Our science | Mobley Lab, UCI

The Open Force Field Consortium launches; check out a webinar

We’re very excited about the Open Force Field Initiative, a new effort to help build better force fields and improve the infrastructure for developing them. And we’re especially excited that a pharmaceutical industry consortium has come together to help fund and guide this work. Check out our announcement for more details!

If you’d like to know more about our longer-term plans, check out graduate student Caitlin Bannan’s upcoming BioExcel Webinar Oct. [More…] “The Open Force Field Consortium launches; check out a webinar”

Papers, Our science

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

Guilherme Duarte Ramos Matos, David L. Mobley

Background: Solubility is a physical property of high importance to the pharmaceutical industry, the prediction of which for potential drugs has so far been a hard task. We attempted to predict the solubility of acetylsalicylic acid (ASA) by estimating the absolute chemical potentials of its most stable polymorph and of solutions with different concentrations of the drug molecule.

[More…]

Papers, Our science

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

Caitlin C. Bannan, David L. Mobley, A. Geoffrey Skillman

A variety of fields would benefit from accurate pK_a predictions, especially drug design due to the affect a change in ionization state can have on a molecules physiochemical properties. Participants in the recent SAMPL6 blind challenge were asked to submit predictions for microscopic and macroscopic pK_as of 24 drug like small molecules. [More…]

Papers, Our science

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

David Mobley, Caitlin C. Bannan, Andrea Rizzi, Christopher I. Bayly, John D. Chodera, Victoria T Lim, Nathan M. Lim, Kyle A. Beauchamp, Michael R. Shirts, Michael K. Gilson, and Peter K. Eastman

Here, we focus on testing and improving force fields for molecular modeling, which see widespread use in diverse areas of computational chemistry and biomolecular simulation. [More…] “Open Force Field Consortium: Escaping atom types using direct chemical perception with SMIRNOFF v0.1”

Our science, Talks and posters

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. [More…] “Machine learning and pKa prediction in SAMPL6”

The Open Force Field Consortium launches; check out a webinar

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

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

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

Machine learning and pKa prediction in SAMPL6

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