Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction

Computational study and in vitro alpha-glucosidase inhibitory effects of medicinal plants from a Thai folk remedy

The number of patients with type 2 diabetes mellitus (T2DM) has increased worldwide. Although an instant cure was achieved with the standard treatment acabose, unsatisfactory symptoms associated with cardiovascular disease after acabose administration have been reported. Therefore, it is important to explore new treatments. A Thai folk recipe has long been used for T2DM treatment, and it effectively decreases blood glucose. However, the mechanism of this recipe has never been proven. Therefore, the potential anti-T2DM effect of this recipe, which is used in Thai hospitals, was determined to involve alpha-glucosidase (AG) inhibition with a half maximal inhibitory concentration (IC50). In vitro experiments showed that crude Cinnamomum verum extract (IC50 = 0.35 ± 0.12 mg/mL) offered excellent inhibitory activity, followed by extracts from Tinospora crispa (IC50 = 0.69 ± 0.39 mg/mL), Stephania suberosa (IC50 = 1.50 ± 0.17 mg/mL), Andrographis paniculate (IC50 = 1.78 ± 0.35 mg/mL), and Thunbergia laurifolia (IC50 = 4.66 ± 0.27 mg/mL). However, the potencies of these extracts were lower than that of acabose (IC50 = 0.55 ± 0.11 mg/mL). Therefore, this study investigated and developed a formulation of this recipe using computational docking. Among 61 compounds, 7 effectively inhibited AG, including chlorogenic acid (IC50 = 819.07 pM) through 5 hydrogen bonds (HBs) and 2 hydrophobic interactions (HIs); β-sitosterol (IC50 = 4.46 nM, 6 HIs); ergosterol peroxide (IC50 = 4.18 nM, 6 HIs); borapetoside D (IC50 = 508.63 pM, 7 HBs and 2 HIs); borapetoside A (IC50 = 1.09 nM, 2 HBs and 2 His), stephasubimine (IC50 = 285.37 pM, 6 HIs); and stephasubine (IC50 = 1.09 nM, 3 HBs and 4 HIs). These compounds bind with high affinity to different binding pockets, leading to additive effects. Moreover, the pharmacokinetics of six of these seven compounds (except ergosterol peroxide) showed poor absorption in the gastrointestinal tract, which would allow for competitive binding to AG in the small intestine. These results indicate that the development of these 6 compounds into oral antidiabetic agents is promising.

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.

PharmDE: A new expert system for drug-excipient compatibility evaluation

Drug-excipient compatibility study is the essential basis for excipient selection at the pre-formulation stage. According to the pharmaceutical Quality by Design (QbD) principles, a comprehensive understanding of the ingredients' physicochemical properties and a theoretical evaluation of the interaction risk between the drugs and excipients are required for conducting rational compatibility experimental design. Currently, there is an urgent need to establish an artificial intelligence system for researchers to easily get through the problem because it is very inconvenient and hard to utilize those drug-excipient incompatibility data scattered in scientific literature. Here, we designed a knowledge-driven expert system named PharmDE for drug-excipient incompatibility risk evaluation. PharmDE firstly developed an information-rich database to store incompatibility data, covering 532 data items from 228 selected articles. Then, 60 drug-excipient interaction rules were created based on our knowledge and formulation research experiences. Finally, the expert system was developed by organically integrating the database searching and rule-based incompatibility risk prediction, which resulted in four main functionalities: basic search of incompatibility database, data matching by similarity search, drug incompatibility risk evaluation, and formulation incompatibility risk evaluation. PharmDE is expected to be a useful tool for drug-excipient compatibility study and accelerate drug formulation design. It is now freely available at https://pharmde.computpharm.org.

In silico prediction of polyethylene-aqueous and air partition coefficients of organic contaminants using linear and nonlinear approaches

Low-density polyethylene (LDPE) passive sampling is very attractive for use in determining chemicals concentrations. Crucial to the measurement is the coefficient (K_PE) describing partitioning between LDPE and environmental matrices. 255, 117 and 190 compounds were collected for the development of datasets in three different matrices, i.e., water, air and seawater, respectively. Further, 3 pp-LFER models and 9 QSPR models based on classical multiple linear regression (MLR) coupled with prevalent nonlinear algorithms (artificial neural network, ANN and support vector machine, SVM) were performed to predict LDPE-water (K_PE-W), LDPE-air (K_PE-A) and LDPE-seawater (K_PE-SW) partition coefficients. These developed models have satisfying predictability (R²_adj: 0.805-0.966, 0.963-0.991 and 0.817-0.941; RMSE_tra: 0.233-0.565, 0.200-0.406 and 0.260-0.459) and robustness (Q²_ext: 0.840-0.943, 0.968-0.984 and 0.797-0.842; RMSE_ext: 0.308-0.514, 0.299-0.426 and 0.407-0.462) in three datasets (water, air and seawater), respectively. In particular, the reasonable mechanism interpretations revealed that the molecular size, hydrophobicity, polarizability, ionization potential, and molecular stability were the most relevant properties, for governing chemicals partitioning between LDPE and environmental matrices. The application domains (ADs) assessed here exhibited the satisfactory applicability. As such, the derived models can act as intelligent tools to predict unknown K_PE values and fill the experimental gaps, which was further beneficial for the construction of enormous and reliable database to facilitate a distinct understanding of the distribution for organic contaminants in total environment.

