Michael Levitt

Michael Levitt
Born	9 5, 1947
Birthplace	Pretoria, South Africa
Nationality	American, British, Israeli
Occupation	Biophysicist, computational biologist, academic
Employer	Stanford University
Known for	Development of multiscale models for complex chemical systems; Nobel Prize in Chemistry (2013)
Education	Ph.D., University of Cambridge
Awards	Nobel Prize in Chemistry (2013)

Michael Levitt (born 9 May 1947) is a South African-born American-British-Israeli biophysicist and professor of structural biology at Stanford University School of Medicine. He was awarded the Nobel Prize in Chemistry in 2013, jointly with Martin Karplus and Arieh Warshel, for the development of multiscale models for complex chemical systems. Levitt's work has been foundational in the field of computational biology, where he pioneered methods to simulate and predict the structures and behaviors of biological molecules. His research contributions span molecular dynamics, protein structure prediction, and the development of computational tools that have become standard in structural biology and drug design. Over a career extending more than five decades, Levitt has held positions at leading research institutions across multiple countries, reflecting his trilateral citizenship and international scientific outlook. In February 2026, Levitt was appointed to the Scientific Advisory Board of Human Longevity, Inc., a precision health and longevity medicine company, further extending his influence into translational science and health technology.

Early Life

Michael Levitt was born on 9 May 1947 in Pretoria, South Africa. He grew up in South Africa during the era of apartheid, and his family was part of the country's Jewish community. Levitt showed an early aptitude for science and mathematics. He later moved to the United Kingdom, where he pursued his higher education and began the scientific career that would eventually take him to institutions in Israel and the United States. Levitt holds citizenship in three countries — the United States, the United Kingdom, and Israel — a reflection of the international trajectory of his life and career. His multicultural background and experiences across continents have been noted in interviews as informing his collaborative and boundary-crossing approach to scientific research.

Education

Levitt pursued his undergraduate studies at King's College London, where he earned a Bachelor of Science degree in physics. He then moved to the University of Cambridge, where he completed his Ph.D. at the Laboratory of Molecular Biology (LMB), one of the most distinguished centers for molecular biology in the world. At Cambridge, Levitt began his groundbreaking work on computational approaches to understanding biological molecules, laying the intellectual foundation for the multiscale modeling techniques that would later earn him the Nobel Prize. His time at the LMB was formative, placing him in proximity to several other Nobel laureates and at the forefront of the emerging discipline of computational structural biology.

Career

Early Research and Molecular Dynamics

After completing his doctorate, Levitt undertook postdoctoral research at several institutions, including the Weizmann Institute of Science in Rehovot, Israel. During the early 1970s, Levitt collaborated with Arieh Warshel on pioneering computational methods to simulate the behavior of biological molecules. Their work was among the earliest efforts to apply computer simulations to the study of enzymes and proteins, developing techniques that combined quantum mechanical and classical mechanical models. These methods, known as QM/MM (quantum mechanics/molecular mechanics), allowed researchers to study chemical reactions within large biological molecules by treating the reactive region with quantum mechanics while modeling the surrounding molecular environment with classical physics. This approach was revolutionary, as it enabled the simulation of molecular processes that were otherwise impossible to study using existing experimental or theoretical techniques alone.

Levitt also made significant contributions to the field of molecular dynamics, developing algorithms and software tools for simulating the physical movements of atoms and molecules over time. His early programs for energy minimization and molecular dynamics simulation became foundational tools in computational chemistry and biology.

Protein Structure Prediction

A major thread of Levitt's research throughout his career has been the prediction of protein structure from amino acid sequences. Protein folding — the process by which a linear chain of amino acids assumes its functional three-dimensional shape — is one of the central problems in molecular biology. Levitt developed computational approaches to model and predict protein structures, contributing to the understanding of how proteins fold and function. His methods combined physical energy functions with simplified representations of protein structures, enabling the simulation of folding processes on computers with limited processing power at the time.

Levitt was an early and active participant in the Critical Assessment of protein Structure Prediction (CASP) experiments, a community-wide initiative to benchmark and advance methods for protein structure prediction. His sustained engagement with this problem over several decades helped establish computational prediction as a complement to experimental techniques such as X-ray crystallography and NMR spectroscopy.

Stanford University

Levitt joined the faculty of Stanford University School of Medicine as a professor of structural biology. At Stanford, he built and led a research group focused on computational biology, training numerous graduate students and postdoctoral researchers who have gone on to careers in academia, industry, and government. His laboratory at Stanford continued to develop new computational methods for simulating molecular systems, analyzing genomic data, and predicting molecular structures. The Stanford position provided Levitt with a base to collaborate with experimentalists in the university's extensive network of biomedical research programs.

As a professor at Stanford, Levitt is identified as a computational biologist whose work encompasses the simulation and prediction of biological molecular structures and dynamics.

Nobel Prize in Chemistry (2013)

In 2013, the Royal Swedish Academy of Sciences awarded the Nobel Prize in Chemistry to Michael Levitt, Martin Karplus, and Arieh Warshel "for the development of multiscale models for complex chemical systems." The Nobel Committee recognized the trio's work in the 1970s as having laid the groundwork for modern computational chemistry. Their methods enabled scientists to use computers to simulate and understand chemical processes — from enzyme catalysis to drug interactions — at an atomic level of detail.

