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Antibodies, crucial for immune defense, primarily rely on complementarity-determining regions (CDRs) to bind and neutralize antigens, such as viruses.
Introduction to Modern Statistical Mechanics
D. Chandler, D. Wu, and P.C.D. Chandler · 1987
Earlier work this paper cites.
An empirical bayes approach to statistics
Herbert E Robbins · 1992
Earlier work this paper cites.
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Earlier work this paper cites.
Annealed importance sampling
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Structure quality and target parameters , chapter 18.3, pages 474–484
R. A. Engh and R. Huber · 2012
Earlier work this paper cites.
Structural consensus among antibodies defines the antigen binding site
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Earlier work this paper cites.
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The h3 loop of antibodies shows unique structural characteristics
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Is it reliable to take the molecular docking top scoring position as the best solution without considering available structural data?
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John Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola · 2019
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