SAMPL7 blind challenge: quantum-mechanical prediction of partition coefficients and acid dissociation constants for small drug-like molecules

The physicochemical properties of a drug molecule determine the therapeutic effectiveness of the drug. Thus, the development of fast and accurate theoretical approaches for the prediction of such properties is inevitable. The participation to the SAMPL7 challenge is based on the estimation of logP coefficients and pK_a values of small drug-like sulfonamide derivatives. Thereby, quantum mechanical calculations were carried out in order to calculate the free energy of solvation and the transfer energy of 22 drug-like compounds in different environments (water and n-octanol) by employing the SMD solvation model. For logP calculations, we studied eleven different methodologies to calculate the transfer free energies, the lowest RMSE value was obtained for the M06L/def2-TZVP//M06L/def2-SVP level of theory. On the other hand, we employed an isodesmic reaction scheme within the macro pK_a framework; this was based on selecting reference molecules similar to the SAMPL7 challenge molecules. Consequently, highly well correlated pK_a values were obtained with the M062X/6-311+G(2df,2p)//M052X/6-31+G(d,p) level of theory.

Recent Experimental Developments in Studying Passive Membrane Transport of Drug Molecules

The ability to measure the passive membrane permeation of drug-like molecules is of fundamental biological and pharmaceutical importance. Of significance, passive diffusion across the cellular membranes plays an effective role in the delivery of many pharmaceutical agents to intracellular targets. Hence, approaches for quantitative measurement of membrane permeability have been the topics of research for decades, resulting in sophisticated biomimetic systems coupled with advanced techniques. In this review, recent developments in experimental approaches along with theoretical models for quantitative and real-time analysis of membrane transport of drug-like molecules through mimetic and living cell membranes are discussed. The focus is on time-resolved fluorescence-based, surface plasmon resonance, and second-harmonic light scattering approaches. The current understanding of how properties of the membrane and permeant affect the permeation process is discussed.

Support vector regression-based QSAR models for prediction of antioxidant activity of phenolic compounds

The Support vector regression (SVR) was used to investigate quantitative structure-activity relationships (QSAR) of 75 phenolic compounds with Trolox-equivalent antioxidant capacity (TEAC). Geometric structures were optimized at the EF level of the MOPAC software program. Using Pearson correlation coefficient analysis, four molecular descriptors [n(OH), Cosmo Area (CA), Core-Core Repulsion (CCR) and Final Heat of Formation (FHF)] were selected as independent variables. The QSAR model was developed from the training set consisting of 57 compounds and then used the leave-one-out cross-validation (LOOCV) correlation coefficient to evaluate the prediction ability of the QSAR model. Used Artificial neural network (ANN) and multiple linear regression (MLR) for comparing. The RMSE (root mean square error) values of LOOCV in SVR, ANN and MLR models were 0.44, 0.46 and 0.54. The RMSE values of prediction of external 18 compounds were 0.41, 0.39 and 0.54 for SVR, ANN and MLR models, respectively. The obtained result indicated that the SVR models exhibited excellent predicting performance and competent for predicting the TEAC of phenolic compounds.

Predicting aqueous solubility by QSPR modeling

The aqueous solubility is predicted here using quantitative structure property relationship (QSPR) models. In this study, we examine whether descriptors that individually yield favorable models for the prediction of the Gibbs energy of solvation and sublimation can be used in combination with octanol-water partition coefficient to produce QSPR models for the prediction of aqueous solubility. Based on this strategy, applied to seven distinct datasets, all models exhibited an R² greater than 0.7 and Q² greater than 0.6 for the estimation of aqueous solubility. We also determined how uncoupling the descriptors used to create QSPR models in the prediction of Gibbs energy of sublimation yielded an improved model. Model refinement using an artificial neural network applying the same descriptors generated significantly better models with improved R² and standard deviation.

Machine learning discovery of high-temperature polymers

To formulate a machine learning (ML) model to establish the polymer's structure-property correlation for glass transition temperatureT g , we collect a diverse set of nearly 13,000 real homopolymers from the largest polymer database, PoLyInfo. We train the deep neural network (DNN) model with 6,923 experimentalT g values using Morgan fingerprint representations of chemical structures for these polymers. Interestingly, the trained DNN model can reasonably predict the unknownT g values of polymers with distinct molecular structures, in comparison with molecular dynamics simulations and experimental results. With the validated transferability and generalization ability, the ML model is utilized for high-throughput screening of nearly one million hypothetical polymers. We identify more than 65,000 promising candidates withT g > 200°C, which is 30 times more than existing known high-temperature polymers (∼2,000 from PoLyInfo). The discovery of this large number of promising candidates will be of significant interest in the development and design of high-temperature polymers.

Predicting Kováts Retention Indices Using Graph Neural Networks

The Kováts retention index is a dimensionless quantity that characterizes the rate at which a compound is processed through a gas chromatography column. This quantity is independent of many experimental variables and, as such, is considered a near-universal descriptor of retention time on a chromatography column. The Kováts retention indices of a large number of molecules have been determined experimentally. The "NIST 20: GC Method/Retention Index Library" database has collected and, more importantly, curated retention indices of a subset of these compounds resulting in a highly valued reference database. The experimental data in the library form an ideal data set for training machine learning models for the prediction of retention indices of unknown compounds. In this article, we describe the training of a graph neural network model to predict the Kováts retention index for compounds in the NIST library and compare this approach with previous work [1]. We predict the Kováts retention index with a mean unsigned error of 28 index units as compared to 44, the putative best result using a convolutional neural network [1]. The NIST library also incorporates an estimation scheme based on a group contribution approach that achieves a mean unsigned error of 114 compared to the experimental data. Our method uses the same input data source as the group contribution approach, making its application straightforward and convenient to apply to existing libraries. Our results convincingly demonstrate the predictive powers of systematic, data-driven approaches leveraging deep learning methodologies applied to chemical data and for the data in the NIST 20 library outperform previous models.