The prize specifically cited the development of methods that bridged quantum mechanical and classical mechanical descriptions of molecular systems. By combining these two theoretical frameworks, Levitt and his colleagues made it possible to study large biological molecules with unprecedented accuracy and efficiency. The Nobel Committee noted that these computational approaches had become indispensable tools in modern chemistry, used in applications ranging from pharmaceutical development to materials science.

Levitt's share of the prize recognized his contributions to the development of both the theoretical foundations and the computational software necessary for these simulations. His work on simplified models of protein folding and on methods for molecular dynamics simulation was highlighted as central to the field's development.

Human Longevity, Inc.

In February 2026, Human Longevity, Inc. (HLI), described as a global leader in precision health and longevity medicine, announced that Levitt had joined its Scientific Advisory Board. According to HLI's announcement, the appointment reflected the company's commitment to integrating advanced computational and biological expertise into its precision health platform. GenomeWeb reported that Levitt was appointed as a computational biologist and that he continued to serve as a professor at Stanford at the time of the appointment.

The appointment placed Levitt at the intersection of academic computational biology and commercial applications in health and longevity, a growing sector of the biotechnology industry. Human Longevity, Inc. utilizes large-scale genomic and health data to develop personalized medicine and longevity programs. Levitt's expertise in computational modeling and simulation of biological systems was cited as a strategic addition to HLI's advisory structure.

COVID-19 Pandemic Commentary

During the COVID-19 pandemic, Levitt attracted public attention for his analysis and commentary on the trajectory of the pandemic. Using statistical methods and data analysis, he offered predictions about infection and mortality curves in various countries. Levitt's early projections, made in the first months of 2020, suggested that the initial growth rates of the virus would slow sooner than many epidemiological models predicted. Some of his forecasts were later borne out, while others were subject to debate within the scientific community. His public commentary during the pandemic drew both praise for offering a quantitative perspective and criticism from some epidemiologists and public health experts who disagreed with aspects of his analysis.

Levitt's engagement with COVID-19 data highlighted the broader role that computational scientists and Nobel laureates can play — and the controversies that may arise — when applying their expertise outside their primary field of research.

Personal Life

Michael Levitt holds citizenship in three countries: the United States, the United Kingdom, and Israel. He has lived and worked in all three countries during his career. Levitt is married and has children. He has spoken publicly about his Jewish heritage and his connection to Israel, where he spent significant periods of his career at the Weizmann Institute of Science.

Levitt has been described as an advocate for the free exchange of scientific ideas across national boundaries and has participated in international scientific collaborations throughout his career. He has also been vocal about the role of computational methods in advancing biological understanding and has engaged with the public on scientific issues, including through media appearances and public lectures.

Recognition

Michael Levitt's most prominent honor is the Nobel Prize in Chemistry, awarded in 2013 jointly with Martin Karplus and Arieh Warshel. The prize recognized the trio's collective work on the development of multiscale models for complex chemical systems, a body of research that transformed computational chemistry and biology.

In addition to the Nobel Prize, Levitt has received numerous other awards and distinctions over his career. He has been elected as a member or fellow of several national academies of science, including the National Academy of Sciences of the United States, the Royal Society of the United Kingdom, and the Israel Academy of Sciences and Humanities. These memberships reflect the international scope of his contributions and the recognition he has received across the scientific communities of the three countries in which he holds citizenship.

Levitt's work has been recognized through additional prizes in computational chemistry and biology, and he has received honorary degrees from universities around the world. His research publications have been cited thousands of times in the scientific literature, underscoring the foundational nature of his contributions to the field.

The 2026 appointment to the Scientific Advisory Board of Human Longevity, Inc. further reflected the ongoing demand for Levitt's expertise in both academic and commercial contexts. In announcing his appointment, HLI described Levitt as a "renowned" Nobel Laureate, highlighting the prestige his involvement brought to the company's advisory structure.

Legacy

Michael Levitt's legacy is defined by his role in establishing computational methods as essential tools in chemistry and biology. The multiscale modeling techniques he co-developed in the 1970s — particularly the QM/MM approach — have become standard methodologies used in academic and industrial research worldwide. These methods are routinely applied in pharmaceutical drug design, where the ability to simulate molecular interactions at the atomic level accelerates the identification of potential drug candidates and reduces the cost and time required for development.

Levitt's contributions to protein structure prediction have also had a lasting impact. His work helped demonstrate that computational methods could complement — and in some cases substitute for — experimental techniques in determining the three-dimensional structures of proteins. This line of research has continued to evolve, with modern machine learning approaches such as AlphaFold building upon the foundations that Levitt and other computational biologists established.

As a mentor and academic leader at Stanford, Levitt has trained a generation of computational biologists and biophysicists. Many of his former students and postdoctoral researchers occupy prominent positions in universities, research institutes, and biotechnology companies around the world, extending the influence of his scientific approach and methods.

Levitt's career also illustrates the increasingly international nature of modern science. His triple citizenship and career spanning institutions in South Africa, the United Kingdom, Israel, and the United States exemplify the global mobility of leading scientists and the cross-pollination of ideas that results from international collaboration. His Nobel Prize, shared with a colleague born in Austria (Karplus) and another born in Israel (Warshel), further underscores the collaborative and international character of contemporary scientific achievement.

His 2026 appointment to the Scientific Advisory Board of Human Longevity, Inc. signals the continued relevance of his expertise in an era of precision medicine and data-driven health interventions, bridging the gap between fundamental computational biology and its practical application in human health.