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"... 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. ...
The “BAR” in DeepBAR stands for “Bennett acceptance ratio,” a decades-old algorithm used in exact calculations of binding free energy. Using the Bennet acceptance ratio typically requires a knowledge of two “endpoint” states (e.g., a drug molecule bound to a protein and a drug molecule completely dissociated from a protein), plus knowledge of many intermediate states (e.g., varying levels of partial binding) ...
The affinity between a drug molecule and a target protein is measured by a quantity called the binding free energy ...
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. ...The “BAR” in DeepBAR stands for “Bennett acceptance ratio,” a decades-old algorithm used in exact calculations of binding free energy. Using the Bennet acceptance ratio typically requires a knowledge of two “endpoint” states (e.g., a drug molecule bound to a protein and a drug molecule completely dissociated from a protein), plus knowledge of many intermediate states (e.g., varying levels of partial binding) ...
DeepBAR slashes those in-between states by deploying the Bennett acceptance ratio in machine-learning frameworks called deep generative models. “These models create a reference state for each endpoint, the bound state and the unbound state,” ... These two reference states are similar enough that the Bennett acceptance ratio can be used directly, without all the costly intermediate steps. ...
“We’re sort of treating each molecular structure as an image, which the model can learn. ... “But here we have proteins and molecules — it’s really a 3D structure. So, adapting those methods in our case was the biggest technical challenge we had to overcome.” ..."
“We’re sort of treating each molecular structure as an image, which the model can learn. ... “But here we have proteins and molecules — it’s really a 3D structure. So, adapting those methods in our case was the biggest technical challenge we had to overcome.” ..."
Here is the link to the underlying research paper:
DeepBAR: A Fast and Exact Method for Binding Free Energy Computation (behind paywall)
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