Mechanistic selectivity investigation and 2D-QSAR study of some new antiproliferative pyrazoles and pyrazolopyridines as potential CDK2 inhibitors

Novel series of diphenyl-1H-pyrazoles (4a-g) and pyrazolo[3,4-b]pyridines (5a-g and 7a-i) were synthesized and evaluated for their antiproliferative activity against breast cancer cell line (MCF7) and Hepatocellular carcinoma cell line (HepG2). The highest MCF7 growth inhibition activity was attained via compounds 4f and 7e (IC₅₀ = 1.29 and 0.93 μM, respectively), while compounds 5b and 7f were the most active ones against HepG2 (IC₅₀ = 1.57 and 1.33 μM, respectively) compared to doxorubicin (IC₅₀ = 1.88 and 7.30 μM, respectively). Cell cycle analysis showed arrest at S and G2-M phases in MCF7 cells treated with 4f and 7e, and at G2-M and G1/S phases in HepG2 cells treated with 5b and 7f, respectively. Apoptotic effect of compounds 4f, 5b, 7e, and 7f was indicated via their pre-G1 early and late apoptotic effects and augmented levels of caspase-9/MCF7 and caspase-3/HepG2. A worthy safety profile was assessed for compounds 4f and 7e on MCF10A and compounds 5b and 7f on THLE2 treated normal cells. Furthermore, compounds 4f, 5b and 7f displayed a promising selective profile for CDK2 inhibition vs. CDK1, CDK4, and CDK7 isoforms as proved from their selectivity index. Docking in CDK2 ATP binding site, co-crystallized with R-Roscovitine, demonstrated analogous interactions and comparable binding energy with the native ligand. 2D QSAR sighted the possible structural features governing the CDK2 inhibition activity elicited by the studied pyrazolo[3,4-b]pyridines. These findings present compounds 4f, 5b, and 7f as selective CDK2 inhibitors with promising antiproliferative activity against MCF7 and HepG2 cancer cells.

Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy

The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.

Dipole Moment Variation Clears Up Electronic Excitations in the π-Stacked Complexes of Fluorescent Protein Chromophores

We propose a quantitative structure-property relationship (QSPR) model for prediction of spectral tuning in cyan, green, orange, and red fluorescent proteins, which are engineered by motifs of the green fluorescent protein. Protein variants, in which their chromophores are involved in the π-stacking interaction with amino acid residues tyrosine, phenylalanine, and histidine, are prospective markers useful in bioimaging and super-resolution microscopy. In this work, we constructed training sets of the π-stacked complexes of four fluorescent protein chromophores (of the green, orange, red, and cyan series) with various substituted benzenes and imidazoles and tested the use of dipole moment variation upon excitation (DMV) as a descriptor to evaluate the vertical excitation energies in these systems. To validate this approach, we computed and analyzed electron density distributions of the π-stacked complexes and correlated the QSPR predictions with the reference values of the transition energies obtained using the high-level ab initio quantum chemistry methods. According to our results, the use of the DMV descriptor allows one to predict excitation energies in the π-stacked complexes with errors not exceeding 0.1 eV, which makes this model a practically useful tool in the development of efficient fluorescent markers for in vivo imaging.

Chemoinformatics for the Safety of Energetic and Reactive Materials at Ineris

The characterization of physical hazards of substances is a key information to manage the risks associated to their use, storage and transport. With decades of work in this area, Ineris develops and implements cutting-edge experimental facilities allowing such characterizations at different scales and under various conditions to study all of the dreaded accident scenarios. This review presents the efforts engaged by Ineris more recently in the field of chemoinformatics to develop and use new predictive methods for the anticipation and management of industrials risks associated to energetic and reactive materials as a complement to experiments. An overview of the methods used for the development of Quantitative Structure-Property Relationships for physical hazards are presented and discussed regarding the specificities associated to this class of properties. A review of models developed at Ineris is also provided from the first tentative models on the explosivity of nitro compounds to the successful application to the flammability of organic mixtures. Then, a discussion is proposed on the use of QSPR models. Good practices for robust use for QSPR models are recalled with specific comments related to physical hazards, notably for regulatory purpose. Dissemination and training efforts engaged by Ineris are also presented. The potential offered by these predictive methods in terms of in silico design and for the development of new intrinsically safer technologies in safety-by-design strategies is finally discussed. At last, challenges and perspectives to extend the application of chemoinformatics in the field of safety and in particular for the physical hazards of energetic and reactive substances are proposed.

