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Chinwe Ekenna, Shawna Thomas, Nancy Amato, "Adaptive Local Learning in Sampling Based Motion Planning for Protein Folding," Bio Med Central Systems Biology, 10(2):165--179, Aug 2016.
Journal(pdf, abstract)

Simulating protein folding motions is an important problem in computational biology. Motion planning algorithms, such as Probabilistic Roadmap Methods, have been successful in modeling the folding landscape. Probabilistic Roadmap Methods and variants contain several phases (i.e., sampling, connection, and path extraction). Most of the time is spent in the connection phase and selecting which variant to employ is a difficult task. Global machine learning has been applied to the connection phase but is inefficient in situations with varying topology, such as those typical of folding landscapes.