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Semiempirical quantum mechanical methods for noncovalent interactions for chemical and biochemical applications.
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Semiempirical (SE) methods can be derived from either Hartree-Fock or density functional theory by applying systematic approximations, leading to efficient computational schemes that are several orders of magnitude faster than ab initio calculations. Such numerical efficiency, in combination with modern computational facilities and linear scaling algorithms, allows application of SE methods to very large molecular systems with extensive conformational sampling. To reliably model the structure, dynamics, and reactivity of biological and other soft matter systems, however, good accuracy for the description of noncovalent interactions is required. In this review, we analyze popular SE approaches in terms of their ability to model noncovalent interactions, especially in the context of describing biomolecules, water solution, and organic materials. We discuss the most significant errors and proposed correction schemes, and we review their performance using standard test sets of molecular systems for quantum chemical methods and several recent applications. The general goal is to highlight both the value and limitations of SE methods and stimulate further developments that allow them to effectively complement ab initio methods in the analysis of complex molecular systems.
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“In the last 10 years, important advances were made in computing hardware (e.g., Graphics Processing Units – GPUs), in the development of quantum algorithms to compute molecular wave functions (Dixon and Merz, 1997; Birgin et al., 2013), the development of more reliable semi-empirical quantum methods (Christensen et al., 2016; Yilmazer and Korth, 2016), and development of new hybrid QM/MM methods (”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Christensen et al., 2016
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“This is particularly true when dealing with systems where the molecular recognition involves bond formation, π-stacking, cation-π, halogen bonding (i.e., σ-hole bonding), and polarization and charge transfer effects (Christensen et al., 2016).”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Christensen et al., 2016
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Metadata
- Authors
- Christensen, A. S., Kubař, T., Cui, Q., & Elstner, M.
- Affiliations
- University of Wisconsin–Madison (US)
- Karlsruhe Institute of Technology , , Kaiserstrasse 12 , ,
- Karlsruhe Institute of Technology (DE)
- Corresponding author
- Cui, Qiang
- Elstner, Marcus
- Venue
- Chemical Reviews
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-04-13
- Volume
- 116
- Issue
- 9
- Pages
- 5301-5337
- ISSN
- 0009-2665 (ISSN-L)
- 1520-6890
- PMC
- PMC4867870
- OpenAlex
- W2337403656
- Cited by
- 446
- References
- 336
- Citations by year
- 2026: 25 · 2025: 54 · 2024: 51 · 2023: 48 · 2022: 59 · 2021: 52 · 2020: 46 · 2019: 35 · 2018: 36 · 2017: 25 · 2016: 13
- FWCI
- 1.85
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Chemical Reviews (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of General Medical Sciences (R01-GM106443)
- Division of Chemistry (CHE-1300209)
- Topics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Crystallography and molecular interactions
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Keywords
- non-covalent interactions, biomolecules, aqueous solutions, organic materials, soft matter, conformational sampling, linear-scaling methods, large molecular systems
- MeSH terms
- Algorithms, Ligands, Metals, Peptides, Proteins, Quantum Theory, Water
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