QSAR models for the acute toxicity of 1,2,4-triazole fungicides to zebrafish (Danio rerio) embryos

In recent decades, the 1,2,4-triazole fungicides are widely used for crop diseases control, and their toxicity to wild lives and pollution to ecosystem have attracted more and more attention. However, how to quickly and efficiently evaluate the toxicity of these compounds to environmental organisms is still a challenge. In silico method, such like Quantitative Structure-Activity Relationship (QSAR), provides a good alternative to evaluate the environmental toxicity of a large number of chemicals. At the present study, the acute toxicity of 23 1,2,4-triazole fungicides to zebrafish (Danio rerio) embryos was firstly tested, and the LC₅₀ (median lethal concentration) values were used as the bio-activity endpoint to conduct QSAR modelling for these triazoles. After the comparative study of several QSAR models, the 2D-QSAR model was finally constructed using the stepwise multiple linear regression algorithm combining with two physicochemical parameters (logD and μ), an electronic parameter (Q_N1) and a topological parameter (X^vPC₄). The optimal model could be mathematically described as following: pLC₅₀ = -7.24-0.30X^vPC₄ + 0.76logD - 26.15Q_N1 - 0.08μ. The internal validation by leave-one-out (LOO) cross-validation showed that the R²_adj (adjusted noncross-validation squared correlation coefficient), Q² (cross-validation correlation coefficient) and RMSD (root-mean-square error) was 0.88, 0.84 and 0.17, respectively. The external validation indicated the model had a robust predictability with the q² (predictive squared correlation coefficient) of 0.90 when eliminated tricyclazole. The present study provided a potential tool for predicting the acute toxicity of new 1,2,4-triazole fungicides which contained an independent triazole ring group in their molecules to zebrafish embryos, and also provided a reference for the development of more environmentally-friendly 1,2,4-triazole pesticides in the future.

Multisolvent Models for Solvation Free Energy Predictions Using 3D-RISM Hydration Thermodynamic Descriptors

The potential to predict solvation free energies (SFEs) in any solvent using a machine learning (ML) model based on thermodynamic output, extracted exclusively from 3D-RISM simulations in water is investigated. The models on multiple solvents take into account both the solute and solvent description and offer the possibility to predict SFEs of any solute in any solvent with root mean squared errors less than 1 kcal/mol. Validations that involve exclusion of fractions or clusters of the solutes or solvents exemplify the model's capability to predict SFEs of novel solutes and solvents with diverse chemical profiles. In addition to being predictive, our models can identify the solute and solvent features that influence SFE predictions. Furthermore, using 3D-RISM hydration thermodynamic output to predict SFEs in any organic solvent reduces the need to run 3D-RISM simulations in all these solvents. Altogether, our multisolvent models for SFE predictions that take advantage of the solvation effects are expected to have an impact in the property prediction space.

Thermal Conductivity Estimation of Diverse Liquid Aliphatic Oxygen-Containing Organic Compounds Using the Quantitative Structure-Property Relationship Method

Thermal conductivity is an essential thermodynamic data in chemical engineering applications. Liquid aliphatic oxygen-containing organic compounds are important organic intermediates and raw materials. As a result, estimating thermal conductivity of liquid aliphatic oxygen-containing organic compounds is of significance in industry production. In this study, the genetic function approximation method was applied to screen descriptors and develop a 6-descriptor linear quantitative structure-property relationship model. The entire data set of these compounds covering 1064 thermal conductivity values was divided into 694-member training set, 298-member test set, and 72-member prediction set. The average absolute relative deviation of the training set, test set, and prediction set were 4.14, 4.41, and 4.16%, respectively. Model validation and Y-randomization test proved that the developed model has goodness-of-fit, predictive power, and robustness. In addition, the applicability domain of the developed model was visualized by the Williams plot. This study can provide a convenient method to estimate the thermal conductivity for researchers in chemical engineering production.

Multitarget Approach to Drug Candidates against Alzheimer's Disease Related to AChE, SERT, BACE1 and GSK3β Protein Targets

Alzheimer’s disease is a neurodegenerative condition for which currently there are no drugs that can cure its devastating impact on human brain function. Although there are therapeutics that are being used in contemporary medicine for treatment against Alzheimer’s disease, new and more effective drugs are in great demand. In this work, we proposed three potential drug candidates which may act as multifunctional compounds simultaneously toward AChE, SERT, BACE1 and GSK3β protein targets. These candidates were discovered by using state-of-the-art methods as molecular calculations (molecular docking and molecular dynamics), artificial neural networks and multilinear regression models. These methods were used for virtual screening of the publicly available library containing more than twenty thousand compounds. The experimental testing enabled us to confirm a multitarget drug candidate active at low micromolar concentrations against two targets, e.g., AChE and BACE1.

A New Machine-Learning Tool for Fast Estimation of Liquid Viscosity. Application to Cosmetic Oils

The viscosities of pure liquids are estimated at 25 °C, from their molecular structures, using three modeling approaches: group contributions, COSMO-RS σ-moment-based neural networks, and graph machines. The last two are machine-learning methods, whereby models are designed and trained from a database of viscosities of 300 molecules at 25 °C. Group contributions and graph machines make use of the 2D-structures only (the SMILES codes of the molecules), while neural networks estimations are based on a set of five descriptors: COSMO-RS σ-moments. For the first time, leave-one-out is used for graph machine selection, and it is shown that it can be replaced with the much faster virtual leave-one-out algorithm. The database covers a wide diversity of chemical structures, namely, alkanes, ethers, esters, ketones, carbonates, acids, alcohols, silanes, and siloxanes, as well as different chemical backbone, i.e., straight, branched, or cyclic chains. A comparison of the viscosities of liquids of an independent set of 22 cosmetic oils shows that the graph machine approach provides the most accurate results given the available data. The results obtained by the neural network based on sigma-moments and by the graph machines can be duplicated easily by using a demonstration tool based on the Docker technology, available for download as explained in the Supporting Information. This demonstration also allows the reader to predict, at 25 °C, the viscosity of any liquid of moderate molecular size (M < 600 Da) that contains C, H, O, or Si atoms, starting either from its SMILES code or from its σ-moments computed with the COSMOtherm software.

