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Molecular modelling encompasses all methods, theoretical and computational, used to model or mimic the behaviour of molecules. [1] The methods are used in the fields of computational chemistry, drug design, computational biology and materials science to study molecular systems ranging from small chemical systems to large biological molecules and material assemblies.
The difference is that this approach relies on equilibrium statistical mechanics rather than molecular dynamics. Instead of trying to reproduce the dynamics of a system, it generates states according to appropriate Boltzmann distribution. Thus, it is the application of the Metropolis Monte Carlo simulation to molecular systems.
In credibility theory, a branch of study in actuarial science, the Bühlmann model is a random effects model (or "variance components model" or hierarchical linear model) used to determine the appropriate premium for a group of insurance contracts. The model is named after Hans Bühlmann who first published a description in 1967.
[63] [64] [65] In statistical physics, Monte Carlo molecular modeling is an alternative to computational molecular dynamics, and Monte Carlo methods are used to compute statistical field theories of simple particle and polymer systems. [37] [66] Quantum Monte Carlo methods solve the many-body problem for quantum systems.
An alpha-helix with hydrogen bonds (yellow dots) The α-helix is the most abundant type of secondary structure in proteins. The α-helix has 3.6 amino acids per turn with an H-bond formed between every fourth residue; the average length is 10 amino acids (3 turns) or 10 Å but varies from 5 to 40 (1.5 to 11 turns).
The creation of mathematical models of molecular properties and behavior is referred to as molecular modeling, and their graphical depiction is referred to as molecular graphics. The term, "molecular model" refer to systems that contain one or more explicit atoms (although solvent atoms may be represented implicitly) and where nuclear structure ...
In the late 1980s and early 1990s, there was a distinct effort for actuaries to combine financial theory and stochastic methods into their established models. [12] Ideas from financial economics became increasingly influential in actuarial thinking, and actuarial science has started to embrace more sophisticated mathematical modelling of ...
David X. Li (Chinese: 李祥林; pinyin: Lǐ Xiánglín [1] born Nanjing, China in the 1960s) is a Chinese-born Canadian quantitative analyst and actuary who pioneered the use of Gaussian copula models for the pricing of collateralized debt obligations (CDOs) in the early 2000s.