A Reinforcement Learning Approach to Protein Loop Modeling
Résumé
Modeling the loop regions of proteins is an active area of research due to their significance in defining how the protein interacts with other molecular partners. The high structural flexibility of loops presents formidable challenges for both experimental and computational approaches. In this work, we combine a robotics approach with reinforcement learning (RL) to compute an ensemble of loop configurations. We are actively performing experiments on well known benchmark sets to illustrate how RL improves the efficiency and effectiveness of our approach.
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MolloyCortes_IROS15_Abstract.pdf (119.21 Ko)
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MolloyCortes_IROS15_Poster_final.pdf (26.49 Mo)
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