Comparative QSAR model generation using pyrazole derivatives for screening Janus kinase-1 inhibitors

Asthma is a multitargeted disease. IL-4-JAK-STAT signaling pathway is a promising route for the effective control of the disease. JAK inhibition by small molecules could effectively block the IL-4 signaling pathway. It was established that JAK1 is responsive toward IL-4-mediated signaling process. In the present study, three-dimensional QSAR analyses on a set of pyrazole derivatives against JAK1 and JAK2 enzyme inhibition had been executed. Molecular docking studies were conducted with the target JAK1 using the pyrazole derivative compounds and found out potential intermolecular interactions operating among them. The binding energy of all the derivative compounds with the target JAK1 has calculated and found out their affinity toward the target system. These models have predicted the JAK1 inhibitory activity of some five JAK1 active drugs and 50 structurally similar compounds. These models can, thus, suggestively be recommended for virtual screening of JAK1-selective candidates as a lead for immunomodulatory diseases like asthma.

Semiquantitative Atomic Force Microscopy-Infrared Spectroscopy Analysis of Chemical Gradients in Silicone Optical Waveguides

Crossing losses in silicone optical waveguides are related to the magnitude and spatial extent of the waveguide refractive index gradient. When processing conditions are altered, the refractive index gradient can vary substantially, even when the formulation remains constant. Controlling the refractive index gradient requires control of the concentration of small molecules present within the core and clad layers. Developing a fundamental understanding of how small molecule migration drives changes in crossing loss requires the ability to examine chemical functionality over small length scales, which is a natural fit for atomic force microscopy-infrared spectroscopy (AFM-IR). In this work, AFM-IR spectra from model bilayer stacks are initially examined to understand molecular migration that occurs from heating the core and clad layers. The results of these model studies are then applied to photopatterned waveguide builds, where structure-function relationships are constructed between values of crossing loss and the concentration of C-H and O-H functionalities present in the core and clad layers. Results show that small molecule evaporation and migration are competing processes that need to be controlled to minimize crossing loss.

Glass transition temperature from the chemical structure of conjugated polymers

The glass transition temperature (Tg) is a key property that dictates the applicability of conjugated polymers. The Tg demarks the transition into a brittle glassy state, making its accurate prediction for conjugated polymers crucial for the design of soft, stretchable, or flexible electronics. Here we show that a single adjustable parameter can be used to build a relationship between the Tg and the molecular structure of 32 semiflexible (mostly conjugated) polymers that differ drastically in aromatic backbone and alkyl side chain chemistry. An effective mobility value, ζ, is calculated using an assigned atomic mobility value within each repeat unit. The only adjustable parameter in the calculation of ζ is the ratio of mobility between conjugated and non-conjugated atoms. We show that ζ correlates strongly to the Tg, and that this simple method predicts the Tg with a root-mean-square error of 13 °C for conjugated polymers with alkyl side chains.

Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion

Activity coefficients, which are a measure of the nonideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling chemical and phase equilibria as well as transport processes. Although experimental data on thousands of binary mixtures are available, prediction methods are needed to calculate the activity coefficients in many relevant mixtures that have not been explored to date. In this report, we propose a probabilistic matrix factorization model for predicting the activity coefficients in arbitrary binary mixtures. Although no physical descriptors for the considered components were used, our method outperforms the state-of-the-art method that has been refined over three decades while requiring much less training effort. This opens perspectives to novel methods for predicting physicochemical properties of binary mixtures with the potential to revolutionize modeling and simulation in chemical engineering.

Pharmacological Chaperones: A Therapeutic Approach for Diseases Caused by Destabilizing Missense Mutations

The term “pharmacological chaperone” was introduced 20 years ago. Since then the approach with this type of drug has been proposed for several diseases, lysosomal storage disorders representing the most popular targets. The hallmark of a pharmacological chaperone is its ability to bind a protein specifically and stabilize it. This property can be beneficial for curing diseases that are associated with protein mutants that are intrinsically active but unstable. The total activity of the affected proteins in the cell is lower than normal because they are cleared by the quality control system. Although most pharmacological chaperones are reversible competitive inhibitors or antagonists of their target proteins, the inhibitory activity is neither required nor desirable. This issue is well documented by specific examples among which those concerning Fabry disease. Direct specific binding is not the only mechanism by which small molecules can rescue mutant proteins in the cell. These drugs and the properly defined pharmacological chaperones can work together with different and possibly synergistic modes of action to revert a disease phenotype caused by an unstable protein.

