Uniform quality gaussian basis sets for molecular calculations. IV. Gradient and charged optimized basis sets for CH4

Comparison of the molecular Q-optimized and molecular gradient optimized carbon basis sets for CH 4 showed that molecular Q optimization is an excellent substitute to the more expensive molecular gradient optimization. The parameter Q of the Q optimization is related to the population (i.e., net charge) on the atom.     

A Non-derivative MFCC Optimization Study of Cyclohexapeptide Monohydrate

The MFCC-downhill simplex method is presented to study the binding structure of small ligands in large molecular complex systems. This method employs the Molecular Fractionation with Conjugated Caps (MFCC) approach to compute the interaction energy-structure relation of the system and implements the downhill simplex algorithm for structural optimization. The method is tested on a molecular system of cyclo-AAGAGG￠H2O to optimize the binding position of water molecule to the ˉxed cyclohexapeptide. The MFCC-downhill simplex optimization results are in good agreement with the crystal structure. An MFCC-Powell optimization method which uses the Powell's minimization algorithm is also described and tested on the same system. The MFCC-downhill simplex optimization is more e±cient than the MFCC-Powell method.     

The atom assignment problem in automated de novo drug design. 3. Algorithms for optimization of fragment placement onto 3D molecular graphs

Summary Atom assignment onto 3D molecular graphs is a combinatoric problem in discrete space. If atoms are to be placed efficiently on molecular graphs produced in drug binding sites, the assignment must be optimized. An algorithm, based on simulated annealing, is presented for efficient optimization of fragment placement. Extensive tests of the method have been performed on five ligands taken from the Protein Data Bank. The algorithm is presented with the ligand graph and the electrostatic potential as input. Self placement of molecular fragments was monitored as an objective test. A hydrogen-bond option was also included, to enable the user to highlight specific needs. The algorithm performed well in the optimization, with successful replications. In some cases, a modification was necessary to reduce the tendency to give multiple halogenated structures. This optimization procedure should prove useful for automated de novo drug design.     

Controlled-advancement rigid-body optimization of nanosystems

In this study, we propose a novel optimization algorithm, with application to the refinement of molecular complexes. Particularly, we consider optimization problem as the calculation of quasi-static trajectories of rigid bodies influenced by the inverse-inertia-weighted energy gradient and introduce the concept of advancement region that guarantees displacement of a molecule strictly within a relevant region of conformational space. The advancement region helps to avoid typical energy minimization pitfalls, thus, the algorithm is suitable to work with arbitrary energy functions and arbitrary types of molecular complexes without necessary tuning of its hyper-parameters. Our method, called controlled-advancement rigid-body optimization of nanosystems (Carbon), is particularly useful for the large-scale molecular refinement, as for example, the putative binding candidates obtained with protein–protein docking pipelines. Implementation of Carbon with user-friendly interface is available in the SAMSON platform for molecular modeling at https://www.samson-connect.net . © 2019 Wiley Periodicals, Inc.     

Emergent strategies for inverse molecular design

Molecular design is essential and ubiquitous in chemistry, physics, biology, and material science. The immense space of available candidate molecules requires novel optimization strategies and algorithms for exploring the space and achieving efficient and effective molecular design. This paper summarizes the current progress toward developing practical theoretical optimization schemes for molecular design. In particular, we emphasize emergent strategies for inverse molecular design. Several representative design examples, based on recently developed strategies, are described to demonstrate the principles of inverse molecular design.     

The quasi-independent curvilinear coordinate approximation for geometry optimization

This paper presents an efficient alternative to well established algorithms for molecular geometry optimization. This approach exploits the approximate decoupling of molecular energetics in a curvilinear internal coordinate system, allowing separation of the 3N-dimensional optimization problem into an O(N) set of quasi-independent one-dimensional problems. Each uncoupled optimization is developed by a weighted least squares fit of energy gradients in the internal coordinate system followed by extrapolation. In construction of the weights, only an implicit dependence on topologically connected internal coordinates is present. This new approach is competitive with the best internal coordinate geometry optimization algorithms in the literature and works well for large biological problems with complicated hydrogen bond networks and ligand binding motifs.     

An adaptive immune optimization algorithm for energy minimization problems

Based on the immune theory of biology, a novel evolutionary algorithm, adaptive immune optimization algorithm (AIOA), is proposed. In AIOA, density regulation and immune selection is adopted to control the individual diversity and the convergence adaptively. By an application of the algorithm to the optimization of test functions, it is shown that the algorithm is a highly efficient optimization method compared with other stochastic optimization methods. The algorithm was also applied to the optimization of Lennard-Jones clusters, and the results show that the method can find the optimal structure of N相似文献   

