Janus particles provide an asymmetry in structure, which can impart diverse functionalities leading to immense importance in various applications, ranging from targeted delivery to interfacial phenomena, including catalysis, electronics, and optics. In this work, we present results of a molecular dynamics study of the growth mechanism of coating on gold nanoparticles (AuNPs) from droplets of n-alkyl thiols with different chain lengths (C5 and C11) and terminal groups (CH3 and COOH). The effect of chain lengths and functional groups on the formation of a monolayer of alkyl thiols on AuNPs is investigated. A two-step mechanism, initiated by the binding of the droplet to the nanoparticle surface with a time constant on the order of similar to 1 ns, followed by the diffusion-driven growth with a larger time constant (on the order of 100 ns), is shown to capture the growth dynamics of the monolayer. It is observed that the time required for complete wetting increases with an increase in the chain length. Moreover, the monolayer formation is slowed down in the presence of carboxyl groups because of strong hydrogen bonding. The kinetics of the n-alkyl thiols coating on the nanoparticles is found to be independent of the droplet size but carboxyl-terminated thiols spread more with increasing droplet size. Furthermore, different time constants for different chains and functional groups yield Janus coating when two droplets of alkyl thiols with different terminal groups are allowed to form monolayers on the nanoparticle. The Janus balance (beta) for different combinations of alkyl thiols and nanoparticle sizes varies in the range of 0.42-0.71.

Prediction of new proteins/enzymes is a main goal in drug development. In this chapter we introduce a new methodology to predict enzyme subclasses based on a new 2D approach. In this contest, Randic, Liao, Nandy, Basak, and many others developed some special types of graph-based representations for pseudofolding process of sequences guided by simple heuristics. These include geometrical constraints to node positioning (sequence pseudofolding rules) in 2D space, leading to final geometrical shapes that resemble latticelike patterns. Lattice networks have been used in the past to visually depict DNA and protein sequences, but they are very flexible. In fact, we can use this technique to create string pseudofolding lattice representations for any kind of string data. In this work, we carried out a statistical analysis of 50,000+ cases to seek and validate a new quantitative structure-activity relationship-like predictor for enzyme subclasses using a machine learning approach. The model uses spectral moments, entropy, and mean potential of pseudofolding lattice graphs as inputs. In this work we report the five best models that we found.

Ionic liquids (ILs) possess a unique physicochemical profile providing a wide range of applications. Their almost limitless structural possibilities allow the design of task-specific ILs. However, their "greenness,"specifically their claimed relative nontoxicity has been frequently questioned, hindering their REACH registration processes and, so, their final application. Since the most of ionic liquids has yet to be synthesized, the development of chemoinformatic tools allowing the efficient profiling of the hazardous potential of these compounds becomes critical. In this sense, the combined use of multiple base classifiers (ensembles or multiclassifiers) have proved to overcome the classification performance limitations associated to the use of single classifiers. In the present work we report two ensembles models with good predictive capabilities in a validation set of ionic liquids never used in the learning process. These models were obtained as part of Quantitative Structure Activity Relationship's studies (QSAR) applied to the characterization of neurotoxic profile of ionic liquids based on its inhibition of the Acetyl cholinesterase enzyme (AChE) as neurotoxicity indicator. The results obtained show that one can expect that at least 96% of a set of news ionic liquids can be correctly classified using theses ensembles models. Consequently, these chemoinformatics models provides efficient decision making tools in the design and development of new "green" ionic liquids.

Molecular modeling indicates the general process of describing complex chemical systems in terms of a realistic atomic model, with the goal being to understand and predict macroscopic properties based on detailed knowledge on an atomic scale. Often, molecular modeling is used to design new materials, for which the accurate prediction of physical properties of realistic systems is required. These properties could be divided in two main groups: static equilibrium properties, like the binding constant of a drug to a receptor, and dynamic or non-equilibrium properties, like the diffusion of molecules through two phases or reaction kinetics and so on. Due to the great variety of techniques we will carefully choose the most appropriate to our problem. In any case, the most accurate is the so called ab initio which uses. © 2017 by Taylor & Francis Group, LLC.

Molecular dynamics simulations of a coarse-grained model are used to study the formation mechanism of periodic mesoporous silica over a wide range of cationic surfactant concentrations. This follows up on an earlier study of systems with low surfactant concentrations. We started by studying the phase diagram of the surfactant-water system and found that our model shows good qualitative agreement with experiments with respect to the surfactant concentrations where various phases appear. We then considered the impact of silicate species upon the morphologies formed. We have found that even in concentrated surfactant systems-in the concentration range where pure surfactant solutions yield a liquid crystal phase-the liquid-crystal templating mechanism is not viable because the preformed liquid crystal collapses as silica monomers are added into the solution. Upon the addition of silica dimers, a new phase separated hexagonal array is formed. The preformed liquid crystals were found to be unstable in the presence of monomeric silicates. In addition, the silica dimer is found to be essential for mesoscale ordering at both low and high surfactant concentrations. Our results support the view that a cooperative interaction of anionic silica oligomers and cationic surfactants determines the mesostructure formation in the M41S family of materials.

In this work we developed a new force field model (FFM) for propylene glycol (PG) based on the OPLS all-atom potential. The OPLS potential was refined using quantum chemical calculations, taking into account the densities and self-diffusion coefficients. The validation of this new FFM was carried out based on a wide range of physicochemical properties, such as density, enthalpy of vaporization, self-diffusion coefficients, isothermal compressibility, surface tension, and shear viscosity. The molecular dynamics (MD) simulations were performed over a large range of temperatures (293.15-373.15 K). The comparison with other force field models, such as OPLS, CHARMM27, and GAFF, revealed a large improvement of the results, allowing a better agreement with experimental data. Specific structural properties (radial distribution functions, hydrogen bonding and spatial distribution functions) were then analyzed in order to support the adequacy of the proposed FFM. Pure propylene glycol forms a continuous phase, displaying no microstructures. It is shown that the developed FFM gives rise to suitable results not only for pure propylene glycol but also for mixtures by testing its behavior for a 50 mol % aqueous propylene glycol solution. Furthermore, it is demonstrated that the addition of water to the PG phase produces a homogeneous solution and that the hydration interactions prevail over the propylene glycol self-association interactions.

On the basis of our earlier work, fortyone 5-N-substituted-2N-(substituted benzenesulphonyl)-L(+)glutamines were synthesized and screened for cancer cell inhibitory activity. The best active compounds showed 91% tumor cell inhibition, whereas other three compounds showed more than 80% inhibition. Two-dimensional quantitative structure–activity relationship modeling and three-dimensional quantitative structure–activity relationship k-nearest neighbor molecular field analysis studies were done to get an insight into structural requirements toward further improved anticancer activity. Considering the fact that these compounds are competitive inhibitors of glutaminase, a molecular docking study followed by molecular dynamic simulation analysis were performed. The work may help to develop new anticancer agents. © 2017, Springer Science+Business Media New York.