In this episode of Intel on AI host Amir Khosrowshahi talks with Ron Dror about breakthroughs in computational biology and molecular simulation.

Ron is an Associate Professor of Computer Science in the Stanford Artificial Intelligence Lab, leading a research group that uses machine learning and molecular simulation to elucidate biomolecular structure, dynamics, and function, and to guide the development of more effective medicines. Previously, Ron worked on the Anton supercomputer at D.E. Shaw Research after earning degrees in the fields of electrical engineering, computer science, biological sciences, and mathematics from MIT, Cambridge, and Rice. His groundbreaking research has been published in journals such as Science and Nature, presented at conferences like Neural Information Processing Systems (NeurIPS), and won awards from the Association of Computing Machinery (ACM) and other organizations.

In the podcast episode, Ron talks about his work with several important collaborators, his interdisciplinary approach to research, and how molecular modeling has improved over the years. He goes into detail about the gen-over-gen advancements made in the Anton supercomputer, including its software, and his recent work at Stanford with molecular dynamics simulations and machine learning. The podcast closes with Amir asking detailed questions about Ron and his team’s recent paper concerning RNA structure prediction that was featured on the cover of Science.

Academic research discussed in the podcast episode:

• Statistics of real-world illumination
• The Role of Natural Image Statistics in Biological Motion Estimation
• Surface reflectance recognition and real-world illumination statistics
• Accuracy of velocity estimation by Reichardt correlators
• Principles of Neural Design
• Levinthal's paradox
• Potassium channels
• Structural and Thermodynamic Properties of Selective Ion Binding in a K+ Channel
• Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters
• Long-timescale molecular dynamics simulations of protein structure and function
• Parallel random numbers: as easy as 1, 2, 3
• Biomolecular Simulation: A Computational Microscope for Molecular Biology
• Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer
• Molecular Dynamics Simulation for All
• Structural basis for nucleotide exchange in heterotrimeric G proteins
• How GPCR Phosphorylation Patterns Orchestrate Arrestin-Mediated Signaling
• Highly accurate protein structure prediction with AlphaFold
• ATOM3D: Tasks on Molecules in Three Dimensions
• Geometric deep learning of RNA structure