Randomness and Noise in Information Systems

SCELib3.0: The new revision of SCELib, the parallel computational library of molecular properties in the Single Center Approach

Analysis of a two dimensional molecular Berry phase system

Revealing gene regulation-based neural network computing in bacteria

Most human diseases are complex multi-factorial diseases resulting from the combination of various genetic and environmental factors. In the KEGG database resource (http://www.genome.jp/kegg/), diseases are viewed as perturbed states of the molecular system, and drugs as perturbants to the molecular system. Disease information is computerized in two forms: pathway maps and gene/molecule lists. The KEGG PATHWAY database contains pathway maps for the molecular systems in both normal and perturbed states. In the KEGG DISEASE database, each disease is represented by a list of known disease genes, any known environmental factors at the molecular level, diagnostic markers and therapeutic drugs, which may reflect the underlying molecular system. The KEGG DRUG database contains chemical structures and/or chemical components of all drugs in Japan, including crude drugs and TCM (Traditional Chinese Medicine) formulas, and drugs in the USA and Europe. This database also captures knowledge about two types of molecular networks: the interaction network with target molecules, metabolizing enzymes, other drugs, etc. and the chemical structure transformation network in the history of drug development. The new disease/drug information resource named KEGG MEDICUS can be used as a reference knowledge base for computational analysis of molecular networks, especially, by integrating large-scale experimental datasets.

Typical experimental setups for molecular systems must deal with a certain coupling to the external environment, that is, the system is open and exchanges mass, momentum, and energy with its surroundings. Instead, standard molecular simulations are mostly performed using periodic boundary conditions with a constant number of molecules. In this review, we summarize major development of simulation methodologies, which, contrary to standard techniques, open up the boundaries of a molecular system and allow for exchange of energy and matter with the environment, in and out of equilibrium. In particular, we construct the review around the open boundary simulation approaches based on the Adaptive Resolution Scheme (AdResS), which seamlessly couples different levels of resolution in molecular simulations. Ideas and theoretical concepts used in its development lie at the crossroad of different fields and disciplines and open many different directions for future developments in molecular simulation. We examine progress related to theoretical as well as novel modeling approaches bridging length scales from quantum to the continuum description and report on their application in various molecular systems. The outlook of the review is dedicated to the perspective of how to further incorporate rigorous theoretical approaches such as the Bergmann–Lebowitz and Emch–Sewell models into the molecular simulation algorithms and stimulate further development of open boundary simulation methods and their application.

During recent decades it has become feasible to simulate the dynamics of molecular systems on a computer. The method of molecular dynamics (MD) solves Newton's equations of motion for a molecular system, which results in trajectories for all atoms in the system. From these atomic trajectories a variety of properties can be calculated. The aim of computer simulations of molecular systems is to compute macroscopic behavior from microscopic interactions. The main contributions a microscopic consideration can offer are (1) the understanding and (2) interpretation of experimental results, (3) semiquantitative estimates of experimental results, and (4) the capability to interpolate or extrapolate experimental data into regions that are only difficultly accessible in the laboratory. One of the two basic problems in the field of molecular modeling and simulation is how to efficiently search the vast configuration space which is spanned by all possible molecular conformations for the global low (free) energy regions which will be populated by a molecular system in thermal equilibrium. The other basic problem is the derivation of a sufficiently accurate interaction energy function or force field for the molecular system of interest. An important part of the art of computer simulation is to choose the unavoidable assumptions, approximations and simplifications of the molecular model and computational procedure such that their contributions to the overall inaccuracy are of comparable size, without affecting significantly the property of interest. Methodology and some practical applications of computer simulation in the field of (bio)chemistry will be reviewed.

Molecular simulation is a mature and versatile tool set widely utilized in many subjects with more than 30,000 publications each year. However, its methodology development has been struggling with a tradeoff between accuracy/resolution and speed, significant improvement of both beyond present state of the art is necessary to reliably substitute many expensive and laborious experiments in molecular biology, materials science and nanotechnology. Previously, the ubiquitous issue regarding severe wasting of computational resources in all forms of molecular simulations due to repetitive local sampling was raised, and the local free energy landscape approach was proposed to address it. This approach is derived from a simple idea of first learning local distributions, and followed by dynamic assembly of which to infer global joint distribution of a target molecular system. When compared with conventional explicit solvent molecular dynamics simulations, a simple and approximate implementation of this theory in protein structural refinement harvested acceleration of about six orders of magnitude without loss of accuracy. While this initial test revealed tremendous benefits for addressing repetitive local sampling, there are some implicit assumptions need to be articulated. Here, I present a more thorough discussion of repetitive local sampling; potential options for learning local distributions; a more general formulation with potential extension to simulation of near equilibrium molecular systems; the prospect of developing computation driven molecular science; the connection to mainstream residue pair distance distribution based protein structure prediction/refinement; and the fundamental difference of utilizing averaging from conventional molecular simulation framework based on potential of mean force. This more general development is termed the local distribution theory to release the limitation of strict thermodynamic equilibrium in its potential wide application in general soft condensed molecular systems.

