Richard A. Friesner
- A hierarchical approach to all‐atom protein loop prediction
2004/03/05 by Matthew P. Jacobson, David L. Pincus, Chaya S. Rapp +5 · 20 citations
Biochemistry, Genetics and Molecular Biology · Materials Science · #Protein Structure and Dynamics #Enzyme Structure and Function #RNA and protein synthesis mechanisms
- Jaguar: A high‐performance quantum chemistry software program with strengths in life and materials sciences
2013/07/04 by Arteum D. Bochevarov, Art D. Bochevarov, Edward Harder +8 · 35 citations
Computer Science · Chemistry · Biochemistry, Genetics and Molecular Biology · #Computational Drug Discovery Methods #Free Radicals and Antioxidants #Protein Structure and Dynamics
- OPLS4: Improving Force Field Accuracy on Challenging Regimes of Chemical Space
2021/06/07 by Chao Lü, Chuanjie Wu, Delaram Ghoreishi +10 · 31 citations
Biochemistry, Genetics and Molecular Biology · Chemistry · Materials Science · #Machine Learning in Materials Science #Mass Spectrometry Techniques and Applications #Protein Structure and Dynamics
- A Multiple-Time-Step Molecular Dynamics Algorithm for Macromolecules
1994/07/01 by Darryl D. Humphreys, Richard A. Friesner, Bruce J. Berne +1 · 29 citations
Physics and Astronomy · Chemistry · #Spectroscopy and Quantum Chemical Studies #Photochemistry and Electron Transfer Studies #Various Chemistry Research Topics
- Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation
2023/12/15 by Dilek Coskun, Muyun Lihan, João Rodrigues +4 · 1 voice · 3 citations
Biochemistry, Genetics and Molecular Biology · #Receptor Mechanisms and Signaling #Chemical Synthesis and Analysis #Protein Structure and Dynamics
- Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties
2025/05/09 by John L. Weber, Rishabh D. Guha, Weber, John L. +22 · 4 citations
Chemistry · Materials Science · #Block Copolymer Self-Assembly #Crystallography and molecular interactions #Machine Learning in Materials Science