Literature DB >> 28485585

A Comparison of Quantum and Molecular Mechanical Methods to Estimate Strain Energy in Druglike Fragments.

Benjamin D Sellers1, Natalie C James1, Alberto Gobbi1.   

Abstract

Reducing internal strain energy in small molecules is critical for designing potent drugs. Quantum mechanical (QM) and molecular mechanical (MM) methods are often used to estimate these energies. In an effort to determine which methods offer an optimal balance in accuracy and performance, we have carried out torsion scan analyses on 62 fragments. We compared nine QM and four MM methods to reference energies calculated at a higher level of theory: CCSD(T)/CBS single point energies (coupled cluster with single, double, and perturbative triple excitations at the complete basis set limit) calculated on optimized geometries using MP2/6-311+G**. The results show that both the more recent MP2.X perturbation method as well as MP2/CBS perform quite well. In addition, combining a Hartree-Fock geometry optimization with a MP2/CBS single point energy calculation offers a fast and accurate compromise when dispersion is not a key energy component. Among MM methods, the OPLS3 force field accurately reproduces CCSD(T)/CBS torsion energies on more test cases than the MMFF94s or Amber12:EHT force fields, which struggle with aryl-amide and aryl-aryl torsions. Using experimental conformations from the Cambridge Structural Database, we highlight three example structures for which OPLS3 significantly overestimates the strain. The energies and conformations presented should enable scientists to estimate the expected error for the methods described and we hope will spur further research into QM and MM methods.

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Year:  2017        PMID: 28485585     DOI: 10.1021/acs.jcim.6b00614

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  14 in total

1.  Toward Learned Chemical Perception of Force Field Typing Rules.

Authors:  Camila Zanette; Caitlin C Bannan; Christopher I Bayly; Josh Fass; Michael K Gilson; Michael R Shirts; John D Chodera; David L Mobley
Journal:  J Chem Theory Comput       Date:  2018-12-24       Impact factor: 6.006

2.  Characterization of Specific N-α-Acetyltransferase 50 (Naa50) Inhibitors Identified Using a DNA Encoded Library.

Authors:  Pei-Pei Kung; Patrick Bingham; Benjamin J Burke; Qiuxia Chen; Xuemin Cheng; Ya-Li Deng; Dengfeng Dou; Junli Feng; Gary M Gallego; Michael R Gehring; Stephan K Grant; Samantha Greasley; Anthony R Harris; Karen A Maegley; Jordan Meier; Xiaoyun Meng; Jose L Montano; Barry A Morgan; Brigitte S Naughton; Prakash B Palde; Thomas A Paul; Paul Richardson; Sylvie Sakata; Alex Shaginian; William K Sonnenburg; Chakrapani Subramanyam; Sergei Timofeevski; Jinqiao Wan; Wen Yan; Albert E Stewart
Journal:  ACS Med Chem Lett       Date:  2020-04-10       Impact factor: 4.345

3.  Transforming Computational Drug Discovery with Machine Learning and AI.

Authors:  Justin S Smith; Adrian E Roitberg; Olexandr Isayev
Journal:  ACS Med Chem Lett       Date:  2018-10-08       Impact factor: 4.345

4.  Ligand Strain Energy in Large Library Docking.

Authors:  Shuo Gu; Matthew S Smith; Ying Yang; John J Irwin; Brian K Shoichet
Journal:  J Chem Inf Model       Date:  2021-09-01       Impact factor: 6.162

5.  SAMPL6 host-guest challenge: binding free energies via a multistep approach.

Authors:  Yiğitcan Eken; Prajay Patel; Thomas Díaz; Michael R Jones; Angela K Wilson
Journal:  J Comput Aided Mol Des       Date:  2018-09-17       Impact factor: 3.686

6.  Improving small molecule force fields by identifying and characterizing small molecules with inconsistent parameters.

Authors:  Jordan N Ehrman; Victoria T Lim; Caitlin C Bannan; Nam Thi; Daisy Y Kyu; David L Mobley
Journal:  J Comput Aided Mol Des       Date:  2021-01-28       Impact factor: 3.686

7.  chemalot and chemalot_knime: Command line programs as workflow tools for drug discovery.

Authors:  Man-Ling Lee; Ignacio Aliagas; Jianwen A Feng; Thomas Gabriel; T J O'Donnell; Benjamin D Sellers; Bernd Wiswedel; Alberto Gobbi
Journal:  J Cheminform       Date:  2017-06-12       Impact factor: 5.514

8.  Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning.

Authors:  Justin S Smith; Benjamin T Nebgen; Roman Zubatyuk; Nicholas Lubbers; Christian Devereux; Kipton Barros; Sergei Tretiak; Olexandr Isayev; Adrian E Roitberg
Journal:  Nat Commun       Date:  2019-07-01       Impact factor: 14.919

9.  Accuracy evaluation and addition of improved dihedral parameters for the MMFF94s.

Authors:  Joel Wahl; Joel Freyss; Modest von Korff; Thomas Sander
Journal:  J Cheminform       Date:  2019-08-07       Impact factor: 5.514

10.  Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network.

Authors:  Roman Zubatyuk; Justin S Smith; Jerzy Leszczynski; Olexandr Isayev
Journal:  Sci Adv       Date:  2019-08-09       Impact factor: 14.136

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