A new method for the gradient‐based optimization of molecular complexes

We present a novel method for the local optimization of molecular complexes. This new approach is especially suited for usage in molecular docking. In molecular modeling, molecules are often described employing a compact representation to reduce the number of degrees of freedom. This compact representation is realized by fixing bond lengths and angles while permitting changes in translation, orientation, and selected dihedral angles. Gradient‐based energy minimization of molecular complexes using this representation suffers from well‐known singularities arising during the optimization process. We suggest an approach new in the field of structure optimization that allows to employ gradient‐based optimization algorithms for such a compact representation. We propose to use exponential mapping to define the molecular orientation which facilitates calculating the orientational gradient. To avoid singularities of this parametrization, the local minimization algorithm is modified to change efficiently the orientational parameters while preserving the molecular orientation, i.e. we perform well‐defined jumps on the objective function. Our approach is applicable to continuous, but not necessarily differentiable objective functions. We evaluated our new method by optimizing several ligands with an increasing number of internal degrees of freedom in the presence of large receptors. In comparison to the method of Solis and Wets in the challenging case of a non‐differentiable scoring function, our proposed method leads to substantially improved results in all test cases, i.e. we obtain better scores in fewer steps for all complexes. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2009     

计算机辅助合成最优化方法研究(I)——Azodrin合成反应优化条件选择

本文提出了一种计算机辅助有机合成条件(摩尔比,反应物浓度,处理时间)最优化选择方法.方法的基础是通过15个预实验建立一个多项式,然后用计算机扫描技术来优化,含量与得率作为优化指标.方法用于工业久效磷的合成,结果表明预示与实验结果非常一致.     

Designing molecules by optimizing potentials

The astronomical number of accessible discrete chemical structures makes rational molecular design extremely challenging. We formulate the design of molecules with specific tailored properties as performing a continuous optimization in the space of electron-nuclear attraction potentials. The optimization is facilitated by using a linear combination of atomic potentials (LCAP), a general framework that creates a continuous property landscape from an otherwise unlinked set of discrete molecular-property values. A demonstration of this approach is given for the optimization of molecular electronic polarizability and hyperpolarizability. We show that the optimal structures can be determined without enumerating and separately evaluating the characteristics of the combinatorial number of possible structures, a process that would be much slower. The LCAP approach may be used with quantum or classical Hamiltonians, suggesting possible applications to drug design and new materials discovery.     

Inverse design and synthesis of acac-coumarin anchors for robust TiO2 sensitization

An inverse design methodology suitable to assist the synthesis and optimization of molecular sensitizers for dye-sensitized solar cells is introduced. The method searches for molecular adsorbates with suitable photoabsorption properties through continuous optimization of "alchemical" structures in the vicinity of a reference molecular framework. The approach is illustrated as applied to the design and optimization of linker chromophores for TiO(2) sensitization, using the recently developed phenyl-acetylacetonate (i.e., phenyl-acac) anchor [McNamara et al. J. Am. Chem. Soc.2008, 130, 14329-14338] as a reference framework. A novel anchor (3-acac-pyran-2-one) is found to be a local optimum, with improved sensitization properties when compared to phenyl-acac. Its molecular structure is related to known coumarin dyes that could be used as lead chromophore anchors for practical applications in dye-sensitized solar cells. Synthesis and spectroscopic characterization confirms that the linker provides robust attachment to TiO(2), even in aqueous conditions, yielding improved sensitization to solar light and ultrafast interfacial electron injection. The findings are particularly relevant to the design of sensitizers for dye-sensitized solar cells because of the wide variety of structures that are possible but they should be equally useful for other applications such as ligand design for homogeneous catalysis.     

Use of symmetric rank-one Hessian update in molecular geometry optimization

The use of the symmetric rank-one Hessian update and the Broyden–Fletcher–Goldfarb–Shano (BFGS) update formula are considered in an ab initio molecular geometry optimization algorithm. It is noted that the symmetric rank-one Hessian update has an advantage when compared with the BFGS update formula and this advantage must be more evident in the optimization of molecular geometry, because the total energy surface is a near-quadratic function with a small nonlinearity close to a minimum point. The results obtained in geometry optimization of a test group of molecules support this proposal and show that the use of the symmetric rank-one Hessian update formula permits reduction of the number of energy and gradient evaluations needed to locate a minimum on the energy surface. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 1877–1886, 1998     

Universal optimal working cycles of molecular motors

Molecular motors capable of directional track-walking or rotation are abundant in living cells, and inspire the emerging field of artificial nanomotors. Some biomotors can convert 90% of free energy from chemical fuels into usable mechanical work, and the same motors still maintain a speed sufficient for cellular functions. This study exposed a new regime of universal optimization that amounts to a thermodynamically best working regime for molecular motors but is unfamiliar in macroscopic engines. For the ideal case of zero energy dissipation, the universally optimized working cycle for molecular motors is infinitely slow like Carnot cycle for heat engines. But when a small amount of energy dissipation reduces energy efficiency linearly from 100%, the speed is recovered exponentially due to Boltzmann's law. Experimental data on a major biomotor (kinesin) suggest that the regime of universal optimization has been largely approached in living cells, underpinning the extreme efficiency-speed trade-off in biomotors. The universal optimization and its practical approachability are unique thermodynamic advantages of molecular systems over macroscopic engines in facilitating motor functions. The findings have important implications for the natural evolution of biomotors as well as the development of artificial counterparts.     

