A computational structure–property relationship study of glass transition temperatures for a diverse set of polymers

Phenolic Acid-β-Cyclodextrin Complexation Study to Mask Bitterness in Wheat Bran: A Machine Learning-Based QSAR Study

The need to solvate and encapsulate hydro-sensitive molecules drives noticeable trends in the applications of cyclodextrins in the pharmaceutical industry, in foods, polymers, materials, and in agricultural science. Among them, β-cyclodextrin is one of the most used for the entrapment of phenolic acid compounds to mask the bitterness of wheat bran. In this regard, there is still a need for good data and especially for a robust predictive model that assesses the bitterness masking capabilities of β-cyclodextrin for various phenolic compounds. This study uses a dataset of 20 phenolic acids docked into the β-cyclodextrin cavity to generate three different binding constants. The data from the docking study were combined with topological, topographical, and quantum-chemical features from the ligands in a machine learning-based structure-activity relationship study. Three different models for each binding constant were computed using a combination of the genetic algorithm (GA) and multiple linear regression (MLR) approaches. The developed ML/QSAR models showed a very good performance, with high predictive ability and correlation coefficients of 0.969 and 0.984 for the training and test sets, respectively. The models revealed several factors responsible for binding with cyclodextrin, showing positive contributions toward the binding affinity values, including such features as the presence of six-membered rings in the molecule, branching, electronegativity values, and polar surface area.

POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles

Block copolymers, composed of poly(2-oxazoline)s and poly(2-oxazine)s, can serve as drug delivery systems; they form micelles that carry poorly water-soluble drugs. Many recent studies have investigated the effects of structural changes of the polymer and the hydrophobic cargo on drug loading. In this work, we combine these data to establish an extended formulation database. Different molecular properties and fingerprints are tested for their applicability to serve as formulation-specific mixture descriptors. A variety of classification and regression models are built for different descriptor subsets and thresholds of loading efficiency and loading capacity, with the best models achieving overall good statistics for both cross- and external validation (balanced accuracies of 0.8). Subsequently, important features are dissected for interpretation, and the DrugBank is screened for potential therapeutic use cases where these polymers could be used to develop novel formulations of hydrophobic drugs. The most promising models are provided as an open-source software tool for other researchers to test the applicability of these delivery systems for potential new drug candidates.

Quantitative Structure-Permittivity Relationship Study of a Series of Polymers

Dielectric constant is an important property which is widely utilized in many scientific fields and characterizes the degree of polarization of substances under the external electric field. In this work, a structure-property relationship of the dielectric constants (ε) for a diverse set of polymers was investigated. A transparent mechanistic model was developed with the application of a machine learning approach that combines genetic algorithm and multiple linear regression analysis, to obtain a mechanistically explainable and transparent model. Based on the evaluation conducted using various validation criteria, four- and eight-variable models were proposed. The best model showed a high predictive performance for training and test sets, with R2 values of 0.905 and 0.812, respectively. Obtained statistical performance results and selected descriptors in the best models were analyzed and discussed. With the validation procedures applied, the models were proven to have a good predictive ability and robustness for further applications in polymer permittivity prediction.

Combined Machine Learning, Computational, and Experimental Analysis of the Iridium(III) Complexes with Red to Near-Infrared Emission

Various coordination complexes have been the subject of experimental and theoretical studies in recent decades because of their fascinating photophysical properties. In this work, a combined experimental and computational approach was applied to investigate the optical properties of monocationic Ir(III) complexes. An interpretative machine learning-based quantitative structure-property relationship (ML/QSPR) model was successfully developed that could reliably predict the emission wavelength of the Ir(III) complexes and provide a foundation for the theoretical evaluation of the optical properties of Ir(III) complexes. A hypothesis was proposed to explain the differences in the emission wavelengths between structurally different individual Ir(III) complexes. The efficacy of the developed model was demonstrated by high R2 values of 0.84 and 0.87 for the training and test sets, respectively. It is worth noting that a relationship between the N-N distance in the diimine ligands of the Ir(III) complexes and emission wavelengths is detected. This effect is most probably associated with a degree of distortion in the octahedral geometry of the complexes, resulting in a perturbed ligand field. This combined experimental and computational approach shows great potential for the rational design of new Ir(III) complexes with the desired optical properties. Moreover, the developed methodology could be extended to other transition-metal complexes.

