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Free energy methods for pharmaceutical drug discovery

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Machine learning and pKa prediction in SAMPL6

Check out these slides on Caitlin Bannan’s SAMPL6 blind challenge entry on pKa prediction! She spent her summer doing an internship at OpenEye building a machine learning model for pKa prediction, and entered it in the recent SAMPL6 blind prediction challenge. She just spoke today at the OpenEye CUP meeting in Santa Fe on the results, and these are her slides.

 

–  David L. Mobley

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Posted: March 7, 2018 · Tags: SAMPL, SAMPL6, pKa

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  • Dr. David L. Mobley
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    • Nathan M. Lim
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Recent Papers

Escaping Atom Types in Force Fields Using Direct Chemical Perception

David L. Mobley , Caitlin C. Bannan , Andrea … [Read More...]

Challenges in the use of atomistic simulations to predict solubilities of drug-like molecules

Guilherme Duarte Ramos Matos, David L. … [Read More...]

SAMPL6 challenge results from pKa predictions based on a general Gaussian process model

Caitlin C. Bannan, David L. Mobley, A. Geoffrey … [Read More...]

Open Force Field Consortium: Escaping atom types using direct chemical perception with SMIRNOFF v0.1

David Mobley, Caitlin C. Bannan, Andrea Rizzi, … [Read More...]

Binding modes of ligands using enhanced sampling (BLUES)

Samuel C. Gill, Nathan M. Lim, Patrick B. … [Read More...]

RSS What we’re reading:

  • Force field development phase II: Relaxation of physics-based criteria… or inclusion of more rigor February 20, 2019
  • Biomolecular force fields: where have we been, where are we now, where do we need to go and how do w February 20, 2019
  • Kirkwood-Buff analysis of aqueous N-methylacetamide and acetamide solutions modeled by the CHARMM ad February 19, 2019
  • Replica exchange and expanded ensemble simulations as Gibbs sampling: Simple improvements for enhanc February 19, 2019
  • Minnesota Solvation Database February 9, 2019

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Contact

David L. Mobley
dmobley@mobleylab.org
phone: 949.385.2436
office: 3134B Nat. Sci. I

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