The paper discusses the development of a multiscale computational model for predicting the permeability of multilayer protective films consisting of multiple polymeric and hybrid layers containing clay minerals as fillers. The presented approach combines three levels of computation: continuous, full atomic, and quantitative structure–property correlations (QSPR). Oxygen and water are chosen as penetrant molecules. The main predictions are made using the continuum model, which takes into account the real scales of films and nanoparticles. It is shown that reliable predictions of the permeability coefficients can be obtained for oxygen molecules, which is not always possible for water. The latter requires the refinement of existing QSPR methods and interatomic interaction potentials for the atomistic level of calculations. Nevertheless, we show that the maximum effect on permeability reduction from the addition of clay fillers to the hybrid layer can be achieved by using nanoparticles with large aspect ratios and a high degree of orientational order. In addition, the use of the hybrid layer should be combined with the use of polymer layers with minimal oxygen and water permeability. The constructed model can be used to improve the properties of protective coatings for food and drug storage and to regulate the gas permeability of polymeric materials.

We present a theoretical approach for the in silico generation of new polymer structures for the systematic search for new materials with advanced properties. It is based on Bicerano’s Regression Model (RM), which uses the structure of the smallest repeating unit (SRU) for fast and adequate prediction of polymer properties. We have developed the programs (a) GenStruc, for generating the new polymer SRUs using the enumeration and Monte Carlo algorithms, and (b) PolyPred, for predicting properties for a given input polymer as well as for multiple structures stored in the database files. The structure database from the original Bicerano publication is used to create databases of backbones and pendant groups. A database of 5,142,153 unique SRUs is generated using the scaffold-based combinatorial method. We show that using only known backbones of the polymer SRU and varying the pendant groups can significantly improve the predicted extreme values of polymer properties. Analysis of the obtained results for the dielectric constant and refractive index shows that the values of the dielectric constant are higher for polyhydrazides than for polyhydroxylamines. The high value predicted for the refractive index of polythiophene and its derivatives is in agreement with the experimental data.

Here, we present a computational package for collaborative distributed design of new nanocomposite materials using multi-level modeling technology by one user and a group of engineers or researchers. It contains a high-level set of integrated, versatile tools to simulate and analyze elastomeric nanocomposites, consisting of cross-linked linear/branched copolymers containing different discrete fillers. The package can also be used to study the formation and properties of nanocomposite interpenetrating polymer networks and microphase-separated organic-inorganic hybrid materials.

We present a MULTICOMP package developed for multiscale modeling of polymer-based nanomaterials. The package implements GUI-based tools for the automatic construction of various types of polymer systems with the transition from the atomistic level of modeling to the mesolevel and from the mesolevel to the macrolevel. Using automated scripts, researchers can construct different kinds of nanomaterials, create flexible simulation schemas and define how data are transferred between engaged software modules and tools. The package makes it possible to analyze structural, thermophysical, mechanical properties, the cohesive energy density, the viscosity of polymer melts, and the diffusion of small molecules. Due to the client-server organization, the package can perform calculations on local and remote computing facilities and transfer the most time-consuming calculations to supercomputers. Examples of preparation and characterization of a cross-linked polymer matrix, a clay-based nanocomposite, and analyzing their properties illustrate MULTICOMP package operation.

Two new features are added to existing algorithms for kekulization of chemical structures, i.e., handling of triple and cumulene bonds in cycles and use of random atom sorting to remove unmatched atoms. Handling of triple and cumulene bonds enables kekulization of graphynes and graphdiynes. Random sorting speeds up the calculation time, i.e., kekulization of large chemical structures containing about 107 atoms takes ≤1 min on a typical PC. Source codes (Pascal, GNU GPL license) are included as a compiled application (Windows 64). Calculation times and unmatched atom statistics are provided for graphenes, graphynes, nanotubes, graphyne nanotubes and fullerenes. Benchmark comparisons are made for some data.

Novel logarithmic time algorithm is proposed for determining the order of chemical bonds from known valences of the atoms. The algorithm has the order of complexity N · log(N) and is applicable to polycyclic compounds containing a combination of the cycles of any size and the atoms with the valences ≤4 in cycle nodes. The algorithm is applicable to structures containing triple and cumulene bonds in the cycles. It was tested for graphene, C[12,12] nanotubes, graphyne-GY1, graphyne-GY7, graphyne-like nanotube, fullerenes C20, C60, C70, C80, C82 and their aza-analogs, polypentadienes, as well as for porphine. Determining the order of bonds in graphene, graphynes or nanotubes, containing >107 atoms, took less than 2 min on a personal computer. For the compounds containing aromatic cycles with an odd number of atoms, this algorithm becomes probabilistic and successful determination of bond orders is not guaranteed. However, the probability of successful determination of bond order is significant, and Kekulé structures, if they existed, were generated for all studied fullerenes and their aza-analogs.

Chemical structure provides exhaustive description of a compound, but it is often proprietary and thus an impediment in the exchange of information. For example, structure disclosure is often needed for the selection of most similar or dissimilar compounds. Authors propose a centroidal algorithm based on structural fragments (screens) that can be efficiently used for the similarity and diversity selections without disclosing structures from the reference set. For an increased security purposes, authors recommend that such set contains at least some tens of structures. Analysis of reverse engineering feasibility showed that the problem difficulty grows with decrease of the screen’s radius. The algorithm is illustrated with concrete calculations on known steroidal, quinoline, and quinazoline drugs. We also investigate a problem of scaffold identification in combinatorial library dataset. The results show that relatively small screens of radius equal to 2 bond lengths perform well in the similarity sorting, while radius 4 screens yield better results in diversity sorting. The software implementation of the algorithm taking SDF file with a reference set generates screens of various radii which are subsequently used for the similarity and diversity sorting of external SDFs. Since the reverse engineering of the reference set molecules from their screens has the same difficulty as the RSA asymmetric encryption algorithm, generated screens can be stored openly without further encryption. This approach ensures an end user transfers only a set of structural fragments and no other data. Like other algorithms of encryption, the centroid algorithm cannot give 100% guarantee of protecting a chemical structure from dataset, but probability of initial structure identification is very small-order of 10−40 in typical cases.

Efficient recognition of tautomeric compound forms in large corporate or commercially available compound databases is a difficult and labor intensive task. Our data indicate that up to 0.5% of commercially available compound collections for bioscreening contain tautomers. Though in the large registry databases, such as Beilstein and CAS, the tautomers are found in an automated fashion using high-performance computational technologies, their real-time recognition in the nonregistry corporate databases, as a rule, remains problematic. We have developed an effective algorithm for tautomer searching based on the proprietary chemoinformatics platform. This algorithm reduces the compound to a canonical structure. This feature enables rapid, automated computer searching of most of the known tautomeric transformations that occur in databases of organic compounds. Another useful extension of this methodology is related to the ability to effectively search for different forms of compounds that contain ionic and semipolar bonds. The computations are performed in the Windows environment on a standard personal computer, a very useful feature. The practical application of the proposed methodology is illustrated by several examples of successful recovery of tautomers and different forms of ionic compounds from real commercially available nonregistry databases.

Some modifications were introduced into the previously described Centroid diversity sorting algorithm, which uses cosine similarity metric. The modified algorithm is suitable for the work with large databases on personal computers. For example, for diversity sorting of the database with the size greater than a million of records, less than 9 h are required (Pentium III, 800 MHz). The problem of selecting new compounds into the existing collection is examined to reach the maximum diversity of the collection. The article describes the new algorithm for the selection of heterocyclic compounds.