Atomistic Simulations of the Permeability and Dynamic Transportation Characteristics of Diamond Nanochannels

Through atomistic simulations, this work investigated the permeability of hexagonal diamond nanochannels for NaCl solution. Compared with the multilayer graphene nanochannel (with a nominal channel height of 6.8 Å), the diamond nanochannel exhibited better permeability. The whole transportation process can be divided into three stages: the diffusion stage, the transition stage and the flow stage. Increasing the channel height reduced the transition nominal pressure that distinguishes the diffusion and flow stages, and improved water permeability (with increased water flux but reduced ion retention rate). In comparison, channel length and solution concentration exerted ignorable influence on water permeability of the channel. Further simulations revealed that temperature between 300 and 350 K remarkably increased water permeability, accompanied by continuously decreasing transition nominal pressure. Additional investigations showed that the permeability of the nanochannel could be effectively tailored by surface functionalization. This work provides a comprehensive atomic insight into the transportation process of NaCl solution in a diamond nanochannel, and the established understanding could be beneficial for the design of advanced nanofluidic devices.

M SB, George JJ, Rasulev B, Raghavan P. Experimental and Simulation Studies on Nonwoven Polypropylene-Nitrile Rubber Blend: Recycling of Medical Face Masks to an Engineering Product

The battle against the COVID-19 pandemic counters the waste management system, as billions of single-use face masks are used per day all over the world. Proper disposal of used face masks without jeopardizing the health and the environment is a challenge. Herein, a novel method for recycling of medical face masks has been studied. This method incorporates the nonwoven polypropylene (PP) fiber, which is taken off from the mask after disinfecting it, with acrylonitrile butadiene rubber (NBR) using maleic anhydride as the compatibilizer, which results in a PP-NBR blend with a high percentage economy. The PP-NBR blends show enhanced thermomechanical properties among which, 70 wt % PP content shows superior properties compared to other composites with 40, 50, and 60 wt % of PP. The fully Atomistic simulation of PP-NBR blend with compatibilizer shows an improved tensile and barrier properties, which is in good agreement with the experimental studies. The molecular dynamics simulation confirms that the compatibility between non-polar PP and polar NBR phases are vitally important for increasing the interfacial adhesion and impeding the phase separation.

QSPR modelling for intrinsic viscosity in polymer-solvent combinations based on density functional theory

Linear and nonlinear quantitative structure-property relationship (QSPR) models were developed based on a dataset with 65 polymer-solvent combinations. Seven quantum chemical descriptors, dipole moment, hardness, chemical potential, electrophilicity index, total energy, HOMO and LUMO orbital energies, were calculated with density functional theory at the B3LYP/6-31 G(d) level for polymers and solvents. Considering the strong correlation between intrinsic viscosity and weight, size, shape as well as topological structure of polymers and solvents, topological descriptors were also applied in this work. Meanwhile, the most appropriate polymer structure representation was investigated by considering 1-5 monomeric repeating units. The molecular descriptors were first screened by using the genetic algorithms-multiple linear regression (GA-MLR), with coefficient of determinations (r2) of 0.78 and 0.83 for the training set and the prediction set, respectively. The support vector machine model (SVM) model based on the selected descriptors subset showed a r2 value of 0.95 for the training set and 0.93 for the prediction set. All statistical results suggest that the established QSPR models have good predictability. Furthermore, a new test set obtained from the literature was used for further validation. The r2 values were 0.81 for the MLR model and 0.90 for the SVM model.

