Vijay S. Pande

Pande graduated from Langley High School's class of 1988 while growing up in McLean, Virginia. As a high school student, Pande was awarded fourth place in the 1988 Westinghouse Science Talent Search for his project on simulating space-based missile defense.^[2] His analysis showed that “to protect against 2,400 Soviet missiles, 8,000 satellites equipped with laser weapons would be required,” a critique of Ronald Reagan’s Strategic Defense Initiative.^[4][5][6]

He remarked in an interview at the time: “Newton was the greatest physicist, even greater than Einstein... he invented calculus to do it.”^[7][8]

In 1992, Pande received his B.A. in physics from Princeton University. He received a PhD in physics from MIT in 1995. His doctoral advisor was Toyoichi Tanaka, and his dissertation was titled Freezing Transition of Heteropolymers.^[9]

After graduating from high school in 1988, Pande worked briefly at the video game development company Naughty Dog in the early 1990s while in his late teens, serving as a programmer and designer on their 1991 release Rings of Power.^[10][11] While Pande was attending MIT and Naughty Dog was based in Boston, he portrayed the secret boss character in the 3DO fighting game Way of the Warrior.^[12]

He is married to Lara Pande and has three daughters and likes cats.^[1][13]

Pande is an adjunct professor of structural biology, computer science, biophysics and chemistry at Stanford University. Previously, he was the Henry Dreyfus Professor of Chemistry and professor of structural biology and of computer science. He was also director of the biophysics program.^[14]

At Stanford, Pande’s research lab made pioneering contributions to the simulation of biomolecular kinetics and thermodynamics, particularly in protein folding.^[15] His development of distributed simulation algorithms enabled modeling of biomolecular systems at atomic resolution, described in press coverage as offering “a way to do in weeks what used to take 30 years” using traditional computing methods.^[1]

In 2014, researchers at Stanford and Google, including Pande, collaborated on an "unprecedented, atom-scale simulation of a receptor site's transformation".^[16]

In 2015, Pande became the ninth general partner at Andreessen Horowitz where he leads the firm's investments in companies at the cross section of biology and computer science. He is the founding investor of their a16z Bio + Health Fund^[17] which invests in life sciences and healthcare valued more than $3 billion under management.^[18] In December 2024, he left the leadership role and moved to an AI role in the company.^[19]

Pande serves on the boards of Apeel Sciences, Bayesian Health, BioAge Labs, Citizen, Devoted Health, Freenome, Insitro, Nautilus Biotechnology, Nobell, Omada Health, Q.bio, Function Health Board, Slingshot AI and Scribe Therapeutics, a CRISPR company co-founded by 2020 Nobel Laureate Jennifer Doudna. He has also been a founder and advisor to startups in Silicon Valley.^[20]

Pande has written for Time,^[21] STAT News,^[22] Fortune,^[23] Scientific American and the New York Times,^[24] among others.

Pande has articulated a vision of using computer simulations to model biological processes at previously unachievable scales. In a Stanford-led collaboration, he co-developed algorithms that simulated 2.5 milliseconds of GPCR receptor motion, described as a “virtual eternity in chemical-reaction time”, capturing every viable intermediate structure between "on" and "off" states of the receptor. These intermediate states were used to predict drug binding efficiency.^[38]

"With the right method and algorithms, you can do the same quality of work with cloud resources," Pande said, advocating for commodity hardware over specialized infrastructure”

He envisions pushing atomistic simulations to the scale of whole cells, aiming to decipher cancer processes where normal proteins behave abnormally.

"We will need to push the boundaries of both computers and algorithms," he stated, “but the conceptual steps are there”

• In 2002, he was named a Frederick E. Terman Fellow and was recognized as one of the top 100 innovators under 35 by MIT Technology Review's TR100. The following year, he was awarded the Henry and Camille Dreyfus Teacher-Scholar award.^[3]
• In 2004, he received a Technovator award from Global Indus Technovators in its Biotech/Med/Healthcare category.^[39]
• In 2006, Pande was awarded the Irving Sigal Young Investigator Award from the Protein Society.
• In 2008, he was named "Netxplorateur of 2008".^[39] Also in 2008 he was given the Thomas Kuhn Paradigm Shift Award and received the Fellowship of the American Physical Society.^[2]
• Pande received the 2012 Michael and Kate Bárány Award for developing computational models for proteins and RNA.^[2][39]
• In 2015, Pande received the DeLano Award for Computational Biosciences, as well as the Camille and Henry Dreyfus Distinguished Chair in Chemistry.^[40][41]

Vijay Pande has authored or co-authored over 300 peer-reviewed scientific papers spanning computational chemistry, biophysics, molecular dynamics, and machine learning in biology. The following is a selection of his notable publications:

• Feinberg, E. N., Sur, D., Wu, Z., Husic, B. E., Mai, H., Li, Y., ... & Pande, V. S. (2018). "PotentialNet for molecular property prediction." ACS Central Science, 4(11), 1520–1530. This paper introduced a novel graph convolutional neural network (GNN) framework—PotentialNet—for predicting molecular properties such as protein-ligand binding affinities. The method demonstrated superior performance in pharmaceutical applications and was adopted by companies including Merck.

• Feinberg, E. N., Tseng, H., Husic, B. E., Mai, H., Li, Y., Cox, S., ... & Pande, V. S. (2020). "Improved ADMET property prediction with graph convolutional networks." Journal of Medicinal Chemistry, 63(16), 8835–8848. Building on PotentialNet, this work achieved what the authors described as "unprecedented accuracy" in predicting ADMET (absorption, distribution, metabolism, elimination, and toxicity) characteristics, outperforming traditional machine learning approaches such as Random Forest models.

• Shirts, M., & Pande, V. S. (2000). "Computing: Screen savers of the world unite!" Science, 290(5498), 1903–1904. This perspective article laid the theoretical groundwork for large-scale distributed computing in molecular biology, proposing the use of consumer-grade hardware to divide massive computational tasks—a vision later realized in Folding@home.

• Bowman, G. R., Voelz, V. A., & Pande, V. S. (2011). "Taming the complexity of protein folding." Current Opinion in Structural Biology, 21(1), 4–11. This review summarized advances in Markov state models (MSMs) for simulating long-timescale protein folding. Pande’s group was among the first to demonstrate how MSMs could extract biologically relevant insights from large-scale molecular simulations.

• Dror, R. O., Dirks, R. M., Grossman, J. P., Xu, H., Shaw, D. E., & Pande, V. S. (2012). "Biomolecular simulation: A computational microscope for molecular biology." Annual Review of Biophysics, 41, 429–452. Co-authored with researchers at D. E. Shaw Research, this article examined how molecular simulations are transforming structural biology by enabling the modeling of dynamic processes such as receptor-ligand binding and allosteric transitions.

• Kohlhoff, K. J., Shukla, D., Lawrenz, M., Bowman, G. R., Konerding, D. E., Belov, D., ... & Pande, V. S. (2014). "Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways." Nature Chemistry, 6(1), 15–21. As part of the Stanford/NIH/Google Exacycle project, this work reported one of the first atomistic simulations of G protein-coupled receptor (GPCR) activation over 2 milliseconds—described as "a virtual eternity in chemical reaction time"—revealing intermediate conformational states critical for rational drug design.