Zhang, Si, Miller, Justin J, and Bowman, Gregory R. How Well Can AI and Physics-Based Simulations Predict the Probability a Cryptic Pocket Is Open?. Retrieved from https://par.nsf.gov/biblio/10710820. Journal of Chemical Theory and Computation 22.8 Web. doi:10.1021/acs.jctc.6c00135.

Zhang, Si, Miller, Justin J, & Bowman, Gregory R. How Well Can AI and Physics-Based Simulations Predict the Probability a Cryptic Pocket Is Open?. Journal of Chemical Theory and Computation, 22 (8). Retrieved from https://par.nsf.gov/biblio/10710820. https://doi.org/10.1021/acs.jctc.6c00135

Zhang, Si, Miller, Justin J, and Bowman, Gregory R. "How Well Can AI and Physics-Based Simulations Predict the Probability a Cryptic Pocket Is Open?". Journal of Chemical Theory and Computation 22 (8). Country unknown/Code not available: Journal of Chemical Theory and Computation. https://doi.org/10.1021/acs.jctc.6c00135. https://par.nsf.gov/biblio/10710820.

@article{osti_10710820,
place = {Country unknown/Code not available}, title = {How Well Can AI and Physics-Based Simulations Predict the Probability a Cryptic Pocket Is Open?}, url = {https://par.nsf.gov/biblio/10710820}, DOI = {10.1021/acs.jctc.6c00135}, abstractNote = {Not Available}, journal = {Journal of Chemical Theory and Computation}, volume = {22}, number = {8}, publisher = {Journal of Chemical Theory and Computation}, author = {Zhang, Si and Miller, Justin J and Bowman, Gregory R}, }