Generators of Maximal Subgroups of Harada-Norton and some Linear Groups

Group theory, the ultimate theory for symmetry, is a powerful tool that has a direct impact on research in robotics, computer vision, computer graphics and medical image analysis. Symmetry is very important in chemistry research and group theory is the tool that is used to determine symmetry. Usually, it is not only the symmetry of molecule but also the symmetries of some local atoms, molecular orbitals, rotations and vibrations of bonds, etc. that are important. Harada-Norton group is an example of a sporadic simple group. There are 14 maximal subgroups of Harada-Norton group. Generators (also known as words) of 11 maximal subgroups are already known. The aim of this note is to give generators of the remaining 3 maximal subgroups, which is an open problem mentioned on A World-wide-web Atlas of Group Representations (http://brauer.maths.qmul.ac.uk/Atlas) [1]. In this report we compute the generators of A6 × A6.D8, 23+2+6.(3 × L3(2)) and 34 : 2.(A4 × A4).4. Moreover we also compute the generators for the Maximal subgroups of some linear groups.

Inverse-QSPR for de novo Design: A Review

The use of computer tools to solve chemistry-related problems has given rise to a large and increasing number of publications these last decades. This new field of science is now well recognized and labelled Chemoinformatics. Among all chemoinformatics techniques, the use of statistical based approaches for property predictions has been the subject of numerous research reflecting both new developments and many cases of applications. The so obtained predictive models relating a property to molecular features - descriptors - are gathered under the acronym QSPR, for Quantitative Structure Property Relationships. Apart from the obvious use of such models to predict property values for new compounds, their use to virtually synthesize new molecules - de novo design - is currently a high-interest subject. Inverse-QSPR (i-QSPR) methods have hence been developed to accelerate the discovery of new materials that meet a set of specifications. In the proposed manuscript, we review existing i-QSPR methodologies published in the open literature in a way to highlight developments, applications, improvements and limitations of each.

Descriptor Selection Improvements for Quantitative Structure-Activity Relationships

Molecular descriptor selection is an essential procedure to improve a predictive quantitative structure–activity relationship (QSAR) model. However, within the QSAR model, there are a number of redundant, noisy and irrelevant descriptors. In this study, we propose a novel descriptor selection framework using self-paced learning (SPL) via sparse logistic regression (LR) with Logsum penalty (SPL-Logsum), which can simultaneously adaptively identify the simple and complex samples and avoid over-fitting. SPL is inspired by the learning process of humans or animals gradually learned from simple and complex samples to train models, and the Logsum penalized LR helps to select a small subset of significant molecular descriptors for improving the QSAR models. Experimental results on some simulations and three public QSAR datasets show that our proposed SPL-Logsum framework outperforms other existing sparse methods regarding the area under the curve, sensitivity, specificity, accuracy, and [Formula: see text]-values.

Molecular Dynamics Studies on COX-2 Protein-tyrosine Analogue Complex and Ligand-based Computational Analysis of Halo-substituted Tyrosine Analogues

Background:

The quest for new drug entities and novel structural fragments with applications in therapeutic areas is always at the core of medicinal chemistry.

Methods:

As part of our efforts to develop novel selective cyclooxygenase-2 (COX-2) inhibitors containing tyrosine scaffold. The objective of this study was to identify potent COX-2 inhibitors by dynamic simulation, pharmacophore and 3D-QSAR methodologies. Dynamics simulation was performed for COX-2/tyrosine derivatives complex to characterise structure validation and binding stability. Certainly, Arg120 and Tyr355 residue of COX-2 protein formed a constant interaction with tyrosine inhibitor throughout the dynamic simulation phase. A four-point pharmacophore with one hydrogen bond acceptor, two hydrophobic and one aromatic ring was developed using the HypoGen algorithm. The generated, statistically significant pharmacophore model, Hypo 1 with a correlation coefficient of r2, 0.941, root mean square deviation, 1.15 and total cost value of 96.85.

Results:

The QSAR results exhibited good internal (r2, 0.992) and external predictions (r2pred, 0.814). The results of this study concluded the COX-2 docked complex was stable and interactive like experimental protein structure. Also, it offered vital chemical features with geometric constraints responsible for the inhibition of the selective COX-2 enzyme by tyrosine derivatives.

Conclusion:

In principle, this work offers significant structural understandings to design and develop novel COX-2 inhibitors.

Anti-hepatitis-C virus activity and QSAR study of certain thiazolidinone and thiazolotriazine derivatives as potential NS5B polymerase inhibitors

The present study reports on evaluation of anti-HCV activity and QSAR of certain arylidenethiazolidinone derivatives as potential inhibitors of HCV-NS5B polymerase. The pursued compounds involving, 5-aryliden-3-arylacetamidothiazolidin-2,4-diones 4-6(a-f), 5-arylidine-2-(N-arylacetamido)-iminothiazolidin-4-one (10) and their rigid counterparts 5-arylidinethiazolotriazines 13-15(a-f), were synthesized and their structures confirmed by spectral and elemental analyses. The results of NS5B polymerase inhibition assay revealed compound 4e, as the most active inhibitor (IC₅₀ = 0.035 μM), which is four folds greater than that of the reference agent, VCH-759, (IC₅₀ = 0.14 μM). Meanwhile, compounds 4b, 4c, 5a, and 5c, and 13b, 14e and 15c displayed equipotency to 2 folds higher activity than VCH-759 (IC₅₀ values: 0.085, 0.14, 0.14, 0.10, 0.12, 0.09 and 0.07 μM, respectively). Assessment of the anti-HCV activity (GT1a) using human hepatoma cell line (Huh-7.5) illustrates superior activity of 4e (EC₅₀ = 3.80 μM) relative to VCH-759 (EC₅₀ = 5.29 μM). Cytotoxicity evaluation on, Transformed normal cell lines (Human Liver Epithelial-2, THLE-2 and Proximal Tubular Epithelial, RPTEC/TERT1), demonstrate enhanced safety profile of 4e (CC₅₀ = 102.77, 161.37 μM, respectively) compared to VCH-759 (CC₅₀ = 61.83, 81.28 μM, respectively). Molecular docking of the synthesized derivatives to NS5B polymerase allosteric site (PDB: 2HWH) showed similar binding modes to that of the co-crystallized ligand. Moreover, QSAR models were established for the studied thiazolidinones and thiazolotriazines to investigate the molecular characteristics contributing to the observed NS5B polymerase inhibition activity. The obtained results inspire further investigations of thiazolidinones and thiazolotriazine aiming at affording more potent, safe and orally active non-nucleoside NS5B polymerase inhibitors as anti-HCV drug candidates.