A new mechanism for realizing negative permeability with a three-level ladder-type atomic (or molecular) system at microwave frequency is suggested. The explicit expressions for the magnetic permeability of both steady and transient cases are presented. Compared with the previous schemes to realize negative permeability within the framework of classical electromagnetic theory, the most remarkable feature in the present mechanism is that it can realize the isotropic material with negative permeability. Such a permeability can be controllably manipulated by external coupling fields and could lead to potential applications in quantum optical devices.

This paper presents an O(N) algorithm and its preliminary computer simulation results for virtual prototyping of molecular systems with a simple chain structure. The algorithm is based on proper integration between an internal coordinate method (ICM) and a multibody molecular model. ICM method makes the use of recursive relations possible between two adjacent subsets within a molecular system. The multibody molecular model takes the benefits of freezing degrees of freedom of some lightly excited high frequency bonds. Because these high frequency bonds would force the use of very small integration step sizes, which severely limits the time scales for virtual prototyping of dynamics of molecular conformation over long periods of time. Thus a new multiscale model and efficient algorithm is produced to increase computational efficiency for virtual prototyping of dynamical behaviors of molecular confirmation. This paper will be initially directed towards introduction of the new model and algorithm. Then attention will be turned to the implementation of the algorithm at macro scale, which can be used to demonstrate the validity of the procedure and algorithm. Final focus will be turned to the implementation of the algorithm to a simple molecular chain at micro scale. The algorithm gives an O(N) computational performance for formation/solution of equations of motion for a molecular chain system.

Continuous advancements in DNA nanotechnology have resulted in innovative information security strategies utilizing biomolecular-based encryption mechanisms to achieve effective protection and reliable storage. However, existing molecular encryption schemes are limited in their practical expansion due to their focus on the precise design and strict management of the key itself while neglecting the impact of complex environments, causing them to suffer from insufficient concealment and poor adaptability to complex access control scenarios. Here, we propose a pH-regulated camouflage strategy that significantly enhances the encryption concealment of molecular systems. Using pH-regulated logical operation transformations, we establish a switchable cascaded logic operation strategy that implements a two-layer DNA molecular firewall access control system. By integrating two distinct camouflage rules, the system constructs four differentiated access modes. This design effectively prevents unauthorized access, conceals true input addresses, and deceives potential attackers, thereby ensuring internal information security. This approach provides a simple method for the design and management of molecular systems and is expected to be applied in fields such as information security and environmental monitoring.

The inelastic neutron scattering (INS) spectrum of the original benzylic amide [2]catenane is recorded and simulated by a semiempirical quantum chemical procedure coupled with the most comprehensive approach available to date, the CLIMAX program. The successful simulation of the spectrum indicates that the modified neglect of differential overlap (MNDO) model can reproduce the intramolecular vibrations of a molecular system as large as a catenane (136 atoms). Because of the computational costs involved and some numerical instabilities, a less expensive approach is attempted which involves the molecular mechanics-based calculation of the INS response in terms of the most basic formulation for the scattering activity. The encouraging results obtained validate the less computationally intensive procedure and allow its extension to the calculation of the INS spectrum for a second, theoretical, co-conformer, which, although structurally and energetically reasonable, is not, in fact, found in the solid state. The second structure was produced by a Monte Carlo simulated annealing method run in the conformational space (a procedure that would have been prohibitively expensive at the semiempirical level) and is characterized by a higher degree of intramolecular hydrogen bonding than the x-ray structure. The two alternative structures yield different simulated spectra, only one of which, the authentic one, is compatible with the experimental data. Comparison of the two simulated and experimental spectra affords the identification of an inelastic neutron scattering spectral signature of the correct hydrogen bonding motif in the region slightly above 700 cm−1. The study illustrates that combinations of simulated INS data and experimental results can be successfully used to discriminate between different proposed structures or possible hydrogen bonding motifs in large functional molecular systems.

