Hope for Improvement in Predictive Modeling

July 18, 2017

A fresh approach to predictive modeling may just improve the process exponentially. Phys.org reports, “Molecular Dynamics, Machine Learning Create ‘Hyper-Predictive Computer Models.” The insight arose, and is being tested, at North Carolina State University.

The article begins by describing the incredibly complex and costly process of drug development, including computer models that predict the effects of certain chemical compounds. Such models traditionally rely on QSAR modeling and molecular docking. We learn:

Denis Fourches, assistant professor of computational chemistry, wanted to improve upon the accuracy of these QSAR models. … Fourches and Jeremy Ash, a graduate student in bioinformatics, decided to incorporate the results of molecular dynamics calculations – all-atom simulations of how a particular compound moves in the binding pocket of a protein – into prediction models based on machine learning. ‘Most models only use the two-dimensional structures of molecules,’ Fourches says. ‘But in reality, chemicals are complex three-dimensional objects that move, vibrate and have dynamic intermolecular interactions with the protein once docked in its binding site. You cannot see that if you just look at the 2-D or 3-D structure of a given molecule.’

See the article for some details about the team’s proof-of-concept study. Fourches asserts the breakthrough delivers a simulation that would previously have been built over six months in a mere three hours. That is quite an improvement! If this technique pans out, we could soon see more rapid prediction not only in pharmaceuticals but many other areas as well. Stay tuned.

Cynthia Murrell, July 18, 2017


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