Proton transfer reaction confined within carbon nanotubes: Density functional theory and quantitative structure–property relationship analysis

In this work, we focus our attention on chemical reactions confined within carbon nanotubes. As a result of the confinement within carbon nanotubes, novel physical and chemical properties are found for the confined materials. We consider the feasibility of proton transfer inside carbon nanotubes. To do that, we have chosen formamide as the simplest real model for exhibiting the tautomerization in DNA. We have used the quantitative structure–property relationship method, based on geometry optimization and quantum chemical structural descriptors, to illustrate the potential of using the confined space inside carbon nanotubes, which will provide comprehensive information about carbon nanotubes. All calculations have been carried out using density functional theory quantum calculations with the B3LYP functional. The geometries optimized by the Gaussian program were transferred to the computer software DRAGON to calculate pertinent descriptors that could be used in the quantitative structure–property relationship model.

Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods

The partitioning of compounds between aqueous and other phases is important for predicting toxicity. Although thousands of octanol-water partition coefficients have been measured, these represent only a small fraction of the anthropogenic compounds present in the environment. The octanol phase is often taken to be a mimic of the inner parts of phospholipid membranes. However, the core of such membranes is typically more hydrophobic than octanol, and other partition coefficients with other compounds may give complementary information. Although a number of (cheap) empirical methods exist to compute octanol-water (log k _OW) and hexadecane-water (log k _HW) partition coefficients, it would be interesting to know whether physics-based models can predict these crucial values more accurately. Here, we have computed log k _OW and log k _HW for 133 compounds from seven different pollutant categories as well as a control group using the solvation model based on electronic density (SMD) protocol based on Hartree-Fock (HF) or density functional theory (DFT) and the COSMO-RS method. For comparison, XlogP3 (log k _OW) values were retrieved from the PubChem database, and KowWin log k _OW values were determined as well. For 24 of these compounds, log k _OW was computed using potential of mean force (PMF) calculations based on classical molecular dynamics simulations. A comparison of the accuracy of the methods shows that COSMO-RS, KowWin, and XlogP3 all have a root-mean-square deviation (rmsd) from the experimental data of ≈0.4 log units, whereas the SMD protocol has an rmsd of 1.0 log units using HF and 0.9 using DFT. PMF calculations yield the poorest accuracy (rmsd = 1.1 log units). Thirty-six out of 133 calculations are for compounds without known log k _OW, and for these, we provide what we consider a robust prediction, in the sense that there are few outliers, by averaging over the methods. The results supplied may be instrumental when developing new methods in computational ecotoxicity. The log k _HW values are found to be strongly correlated to log k _OW for most compounds.

Novel thiazolidines: Synthesis, antiproliferative properties and 2D-QSAR studies

A series of N-substituted (Z)-2-imino-(5Z)-ylidene thiazolidines/thiazolidin-4-ones were synthesized and their antiproliferative activities against colon (HCT-116) and breast (MCF7) cancer cell lines were evaluated utilizing an MTT growth assay. A 2D-QSAR investigation was conducted to probe and validate the obtained antiproliferative properties for the thiazolidine derivatives. The majority of the thiazolidines exhibit higher potency against a colon cancer cell line relative to the standard reference. The p-halophenylimino p-anisylidene derivatives exhibited the highest anti-proliferative activity against HCT116 relative to control (IC₅₀ = 8.9-10.0 μM compared to 20.4 μM observed for 5-fluorouracil as positive control). An X-ray study confirmed the Z, Z'-configurations for two examples of the synthesized compounds.

Inhibition activity prediction for a dataset of candidates' drug by combining fuzzy logic with MLR/ANN QSAR models

A hybrid of artificial intelligence simple and low computational cost QSAR was used. Approximately 90 pyridinylimidazole-based drug candidates with a range of potencies against p38R MAP kinase were investigated. To obtain more flexibility and effective capability of handling and processing information about the real world, in this case, the fuzzy set theory was introduced into the QSAR. An integration of multiple linear regression and artificial neural network with adaptive neuro-fuzzy inference systems (ANFIS) was developed to predict the inhibition activity. The algorithm of ANFIS was applied to identify the suitable variables and then to find the optimal descriptors. The gradient descent with momentum backpropagation ANN was used to establish the nonlinear multivariate relationships between the chemical structural parameters and biological response. A comparison between the result of the proposed linear and nonlinear regression showed the superiority of QSAR modeling by ANFIS-ANN method over the MLR. The results demonstrated that the ANFIS could be applied successfully as a feature selection. The appearance of Diam, Homo, and LogP descriptors in the model showed the importance of the steric, electronic, and thermodynamic interactions between a drug and its target site in the distribution of a compound within a biosystem and its interaction with competing for binding sites.

