Richard A. Friesner

Columbia University

Primary Section: 14, Chemistry
Secondary Section: 29, Biophysics and Computational Biology
Membership Type:
Member (elected 2016)


Richard A. Friesner is a computational chemist whose work is focused on the development of methods for quantum chemistry, quantum dynamics, biomolecular simulation, and structure based drug discovery. Key innovative software programs from his laboratory include Jaguar (quantum chemistry), Glide (protein-ligand docking), WaterMap (elucidation of active site water structure), OPLS3 (force field), and FEP/REST (protein-ligand binding affinity), all of which are widely used in the pharmaceutical industry. Applications studies include enzymatic catalysis in metalloenzymes such as methane monooxygenase, electron transfer and migration in solar energy conversion systems, and investigation of a wide range of protein-ligand complexes. He received his BS degree in chemistry from the University of Chicago in 1973, and obtained his PhD in 1979 at the University of California, Berkeley. He was then a postdoctoral fellow at the Massachusetts Institute of Technology from 1979-1982. He joined the Chemistry Department at the University of Texas at Austin in 1982 as an Assistant Professor, and in 1990, he became Professor of Chemistry at Columbia University, where he is currently the William P. Schweitzer Professor of Chemistry. He is a Fellow of the American Academy of Arts and Sciences and a member of the National Academy of Sciences.

Research Interests

My research is focused on developing improved computational methods for quantum chemistry, biomolecular simulation, and structure based drug design. In the area of quantum chemistry, we are pursuing improvements in density functional theory, as well as highly correlated wavefunction based approaches. We are applying these methods to modeling of the electronic properties of interesting electronic materials such as titanium dioxide, and to a wide range of transition metal complexes. Our work in biomolecular simulation utilizes both continuum solvent based models and all atom, explicit solvent molecular dynamics. We have two principal long term objectives in this work. The first is to be able to computationally refine protein structures, and the structures of protein-ligand complexes, to high resolution. Low resolution structures can often be generated by homology modeling or approximate docking methods; achieving high resolution is much more difficult. Increasing computing capacity, combined with advances in computational models and sampling algorithms, are enabling this problem to be profitably tackled. The second objective is the development of a hierarchy of approaches for predicting protein-ligand binding affinities, using methods ranging from empirical scoring functions to free energy perturbation theory. Both of these capabilities are crucial in enabling computational methods to be effectively used in structure based drug discovery projects.

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