Title: All for one and one for all: unsupervised learning techniques in molecular simulations
Abstract:
Quantifying similarity is a central notion in science and data analysis, pervading everything from phylogenetic trees to the foundation of clustering. Unfortunately, despite being examined and applied for decades, traditional similarity and distance metrics have fundamental drawbacks. The key problem is that all of them are only defined over pairs of objects, so they scale quadratically when one tries to compare N objects. The present explosion in the amount of data available to us requires new ways to process information, and while some current algorithms can handle millions of points, we need alternatives applicable to billions. This is what motivated us to develop a new framework that can compare any number of objects at the same time. With this, we achieve an unprecedented linear scaling when comparing multiple objects. Here we will discuss the main properties of this formalism, along with its applications in drug design and to the analysis of Molecular Dynamics (MD) simulations. Our indices have proven to be incredibly versatile when applied to chemical space exploration and visualization, allowing us to rigorously quantify the chemical diversity of very large molecular libraries. This has led to the creation of several algorithms to sample important regions in chemical space, including a more efficient way of identifying the prevalence of activity cliffs. Additionally, our indices provide a convenient route to sample complex MD trajectories, allowing to identify representative structures very efficiently. Moreover, we can also cluster biological ensembles in a more robust way than with standard algorithms, which has led to our group’s work on MDANCE, a very flexible and efficient open-source clustering module.
Bio:
Ramon Alain Miranda-Quintana majored in Radiochemistry in the Higher Institute of Technologies and Applied Sciences in 2011 and obtained his Ph.D. in Chemistry from the University of Havana. After a research appointment at McMaster University, he won a York Science Fellowship to work in York University as a Postdoctoral Scholar (where he won the 2019 Polanyi Prize in Chemistry). He then joined the Department of Chemistry at the University of Florida as an Assistant Professor in 2020, where he is also a member of the Quantum Theory Project. His research interests include the development of ab initio electronic structure methods to study strongly correlated systems, understanding how charge and spin transfer processes shape chemical reactivity and solvation processes, and developing efficient similarity-based tools for data science applications in chemistry and biomedical sciences. At UF he was won an Oak Ridge Ralph E. Powe Junior Faculty Enhancement Award, the OpenEye Cadence Molecular Sciences Outstanding Junior Faculty Award in Computational Chemistry, and R35 and CAREER grants.
Keywords: Pharmacy, Chemical engineering
Host: Prof. Xuhui Huang