Physicochemical Descriptors Research Articles

Using Big Data Analytics to "Back Engineer" Protein Conformational Selection Mechanisms.

In the living cells, proteins bind small molecules (or “ligands”) through a “conformational selection” mechanism, where a subset of protein structures are capable of binding the small molecules well while most other protein structures are not capable of such binding. The present work uses machine learning approaches to identify, in a very large amount of protein:ligand complexes, what protein properties are associated with their capacity to bind small molecules. In order to do so, we calculate 40 physicochemical properties on about 1.5 millions of protein conformations: ligand and protein conformations. This work describes a machine learning approach to identify the unique physico-chemical descriptors of a protein that maximize the prediction rate of potential protein molecular conformations for the test case proteins ADORA2A (Adenosine A2a Receptor), ADRB2 (Adrenoceptor Beta 2) and OPRK1 (Opioid Receptor Kappa 1). We find adequate machine learning techniques can increase by an order of magnitude the identification of “binding protein conformations” in an otherwise very large ensemble of protein conformations, compared to random selection of protein conformations. This opens the door to the systematic identification of such “binding conformations” for proteins and provides a big data approach to the conformational selection mechanism.

Permeability of diverse drugs through a lipid barrier: Impact of pH and cyclodextrin

Evaluation of permeability of drugs and drug-like compounds is a complicated task. In the present study in order to construct the predictive schemes of passive diffusion permeation for a set of drugs with diverse structure their permeability was determined using a lipophilic biomimetic membrane - PermeaPad barrier and the vertical-type Franz diffusion cell. Permeability coefficients were analyzed and correlated to physicochemical properties and structure descriptors. The best fitting equations were derived using the solubility of the studied compounds in 1-octanol and in the frameworks of the solubility-diffusion theory. The impact of pH and addition of 2-hydroxypropyl-β-cyclodextrin on the permeability of two selected amphiphilic compounds has been disclosed.

Novel Solubility Prediction Models: Molecular Fingerprints and Physicochemical Features vs Graph Convolutional Neural Networks.

Predicting both accurate and reliable solubility values has long been a crucial but challenging task. In this work, surrogated model-based methods were developed to accurately predict the solubility of two molecules (solute and solvent) through machine learning and deep learning. The current study employed two methods: (1) converting molecules into molecular fingerprints and adding optimal physicochemical properties as descriptors and (2) using graph convolutional network (GCN) models to convert molecules into a graph representation and deal with prediction tasks. Then, two prediction tasks were conducted with each method: (1) the solubility value (regression) and (2) the solubility class (classification). The fingerprint-based method clearly demonstrates that high performance is possible by adding simple but significant physicochemical descriptors to molecular fingerprints, while the GCN method shows that it is possible to predict various properties of chemical compounds with relatively simplified features from the graph representation. The developed methodologies provide a comprehensive understanding of constructing a proper model for predicting solubility and can be employed to find suitable solutes and solvents.

AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials

Antibiotic resistance is a major threat to public health. The development of chemo-informatic tools to guide medicinal chemistry campaigns in the efficint design of antibacterial libraries is urgently needed. We present AB-DB, an open database of all-atom force-field parameters, molecular dynamics trajectories, quantum-mechanical properties, and curated physico-chemical descriptors of antimicrobial compounds. We considered more than 300 molecules belonging to 25 families that include the most relevant antibiotic classes in clinical use, such as β-lactams and (fluoro)quinolones, as well as inhibitors of key bacterial proteins. We provide traditional descriptors together with properties obtained with Density Functional Theory calculations. Noteworthy, AB-DB contains less conventional descriptors extracted from μs-long molecular dynamics simulations in explicit solvent. In addition, for each compound we make available force-field parameters for the major micro-species at physiological pH. With the rise of multi-drug-resistant pathogens and the consequent need for novel antibiotics, inhibitors, and drug re-purposing strategies, curated databases containing reliable and not straightforward properties facilitate the integration of data mining and statistics into the discovery of new antimicrobials.

Eco-friendly synthesis, biological evaluation and QSAR studies of dihydropyrimidinone/thione derivatives