A molecular contact theory for simulating polarization: application to dielectric constant prediction

Microscopic polarization in liquids, which is challenging to account for intuitively and quantitatively, can impact the behavior of liquids in numerous ways and thus is ubiquitous in a broad range of domains and applications. To overcome this challenge, in this work, a molecular contact theory was proposed as a proxy to simulate microscopic polarization in liquids. In particular, molecular surfaces from implicit solvation models were used to predict both the dipole moment of individual molecules and mutual orientations arising from contacts between molecules. Then, the calculated dipole moments and orientations were combined in an analytical coupling, which allowed for the prediction of effective (polarized) dipole moments for all distinct species in the liquid. As a proof-of-concept, the model focused on predicting the dielectric constant and was tested on 420 pure liquids, 269 binary organic mixtures (3792 individual compositions) and 46 aqueous mixtures (704 individual compositions). The model proved to be flexible enough to reach an unprecedented satisfactory mean relative error of about 16-22% and a classification accuracy of 84-90% within four meaningful classes of weak, low average, high average and strong dielectric constants. The method also proved to be computationally very efficient, with calculation times ranging from a few seconds to about ten minutes on a personal computer with a single CPU. This success demonstrates that much of the microscopic polarization concept can be satisfactorily described based on a simple molecular contact theory. Moreover, the new model for dielectric constants provides a useful alternative to computationally expensive molecular dynamics simulations for large scale virtual screenings in chemical engineering and material sciences.

Message-passing neural networks for high-throughput polymer screening

Machine learning methods have shown promise in predicting molecular properties, and given sufficient training data, machine learning approaches can enable rapid high-throughput virtual screening of large libraries of compounds. Graph-based neural network architectures have emerged in recent years as the most successful approach for predictions based on molecular structure and have consistently achieved the best performance on benchmark quantum chemical datasets. However, these models have typically required optimized 3D structural information for the molecule to achieve the highest accuracy. These 3D geometries are costly to compute for high levels of theory, limiting the applicability and practicality of machine learning methods in high-throughput screening applications. In this study, we present a new database of candidate molecules for organic photovoltaic applications, comprising approximately 91 000 unique chemical structures. Compared to existing datasets, this dataset contains substantially larger molecules (up to 200 atoms) as well as extrapolated properties for long polymer chains. We show that message-passing neural networks trained with and without 3D structural information for these molecules achieve similar accuracy, comparable to state-of-the-art methods on existing benchmark datasets. These results therefore emphasize that for larger molecules with practical applications, near-optimal prediction results can be obtained without using optimized 3D geometry as an input. We further show that learned molecular representations can be leveraged to reduce the training data required to transfer predictions to a new density functional theory functional.

A Way towards Reliable Predictive Methods for the Prediction of Physicochemical Properties of Chemicals Using the Group Contribution and other Methods

Physicochemical properties of chemicals as referred to in this review include, for example, thermodynamic properties such as heat of formation, boiling point, toxicity of molecules and the fate of molecules whenever undergoing or accelerating (catalytic) a chemical reaction and therewith about chemical equilibrium, that is, the equilibrium in chemical reactions. All such properties have been predicted in literature by a variety of methods. However, for the experimental scientist for whom such predictions are of relevance, the accuracies are often far from sufficient for reliable application We discuss current practices and suggest how one could arrive at better, that is sufficiently accurate and reliable, predictive methods. Some recently published examples have shown this to be possible in practical cases. In summary, this review focuses on methodologies to obtain the required accuracies for the chemical practitioner and process technologist designing chemical processes. Finally, something almost never explicitly mentioned is the fact that whereas for some practical cases very accurate predictions are required, for other cases a qualitatively correct picture with relatively low correlation coefficients can be sufficient as a valuable predictive tool. Requirements for acceptable predictive methods can therefore be significantly different depending on the actual application, which are illustrated using real-life examples, primarily with industrial relevance. Furthermore, for specific properties such as the octanol-water partition coefficient more close collaboration between research groups using different methods would greatly facilitate progress in the field of predictive modelling.

QSAR characterization of new synthesized hydantoins with antiproliferative activity

Hydantois have been identified as constituents of a number of pharmacologically active molecules. In the present study, we have examined in vitro antiproliferative activity against human colon cancer cell lines HCT-116 of three series of 3-(4-substituted benzyl)-hydantoins with various substituent attached in position 5 of the hydantoin ring. Since the investigated compounds have recently been synthesized and show antiproliferative activity, a good understanding of the properties of the potential drug responsible for their pharmacokinetics is an important goal for their further development. One of the important properties is lipophilicity. Lipophilicity has been assessed by reversed-phase liquid chromatography (high-performance thin-layer chromatography and high-pressure liquid chromatography) by means of direct and indirect (using calibration curve) methods. Chromatographic lipophilicity indices in addition to calculated logP values were compared by hierarchical cluster analysis. The linear solvation energy relationship approach was used to understand and compare the types and relative strength of the molecular interactions that occur in the chromatographic as well as in the n-octanol-water partitioning systems. Finally, correlation between in silico pharmacokinetic predictors and antiproliferative activity was examined. Preliminary quantitative structure-activity relationship modeling indicates that pharmacokinetic predictors capture only one-quarter of all chemical features that are important for antiproliferative activity itself. Among selected descriptors are chromatographic lipophilicity indices.