With the introduction of the Schrodinger equation in 1926, it was in principle clear how to describe a molecular system and its properties exactly in a nonrelativistic sense. However, for most molecular systems of chemical interest, the analytic solution of the Schrodinger equation is not possible. Therefore, since 1926, a multitude of hierarchical approximations (some of which are displayed in Figure 1) have been devised that allow for a systematic approach to the exact solution of the Schrodinger equation. Although the Schrodinger equation as the fundamental equation in electronic structure theory is already quite old, the field of quantum chemistry is still fairly young and fast moving, and much can be expected in the future for developing and applying quantum chemical methods for the treatment of molecular systems. The importance of the systematic hierarchy for solving the Schrodinger equation cannot be overemphasized, because it allows one, in principle, to systematicallyapproach the exact result for a molecularpropertyof interest. The simplest approach in this hierarchy is the Hartree–Fock (HF) method, which describes electron–electron interactions within a mean-field approach. The electroncorrelation effects neglected in this approach can be described by the so-called

Machine learning interatomic potentials (MLIPs) enables molecular dynamics (MD) simulations with ab initio accuracy and has been applied to various fields of physical science. However, the performance and transferability of MLIPs are limited by insufficient labeled training data, which require expensive ab initio calculations to obtain the labels, especially for complex molecular systems. To address this challenge, we design a novel geometric structure learning paradigm that consists of two stages. We first generate a large quantity of 3D configurations of target molecular system with classical molecular dynamics simulations. Then, we propose geometry-enhanced self-supervised learning consisting of masking, denoising, and contrastive learning to better capture the topology and 3D 1 geometric information from the unlabeled 3D configurations. We evaluate our method on various benchmarks ranging from small molecule datasets to complex periodic molecular systems with more types of elements. The experimental results show that the proposed pre-training method can greatly enhance the accuracy of MLIPs with few extra computational costs and works well with different invariant or equivariant graph neural network architectures. Our method improves the generalization capability of MLIPs and helps to realize accurate MD simulations for complex molecular systems.

Absorption and emission of a given molecular system are characterized by their specific spectral distributions. Great effort was undertaken to get spectral profiles of liquid dye solutions in analytical forms. One of the infallible methods for a spectral profile description is the momentum method. An observed spectral distribution, independently on a theoretical specification of the system, is represented by the set of moments expressing the maximum positions, half-widths and asymetries of the spectum [1]. It appears that in most cases of stuctureless bands the first three moments suffice for the description of their profiles. Another spectral distribution, accurately reproducing electronic band profiles is the log-normal distribution [2, 3]. This distribution is also a three parameter distribution similar to those, based on the configurational model [4]. Many attempts were undertaken for the adaptations of Gaussian distributions and their superpositions to represent the molecular spectra. Unfortunately molecular electronic spectra never appear as Gaussian. The superposition of Gaussian curves needs more than three parameters, besides it suggests several numbers of transitions, which may not be present. It is a point why does the log-normal distribution fit so well to the description of so many molecular systems?

In this work, we address two critical aspects of calculation of the free energy differences in molecular systems from molecular simulations. The first aspect involves checking whether the calculated free energy difference depends significantly on the extent of perturbation used for accomplishment of a given transformation. The second aspect of interest is to verify if the sampling errors in calculating the free energy differences between the wild-type molecule and a mutated one in its free state and in a complex are similar, or not, for a finite-length dynamic simulation. The reliability of the free energy estimates obtained from molecular simulations using thermodynamic cycles depends in part on this fact. For investigating these aspects, we use a self-transformation scheme in which a transformation of a part of a molecular system into itself is considered. We perform MD simulations of DNA fragments in which a part of a specific base is subjected to such a self-transformation. Results indicate that the estimated free energy differences do not depend significantly on the extent of perturbation used to achieve the transformation. Interestingly, the variation in the cumulative free energy difference, ΔA, with the coupling parameter, λ, depends significantly on the extent of perturbation. We examine the physical basis of the observed nature of the variation of the accumulated free energy difference, ΔA, against the λ value in the case of a self-transformation. In a thermodynamic cycle, the sampling errors due to the finite-length simulation for the molecular system are found to be similar to each other for the two perturbations (free and in a complex) justifying the use of such approach in calculating ΔΔA in molecular complexes. ©1999 John Wiley & Sons, Inc. J Comput Chem 20: 877-885, 1999.