Literature DB >> 19929831

Quantum mechanical methods for drug design.

Ting Zhou1, Danzhi Huang, Amedeo Caflisch.   

Abstract

Quantum mechanical (QM) methods are becoming popular in computational drug design and development mainly because high accuracy is required to estimate (relative) binding affinities. For low-to medium-throughput in silico screening, (e.g., scoring and prioritizing a series of inhibitors sharing the same molecular scaffold) efficient approximations have been developed in the past decade, like linear scaling QM in which the computation time scales almost linearly with the number of basis functions. Furthermore, QM-based procedures have been used recently for determining protonation states of ionizable groups, evaluating energies, and optimizing molecular structures. For high-throughput in silico screening QM approaches have been employed to derive robust quantitative structure-activity relationship models. It is expected that the use of QM methods will keep growing in all phases of computer-aided drug design and development. However, extensive sampling of conformational space and treatment of solution of macromolecules are still limiting factors for the broad application of QM in drug design.

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Year:  2010        PMID: 19929831     DOI: 10.2174/156802610790232242

Source DB:  PubMed          Journal:  Curr Top Med Chem        ISSN: 1568-0266            Impact factor:   3.295


  24 in total

1.  Correlation between biological activity and binding energy in systems of integrin with cyclic RGD-containing binders: a QM/MM molecular dynamics study.

Authors:  Mingli Xiang; Yuchun Lin; Gu He; Lijuan Chen; Mingli Yang; Shengyong Yang; Yirong Mo
Journal:  J Mol Model       Date:  2012-06-27       Impact factor: 1.810

Review 2.  Computational methods in drug discovery.

Authors:  Gregory Sliwoski; Sandeepkumar Kothiwale; Jens Meiler; Edward W Lowe
Journal:  Pharmacol Rev       Date:  2013-12-31       Impact factor: 25.468

3.  Transferable scoring function based on semiempirical quantum mechanical PM6-DH2 method: CDK2 with 15 structurally diverse inhibitors.

Authors:  Petr Dobeš; Jindřich Fanfrlík; Jan Rezáč; Michal Otyepka; Pavel Hobza
Journal:  J Comput Aided Mol Des       Date:  2011-02-01       Impact factor: 3.686

Review 4.  Biomolecular force fields: where have we been, where are we now, where do we need to go and how do we get there?

Authors:  Pnina Dauber-Osguthorpe; A T Hagler
Journal:  J Comput Aided Mol Des       Date:  2018-11-30       Impact factor: 3.686

5.  Computational analysis of aspartic protease plasmepsin II complexed with EH58 inhibitor: a QM/MM MD study.

Authors:  Natália de Farias Silva; Jerônimo Lameira; Cláudio Nahum Alves
Journal:  J Mol Model       Date:  2011-01-25       Impact factor: 1.810

6.  The electrostatic embedding contribution to DFT calculations of ligand-amino acid residues interaction.

Authors:  Tamires C da Silva Ribeiro; Marcelo L Lyra; Vinícius Manzoni
Journal:  J Mol Model       Date:  2018-07-19       Impact factor: 1.810

7.  Computational ligand-based rational design: Role of conformational sampling and force fields in model development.

Authors:  Jihyun Shim; Alexander D Mackerell
Journal:  Medchemcomm       Date:  2011-05       Impact factor: 3.597

8.  Reaction Pathway and Free Energy Profile for Cocaine Hydrolase-Catalyzed Hydrolysis of (-)-Cocaine.

Authors:  Junjun Liu; Chang-Guo Zhan
Journal:  J Chem Theory Comput       Date:  2012-03-06       Impact factor: 6.006

9.  A variational linear-scaling framework to build practical, efficient next-generation orbital-based quantum force fields.

Authors:  Timothy J Giese; Haoyuan Chen; Thakshila Dissanayake; George M Giambaşu; Hugh Heldenbrand; Ming Huang; Erich R Kuechler; Tai-Sung Lee; Maria T Panteva; Brian K Radak; Darrin M York
Journal:  J Chem Theory Comput       Date:  2013-03-12       Impact factor: 6.006

10.  Insights into the EGFR SAR of N-phenylquinazolin-4-amine-derivatives using quantum mechanical pairwise-interaction energies.

Authors:  Saw Simeon; Nathjanan Jongkon; Warot Chotpatiwetchkul; M Paul Gleeson
Journal:  J Comput Aided Mol Des       Date:  2019-09-07       Impact factor: 3.686

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