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An efficient newton‐like method for molecular mechanics energy minimization of large molecules
Abstract
Techniques from numerical analysis and crystallographic refinement have been combined to produce a variant of the Truncated Newton nonlinear optimization procedure. The new algorithm shows particular promise for potential energy minimization of large molecular systems. Usual implementations of Newton's method require storage space proportional to the number of atoms squared (i.e., O(N2)) and computer time of O(N3). Our suggested implementation of the Truncated Newton technique requires storage of less than O(N1.5) and CPU time of less than O(N2) for structures containing several hundred to a few thousand atoms. The algorithm exhibits quadratic convergence near the minimum and is also very tolerant of poor initial structures. A comparison with existing optimization procedures is detailed for cyclohexane, arachidonic acid, and the small protein crambin. In particular, a structure for crambin (662 atoms) has been refined to an RMS gradient of 3.6 × 10−6 kcal/mol/Å per atom on the MM2 potential energy surface. Several suggestions are made which may lead to further improvement of the new method.
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Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Cited by
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Fennell, C. J., & Gezelter, J. D. (2006) · The Journal of Chemical Physics · doi:10.1063/1.2206581 · Cited by 667
Artrith, N., & Urban, A. (2016) · Computational Materials Science · doi:10.1016/j.commatsci.2015.11.047 · Cited by 508
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Metadata
- Authors
- Ponder, J. W., & Richards, F. M.
- Venue
- Journal of Computational Chemistry
- Publisher
- Wiley
- Published
- 1987-10
- Volume
- 8
- Issue
- 7
- Pages
- 1016-1024
- ISSN
- 0192-8651 (ISSN-L)
- 1096-987X
- OpenAlex
- W2128172847
- Cited by
- 880
- References
- 1
- FWCI
- 0.24
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ponder, J. W., & Richards, F. M. (1987). An efficient newton‐like method for molecular mechanics energy minimization of large molecules. Journal of Computational Chemistry. https://doi.org/10.1002/jcc.540080710
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.