Dihydropyrimidinone/thiones (DHPMs) hold earmarked place in the field of natural and synthetic organic chemistry, because of their therapeutic and pharmacological properties. Synthesis of such biologically important compounds assumes great concern. Objectives The aim of the present work was to synthesize structurally challenging DHPMs through green procedure, evaluated for antibacterial activity and to perform QSAR analysis to attain the structural requirements for the antibacterial activity. The synthesis of dihydropyrimidinone/thiones was accomplished via three-component one-pot cyclocondensation between substituted alkyl/aryl aldehydes, diketone and urea/thiourea in the presence of pine apple juice at room temperature. The structures of the compounds were established by spectral analysis and evaluated for antibacterial activity. Possible correlation between observed biological activity and physicochemical descriptors was studied. Synthesis of DHPMs by pineapple juice as a catalyst, complies with principle of green chemistry. Moreover, this solvent free approach totally offers nonpolluting environment and avoiding the usage of toxic organic solvents. The spectral analysis reveals that all the compounds 1a-1p was in good agreement with the standard values reported in the literature for these types of structures. The tested compounds 1e, 1m &1i, 1o showed maximum activity against S. aureus and E. coli at concentrations i.e250 & 500μg /ml respectively. The statistically significant equations with good coefficient of correlation were obtained by correlating antibacterial activities as dependent variables and substituent constants as independent variable. The result of the QSAR study suggested that steric and thermodynamic descriptors contributed significantly to biological activity. Green synthesis of dihydropyrimidinone/thiones is extremely useful and improved procedure for the Biginelli reaction with excellent yields in shorter reaction time with high purity of the products. Through the iterative computational approach, this study could be utilized to design more potent dihydropyrimidinone/thione analogues as antimicrobial agents. Keywords: Green Synthesis, Dihydropyrimidinone/thioneanalogues, Anti-bacterial.

Computational Studies, GERS, Photovoltaic Modelling and Molecular Docking Studies of Diethylstilbestrol and Its Methyl Ether

Two very important compounds diethylstilbestrol (DESB) and diethylstilbestrol dimethyl ether (DESME) were studied in the present work. DFT theory with B3LYP functional was used to study the vibrational spectra in detail along with other quantum mechanical studies. The compounds were found to interact with graphene monolayer and the results show that there is enhancement in various physicochemical descriptors and enhancement of Raman modes. Enhancement of polarizability values of molecule-graphene complex systems is responsible for the enhancement in Raman intensity of different vibrational modes. The global reactivity descriptors were found out. The compounds are efficient non-linear active materials. DESB and DESME are docked with androgen receptor for prostate cancer and the amino acid interactions are reported UV spectra were simulated using TD-DFT and is used to predict the light-harvesting efficiency and their ability to act as photo sensitizers in solar cells.

Collection of Data Variation Using a High-Throughput Image-Based Assay Platform Facilitates Data-Driven Understanding of TRPA1 Agonist Diversity

Because transient receptor potential ankyrin 1 (TRPA1) is involved in various physiological functions, TRPA1-targeting drugs have been energetically developed. Although TRPA1 is considered a multimodal receptor, the structural diversity of TRPA1 agonists is not fully elucidated. We hypothesized that collecting a wider variation of TRPA1–compound interaction data would aid the understanding of its complex mechanism and aimed to challenge such data collection using an “image-based TRPA1 assay system combined with an in silico chemical space clustering concept.” Our library was clustered with 27 physicochemical molecular descriptors in silico, and structurally diverse compounds from each cluster were selected for a detailed kinetic assay to investigate variations of agonist structural rules. Through two sets of assays evaluating various compounds in parallel with validating effects of the previously established structural rules, we discovered that different chemical groups contribute to agonist activity, indicating that there are multiple agonist design concepts. A novel core structure for a TRPA1 agonist has been also proposed. Our new approach, “collection of TRPA1 activity data on compounds with physicochemical diversity,” will not only facilitate the understanding of the structural diversity of TRPA1 agonists but also contribute to the development of a new type of TRPA1-targeting drug.

Quantitative structure retention relationship (QSRR) modelling for Analytes’ retention prediction in LC-HRMS by applying different Machine Learning algorithms and evaluating their performance

In metabolomics, retention prediction methods have been developed based on the structural and physicochemical characteristics of analytes. Such methods employ regression models, harnessing machine learning algorithms mapping experimentally derived retention time (tR) analytes with various structural and physicochemical descriptors, known as Quantitative Structure Retention Relationships (QSRR) models. In the present study, QSRR models have been developed by applying four Machine Learning regression algorithms, i.e. Bayesian Ridge Regression (BRidgeR), Extreme Gradient Boosting Regression (XGBR) and Support Vector Regression (SVR) using both linear and non-linear kernels, all tested and compared for their retention prediction ability on experimentally derived and on publicly available chromatographic data, using Molecular Descriptors to describe the physical, chemical or structural properties of molecules. Various configurations of the available datasets, in terms of the highly-correlated features levels (defined as the maximum absolute value of the Pearson’s correlation coefficient calculated between any pair of features) they contained, were analyzed in parallel. This is the first study, to the best of our knowledge, of the effect of collinearity on the performance of QSRR predictive models. In the vast majority of cases studied there was no statistically significant difference in the performance of the generated QSRR predictive models among the specified dataset configurations, indicative of the ability of the selected regression algorithms to effectively handle collinearity. In terms of the individual performance of the selected regression algorithms, no pattern was found where one algorithm (or class of algorithms) stood out significantly relative to the others among the study datasets.

