic responses and from 2020-22 she led the Foundation’s COVID-19 discovery and translational vaccine response efforts, managing a large portfolio of COVID-19 and pan-coronavirus vaccine candidates including the IPD’s computationally designed COVID-19 vaccine SKYCovione and helped guided its development and approval. After leaving the Gates Foundation, Dr. Stuart spent one year as the Vice President of Infectious Disease at the mRNA company BioNTech.

Dr. Stuart received a BA from University of Cambridge, her medical degree from the University of London, and PhD from the University of Edinburgh. Between 2003 to 2013 she was faculty at Massachusetts General Hospital/Harvard Medical School and an affiliate of the Broad Institute of Harvard and MIT. She is a Fellow of the American Society for Clinical Investigation.

Dr. Georgia Tourassi is the Associate Laboratory Director of the Computing and Computational Sciences Directorate at the Oak Ridge National Laboratory (ORNL). Concurrently, she holds appointments as an Adjunct Professor of Radiology at Duke University and as a joint UT-ORNL Professor of the Bredesen Center Data Science Program at the University of Tennessee at Knoxville. Under her leadership, the Oak Ridge Leadership Computing Facility delivered Frontier in 2022, the world’s first exascale computing system dedicated to open science.

Her scholarly work includes 13 US patents and innovation disclosures, 2 R&D 100 awards, and more than 260 peer-reviewed journal articles, conference proceedings articles, editorials, and book chapters. She is an elected Fellow of the Institute of Electrical and Electronics Engineers (IEEE), the American Institute of Medical and Biological Engineering (AIMBE), the American Association of Medical Physicists (AAPM), the International Society for Optics and Photonics (SPIE), and the American Association for the Advancement of Science (AAAS).

Her research interests include high performance computing and artificial intelligence in biomedicine. For her leadership in the Joint Design of Advanced Computing Solutions for Cancer initiative, she received the DOE Secretary’s Appreciation Award in 2016. In 2017, she received the ORNL Director's Award for Outstanding Individual Accomplishment in Science and Technology and the UT-Battelle Distinguished Researcher Award. In 2020, Dr. Tourassi received the DOE’s Secretary Honors Award for contributing to the COVID-19 Insights Partnership Team and the COVID-19 HPC Resource Team.

Tourassi holds a B.S. in Physics from Aristotle University of Thessaloniki, Greece, and a Ph.D. in Biomedical Engineering from Duke University.