Modified Group Contribution Scheme to Predict the Glass-Transition Temperature of Homopolymers through a Limiting Property Dataset

Previous studies on glass-transition temperature (T _g) prediction mainly focus on developing diverse methods with higher regression accuracy, but very little attention has been paid to the dataset. Generally, a large range of T _g values of a specified polymer could be found in the literature but which one should be selected into a dataset merely depends on the implicit preference rather than a recognized and clear criterion. In this paper, limiting glass-transition temperature (T _g(∞)), a constant value obtained at the infinite number-average molecular weight M _n, was validated to be an adequate bridge index in the T _g prediction models. Furthermore, a new dataset containing 198 polymers was established to predict T _g(∞) using the improved group contribution method and it showed a good correlation (R ² = 0.9925, adjusted R ² = 0.9894). The method could also generate T _g-M _n curves by introducing the T _g(∞) function and provide more information to polymer scientists and engineers for material selection, product design, and synthesis.

Feature Selection for Polymer Informatics: Evaluating Scalability and Robustness of the FS4RVDD Algorithm Using Synthetic Polydisperse Data Sets

The feature selection (FS) process is a key step in the Quantitative Structure-Property Relationship (QSPR) modeling of physicochemical properties in cheminformatics. In particular, the inference of QSPR models for polymeric material properties constitutes a complex problem because of the uncertainty introduced by the polydispersity of these materials. The main challenge is how to capture the polydispersity information from the molecular weight distribution (MWD) curve to achieve a more effective computational representation of polymeric materials. To date, most of the existing QSPR techniques use only a single molecule to represent each of these materials, but polydispersity is not considered. Consequently, QSPR models obtained by these approaches are being oversimplified. For this reason, we introduced in a previous work a new FS algorithm called Feature Selection for Random Variables with Discrete Distribution (FS4RV_DD), which allows dealing with polydisperse data. In the present paper, we evaluate both the scalability and the robustness of the FS4RV_DD algorithm. In this sense, we generated synthetic data by varying and combining different parameters: the size of the database, the cardinality of the selected feature subsets, the presence of noise in the data, and the type of correlation (linear and nonlinear). Moreover, the performances obtained by FS4RV_DD were contrasted with traditional FS techniques applied to different simplified representations of polymeric materials. The obtained results show that the FS4RV_DD algorithm outperformed the traditional FS methods in all proposed scenarios, which suggest the need of an algorithm such as FS4RV_DD to deal with the uncertainty that polydispersity introduces in human-made polymers.

Consensus QSPR modelling for the prediction of cellular response and fibrinogen adsorption to the surface of polymeric biomaterials

In the current study, we have developed predictive quantitative structure-activity relationship (QSAR) models for cellular response (foetal rate lung fibroblast proliferation) and protein adsorption (fibrinogen adsorption (FA)) on the surface of tyrosine-derived biodegradable polymers designed for tissue engineering purpose using a dataset of 66 and 40 biodegradable polymers, respectively, employing two-dimensional molecular descriptors. Best four individual models have been selected for each of the endpoints. These models are developed using partial least squares regression with a unique combination of six and four descriptors for cellular response and protein adsorption, respectively. The generated models were strictly validated using internal and external metrics to determine the predictive ability and robustness of proposed models. Subsequently, the validated individual models for each response endpoints were used for the generation of 'intelligent' consensus models ( http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/ ) to improve the quality of predictions for the external data set. These models may help in prediction of virtual polymer libraries for rational design/optimization for properties relevant to biomedical applications prior to their synthesis.

QSPR modelling for prediction of glass transition temperature of diverse polymers

The glass transition temperature is a vital property of polymers with a direct impact on their stability. In the present study, we built quantitative structure-property relationship models for the prediction of the glass transition temperatures of polymers using a data set of 206 diverse polymers. Various 2D molecular descriptors were computed from the single repeating units of polymers. We derived five models from different combinations of six descriptors in each case by employing the double cross-validation technique followed by partial least squares regression. The selected models were subsequently validated by methods such as cross-validation, external validation using test set compounds, the Y-randomization (Y-scrambling) test and an applicability domain study of the developed models. All of the models have statistically significant metric values such as r² ranging from 0.713-0.759, Q² ranging from 0.662-0.724 and [Formula: see text] ranging 0.702-0.805. Finally, a comparison was made with recently published models, though the previous models were based on a much smaller data set with limited diversity. We also used a true external set to demonstrate the performance of our developed models, which may be used for the prediction and design of novel polymers prior to their synthesis.