A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis.

The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure-property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce kraken, a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300000 new ligands. We demonstrate the application of kraken to systematically explore the property space of organophosphorus ligands and how existing data sets in catalysis can be used to accelerate ligand selection during reaction optimization.

Discovery of new Cdc2-like kinase 4 (CLK4) inhibitors via pharmacophore exploration combined with flexible docking-based ligand/receptor contact fingerprints and machine learning.

Cdc2-like kinase 4 (CLK4) inhibitors are of potential therapeutic value in many diseases particularly cancer. In this study, we combined extensive ligand-based pharmacophore exploration, ligand–receptor contact fingerprints generated by flexible docking, physicochemical descriptors and machine learning-quantitative structure–activity relationship (ML-QSAR) analysis to investigate the pharmacophoric/binding requirements for potent CLK4 antagonists. Several ML methods were attempted to tie these properties with anti-CLK4 bioactivities including multiple linear regression (MLR), random forests (RF), extreme gradient boosting (XGBoost), probabilistic neural network (PNN), and support vector regression (SVR). A genetic function algorithm (GFA) was combined with each method for feature selection. Eventually, GFA-SVR was found to produce the best self-consistent and predictive model. The model selected three pharmacophores, three ligand–receptor contacts and two physicochemical descriptors. The GFA-SVR model and associated pharmacophore models were used to screen the National Cancer Institute (NCI) structural database for novel CLK4 antagonists. Three potent hits were identified with the best one showing an anti-CLK4 IC50 value of 57 nM.

FEPS: A Tool for Feature Extraction from Protein Sequence.

Machine learning has become one of the most popular choices for developing computational approaches in protein structural bioinformatics. The ability to extract features from protein sequence/structure often becomes one of the crucial steps for the development of machine learning-based approaches. Over the years, various sequence, structural, and physicochemical descriptors have been developed for proteins and these descriptors have been used to predict/solve various bioinformatics problems. Hence, several feature extraction tools have been developed over the years to help researchers to generate numeric features from protein sequences. Most of these tools have some limitations regarding the number of sequences they can handle and the subsequent preprocessing that is required for the generated features before they can be fed to machine learning methods. Here, we present Feature Extraction from Protein Sequences (FEPS), a toolkit for feature extraction. FEPS is a versatile software package for generating various descriptors from protein sequences and can handle several sequences: the number of which is limited only by the computational resources. In addition, the features extracted from FEPS do not require subsequent processing and are ready to be fed to the machine learning techniques as it provides various output formats as well as the ability to concatenate these generated features. FEPS is made freely available via an online web server as well as a stand-alone toolkit. FEPS, a comprehensive toolkit for feature extraction, will help spur the development of machine learning-based models for various bioinformatics problems.

Natural or synthetic? Classification of common preservatives in food and drug industry by artificial intelligence

Os antioxidantes monoaromáticos são uma das principais classes de pequenas moléculas farmacológicas cujo uso como conservantes é difundido na indústria farmacêutica e alimentícia. A diferenciação desses compostos de acordo com sua origem é notavelmente difícil devido às suas características estruturais compartilhadas. Este trabalho mostra como promover a classificação de antioxidantes monoaromáticos naturais e sintéticos usando análise multivariada, mineração de dados e algoritmos de aprendizado de máquina. Descritores moleculares físico-químicos e biofarmacêuticos foram selecionados e calculados para renderizar modelos de alinhamento e classificação usando análise de componentes principais, mineração de dados, máquinas de suporte de vetores (kernel linear) e perceptron multicamadas. Mostramos que preditores moleculares físico-químicos e biofarmacêuticos podem ser atributos adequados para diferenciar antioxidantes monoaromáticos naturais e sintéticos, uma vez que seus resultados de análise multivariada, mineração de dados e algoritmos de aprendizado de máquina geraram um modelo confiável e preciso para classificação imediata de antioxidantes monoaromáticos naturais e sintéticos. Além disso, todos os modelos de classificação apresentaram acurácias acima de 80%. Este trabalho, portanto, lança luz sobre o uso da inteligência artificial no desenvolvimento de classificadores para aplicações farmacêuticas e alimentícias.

