scholarly journals Multipole electrostatics in hydration free energy calculations

2010 ◽  
Vol 32 (5) ◽  
pp. 967-977 ◽  
Author(s):  
Yue Shi ◽  
Chuanjie Wu ◽  
Jay W. Ponder ◽  
Pengyu Ren
Keyword(s):  
Free Energy ◽  
Free Energy Calculations ◽  
Hydration Free Energy ◽  
Energy Calculations

A hybrid quantum mechanical molecular mechanical method: Application to hydration free energy calculations

2002 ◽  
Vol 117 (6) ◽  
pp. 2762-2770 ◽  
Author(s):  
Tamer Shoeib ◽  
Giuseppe D. Ruggiero ◽  
K. W. Michael Siu ◽  
Alan C. Hopkinson ◽  
Ian H. Williams
Keyword(s):  
Free Energy ◽  
Free Energy Calculations ◽  
Quantum Mechanical ◽  
Hydration Free Energy ◽  
Mechanical Method ◽  
Energy Calculations

Alchemical Hydration Free Energy Calculations Using Molecular Dynamics with Explicit Polarization and Induced Polarity Decoupling: An OTFP Approach

2019 ◽  
Author(s):  
Braden Kelly ◽  
William Smith
Keyword(s):  
Free Energy ◽  
Gas Phase ◽  
Free Energy Calculations ◽  
Partial Charge ◽  
Experimental Uncertainty ◽  
Hydration Free Energy ◽  
Energy Calculations ◽  
Lennard Jones ◽  
Partial Charges ◽  
The Cost

We present a methodology using fixed charge force–fields for alchemical solvation free energy calculations which accounts for the change in polarity that the solute experiences as it transfers from the gas-phase to the condensed phase. We update partial charges use QM/MM snapshots, decoupling the electric field appropriately when updating the partial charges. We also show how to account for the cost of self-polarization. We test our methodology on 30 molecules ranging from small polar to large drug–like molecules.We use Minimum Basis Iterative Stockholder (MBIS), Restrained Electrostatic Potential(RESP) and AM1-BCC partial charge methodologies. Using our method with MP2/cc-pVTZ and MBIS partial charges yields an AAD that is 2.98 kJ·mol−1(0.71 kcal·mol−1) lower than AM1–BCC. AM1–BCC is within experimental uncertainty on 10% of thedata compared to 40% with our method. We conjecture that results can be further improved by using Lennard–Jones and torsional parameters refitted to MBIS and RESP partial charge methods that use high levels of theory.<br>

Free-Energy Calculations of Ionic Hydration Consistent with the Experimental Hydration Free Energy of the Proton

2017 ◽  
Vol 8 (12) ◽  
pp. 2705-2712 ◽  
Author(s):  
Haiyang Zhang ◽  
Yang Jiang ◽  
Hai Yan ◽  
Chunhua Yin ◽  
Tianwei Tan ◽  
...  
Keyword(s):  
Free Energy ◽  
Free Energy Calculations ◽  
Hydration Free Energy ◽  
Energy Calculations ◽  
Ionic Hydration

Automated Assessment of Binding Affinity via Alchemical Free Energy Calculations

2020 ◽  
Author(s):  
Maximilian Kuhn ◽  
Stuart Firth-Clark ◽  
Paolo Tosco ◽  
Antonia S. J. S. Mey ◽  
Mark Mackey ◽  
...  
Keyword(s):  
Free Energy ◽  
Protein Structures ◽  
Free Energy Calculations ◽  
Distinct Advantage ◽  
Binding Affinities ◽  
Structure Based Drug Design ◽  
Energy Calculations ◽  
Kendall’S Τ ◽  
Experimental Values ◽  
Better Than

Free energy calculations have seen increased usage in structure-based drug design. Despite the rising interest, automation of the complex calculations and subsequent analysis of their results are still hampered by the restricted choice of available tools. In this work, an application for automated setup and processing of free energy calculations is presented. Several sanity checks for assessing the reliability of the calculations were implemented, constituting a distinct advantage over existing open-source tools. The underlying workflow is built on top of the software Sire, SOMD, BioSimSpace and OpenMM and uses the AMBER14SB and GAFF2.1 force fields. It was validated on two datasets originally composed by Schrödinger, consisting of 14 protein structures and 220 ligands. Predicted binding affinities were in good agreement with experimental values. For the larger dataset the average correlation coefficient Rp was 0.70 ± 0.05 and average Kendall’s τ was 0.53 ± 0.05 which is broadly comparable to or better than previously reported results using other methods. <br>

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

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