Drugs can only work if they stick to their target proteins in the body. Assessing that stickiness is a key hurdle in the drug discovery and screening process. New research combining chemistry and machine learning could lower that hurdle.
The new technique, dubbed DeepBAR, quickly calculates the binding affinities between drug candidates and their targets. The approach yields precise calculations in a fraction of the time compared to previous state-of-the-art methods. The researchers say DeepBAR could one day quicken the pace of drug discovery and protein engineering.
“Our method is orders of magnitude faster than before, meaning we can have drug discovery that is both efficient and reliable,” says Bin Zhang, the Pfizer-Laubach Career Development Professor in Chemistry at MIT, an associate member of the Broad Institute of MIT and Harvard, and a co-author of a new paper describing the technique.
The research appears today in the Journal of Physical Chemistry Letters. The study’s lead author is Xinqiang Ding, a postdoc in MIT’s Department of Chemistry.
The affinity between a drug molecule and a target protein is measured by a quantity called the binding free energy — the smaller the number, the stickier the bind. “A lower binding free energy means the drug can better compete against other molecules,” says Zhang, “meaning it can more effectively disrupt the protein’s normal function.” Calculating the binding free energy of a drug candidate provides an indicator of a drug’s potential effectiveness. But it’s a difficult quantity to nail down.
Methods for computing binding free energy fall into two broad categories, each with its own drawbacks. One category calculates the quantity exactly, eating up significant time and computer resources. The second category is less computationally expensive, but it yields only an approximation of the binding free energy. Zhang and Ding devised an approach to get the best of both worlds.
Exact and efficient
DeepBAR computes binding free energy exactly, but it requires
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