D
David L. Mobley
Researcher at University of California, Irvine
Publications - 199
Citations - 12960
David L. Mobley is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Solvation & Medicine. The author has an hindex of 51, co-authored 177 publications receiving 10569 citations. Previous affiliations of David L. Mobley include University of California & NewYork–Presbyterian Hospital.
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Overview of the SAMPL6 pKa Challenge: Evaluating small molecule microscopic and macroscopic pKa predictions
Mehtap Işık,Mehtap Işık,Ariën S. Rustenburg,Ariën S. Rustenburg,Andrea Rizzi,Andrea Rizzi,Marilyn R. Gunner,David L. Mobley,John D. Chodera +8 more
TL;DR: The SAMPL6 pKa Challenge demonstrated the need for improving pKa prediction methods for drug-like molecules, especially for challenging moieties and multiprotic molecules with significant inaccuracies observed in prior physical property prediction challenges.
Journal ArticleDOI
Correction to Small Molecule Hydration Free Energies in Explicit Solvent: An Extensive Test of Fixed-Charge Atomistic Simulations.
TL;DR: None of the conclusions in the original study need revision, but the set of calculated and experimental hydration free energies provided in the Supporting Information is now obsolete and is replaced by FreeSolv.
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Improving small molecule force fields by identifying and characterizing small molecules with inconsistent parameters.
TL;DR: In this paper, the authors present a pipeline for comparing the geometries of small molecule conformers and identify molecules or chemistries that are particularly informative for future force field development because they display inconsistencies between force fields.
Advancing predictive modeling through focused development of model systems to drive new modeling innovations
TL;DR: The research proposed here will lead to significant improvements in the predictive power of physical models for drug discovery, molecular design and the prediction of physical properties.
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Challenges in the use of atomistic simulations to predict solubilities of drug-like molecules.
TL;DR: Solubility prediction of drug-like solids remains computationally challenging, and it appears that both the underlying energy model and the computational approach applied may need improvement before the approach is suitable for routine use.