Calibration of forcefields for molecular simulation: Sequential design of computer experiments for building cost‐efficient kriging metamodels

We present a global strategy for molecular simulation forcefield optimization, using recent advances in Efficient Global Optimization algorithms. During the course of the optimization process, probabilistic kriging metamodels are used, that predict molecular simulation results for a given set of forcefield parameter values. This enables a thorough investigation of parameter space, and a global search for the minimum of a score function by properly integrating relevant uncertainty sources. Additional information about the forcefield parameters are obtained that are inaccessible with standard optimization strategies. In particular, uncertainty on the optimal forcefield parameters can be estimated, and transferred to simulation predictions. This global optimization strategy is benchmarked on the TIP4P water model. © 2013 Wiley Periodicals, Inc.     

A technique for orbital exponent optimization in ab initio HF?SCF?LCAO?MO calculations

A technique for Slater orbital exponent optimization in an HF? SCF? LCAO? MO calculation is proposed in which orbital exponent variation is incorporated into the SCF scheme. This is accomplished by rewriting Slater's rules so that the shielding terms depend on the molecular charge distribution through the elements of the population matrix. The SCF scheme then includes a calculation of a new set of orbital exponents from the coefficients of self-consistent molecular orbitals obtained from the previous set of exponents. The process is iterated until the energy attains its lowest value. The technique is illustrated by minimal basis calculations on LiH, BH, and HF. Near optimization is obtained with considerably less effort than is necessary for other reported techniques. Aside from interesting properties, the technique can be important for extended basis calculations where exponent optimization is a difficult task.     

Optimization of molecular electronic structure calculations. 1. Local symmetry and local symmetricized orbitals

A formalism is suggested of the so-called local symmetricized orbitals to be used for the construction of a symmetricized basis in molecular electronic structure calculations. The local symmetricized orbitals are defined as additive contributions to the symmetry orbitals of a molecule that arise from symmetry operations of a corresponding atom. The local symmetricized orbitals are transformed according to the irreducible representations of the molecular symmetry group. This approach appears to be most suitable for the optimization of quantum mechanical calculations accounting for the spatial symmetry of compounds under consideration. This fact is due to the formalism of the local symmetricized orbitals that explicitly accounts for the local symmetry of basis function centers, which is essential for such optimization.     

The Design of Leadlike Combinatorial Libraries

The optimization of low-potency leads into drugs is often accompanied by an increase in molecular weight (M(r)) and lipophilicity, as a consequence of affinity enhancement. Hits with affinity at μM levels discovered by screening leadlike libraries allow scope for this optimization process, as shown schematically by the distributions of M(r) for a leadlike library (1), oral drugs (2), and a typical combinatorial chemistry library (3). y=percentage with a particular molecular weight.     

Parameterization of a reactive force field using a Monte Carlo algorithm

Parameterization of a molecular dynamics force field is essential in realistically modeling the physicochemical processes involved in a molecular system. This step is often challenging when the equations involved in describing the force field are complicated as well as when the parameters are mostly empirical. ReaxFF is one such reactive force field which uses hundreds of parameters to describe the interactions between atoms. The optimization of the parameters in ReaxFF is done such that the properties predicted by ReaxFF matches with a set of quantum chemical or experimental data. Usually, the optimization of the parameters is done by an inefficient single‐parameter parabolic‐search algorithm. In this study, we use a robust metropolis Monte‐Carlo algorithm with simulated annealing to search for the optimum parameters for the ReaxFF force field in a high‐dimensional parameter space. The optimization is done against a set of quantum chemical data for MgSO₄ hydrates. The optimized force field reproduced the chemical structures, the equations of state, and the water binding curves of MgSO₄ hydrates. The transferability test of the ReaxFF force field shows the extend of transferability for a particular molecular system. This study points out that the ReaxFF force field is not indefinitely transferable. © 2013 Wiley Periodicals, Inc.     

Molecular equilibrium geometries and vibrational frequencies by maximum overlap symmetry molecular orbital method

It is demonstrated that the maximum overlap symmetry molecular orbital method can be used for optimization of molecular geometries and calculation of vibrational frequencies by adding a two-body repulsive energy term and a modification of the Wolfsberg–Helmholz formula. The obtained equilibrium geometries and vibrational frequencies are on the whole in good accordance with experimental data, which shows that the basic idea using the method to optimize molecular geometries and to calculate vibrational frequencies is reasonable.     

A novel conformation optimization model and algorithm for structure-based drug design

In this paper, we present a multi-scale optimization model and an entropy-based genetic algorithm for molecular docking. In this model, we introduce to the refined docking design a concept of residue groups based on induced-fit and adopt a combination of conformations in different scales. A new iteration scheme, in conjunction with multi-population evolution strategy, entropy-based searching technique with narrowing down space and the quasi-exact penalty function, is developed to address the optimization problem for molecular docking. A new docking program that accounts for protein flexibility has also been developed. The docking results indicate that the method can be efficiently employed in structure-based drug design.