MOLECULAR FIELD-BASED QSAR STUDIES AND DOCKING ANALYSIS OF MERCAPTOQUINAZOLINONE BENZENE SULFONAMIDE DERIVATIVES AGAINST hCA XII

Selective targeting of the tumor-associated hCA XII isozyme is a promising strategy to obtain effective and safer agents in cancer therapy. A series of mercapto-quinazolinone benzene sulfonamide derivatives were subjected to molecular field analysis to derive 3D-QSAR models. Structural properties such as physicochemical, topological, electro-topological, and quantum-chemical descriptors were calculated using the Molecular Design Suite of V-life MDS 4.6 Software. The contour map generated from the SA-kNN model explains the significance of electrostatic and steric descriptors for hCA XII binding interaction. Molecular docking studies favored structural insights in association with QSAR. Most interacting residues Asn67, Gln92, Thr199 and His119 stabilized the compounds in the active pocket. The results suggest structural insights as well as highlight the key binding features of mercaptoquinazolinone benzene sulfonamide derivatives against hCA XII which can be utilized for the design and development of potent leads.

Synthesis and Critical View on the Structure-Activity Relationships of N-(Substituted phenyl)-/N-Diphenylmethyl-piperazine-Based Conjugates as Antimycobacterial Agents

This research focused on a three-step synthesis, analytical, physicochemical, and biological evaluation of hybrid molecules 6a–g, containing a lipophilic 3-trifluoromethylphenyl moiety, polar carbamoyloxy bridge, 2-hydroxypropan-1,3-diyl chain and 4-(substituted phenyl)-/4-diphenylmethylpiperazin-1-ium-1-yl fragment. The estimation of analytical and physicochemical descriptors (m/zmeasured via HPLC-UV/HR-MS, log ε2 (Ch–T) from UV/Vis spectrophotometry and log kw via RP-HPLC) as well as in vitro antimycobacterial and cytotoxic screening of given compounds were carried out (i.e., determination of MIC and IC50 values). These highly lipophilic molecules (log kw = 4.1170–5.2184) were tested against Mycobacterium tuberculosis H37Ra ATCC 25177 (Mtb H37Ra), M. kansasii DSM 44162 (MK), M. smegmatis ATCC 700084 (MS), and M. marinum CAMP 5644 (MM). The impact of the 6a–g set on the viability of human liver hepatocellular carcinoma (HepG2) cells was also investigated. 1-[2-Hydroxypropyl-{(3-trifluoromethyl)- phenyl}carbamoyloxy]-4-(3,4-dichlorophenyl)piperazin-1-ium chloride (6e) and 1-[2-hydroxy- propyl-{(3-trifluoromethyl)phenyl}carbamoyloxy]-4-(4-diphenylmethyl)piperazin-1-ium chloride (6g) most effectively inhibited the growth of Mtb H37Ra (MIC < 3.80 μM). The substance 6g also showed interesting activity against MM (MIC = 8.09 μM). All obtained data served as input values for structure-activity relationship evaluations using statistical principal component analysis. In fact, the toxicity of both 6e (IC50 = 29.39 μM) and 6g (IC50 = 22.18 μM) in HepG2 cells as well as selectivity index (SI) values (SI < 10.00) prevented to consider these promising antimycobacterials safe.

Comparative study of DFT, AM1, and PM3 optimization methods modeling new psychotropic amphetamines

In order to improve the determination of physico-chemical descriptors of organic substances, it is necessary to optimize the computational representations of their molecular structures. The computerized estimation of these descriptors is especially useful in the case of drugs of abuse, as clinical studies are not recommended for new compounds belonging to classes known to have a high toxicity. The optimization of molecular structures allows the improvement of the accuracy in determining the physico-chemical descriptors and consequently of the associated quantitative structure - property or structure - activity relationships (QSAR). We are presenting a comparative study regarding the effect of the AM1, PM3, and DFT optimization methods, which were applied in the case of a series of new psychotropic amphetamines for their physico-chemical characterization based on molecular descriptors.

Quantitative and Qualitative Structure Activity Relationship Study for Development of the Pet- Inhibitory Activity of 3-Nitro-2,4,6- Trihydroxy Benzamides

The aim in this paper of these work present two components analyses quantitative and qualitative which consisted in the development and evaluation of the first component quantitative and structure activity relationships (QSAR) for the prediction In fact, various compounds inhibiting photosynthesis constitute the largest class of commercial herbicides. All of those inhibitors, including ureas, triazines, bis carbamates and phenols, interrupt photosynthetic electron transport (PET) by binding to quinine-binding protein (D1-protein). Various physicochemical descriptors were used in multiple linear regressions method (MLR) to develop the theoretical models, than using a cross-validation with leave-one-out method to optimize the model as well as possible to fit with the biological data, the most common approach used to study. The second quality of the compound compared with criteria for their power, these rules Lu Propose the four basic characteristics that Lipinski has identified.

Methods for methanol-to-gasoline conversion with forwarding methanol processing

The design of modified H-ZSM-5 catalyst for catalytic conversion of methanol to gasoline range hydrocarbons (MTG) was carried out using neuro-genetic approach. Four factors: SiO2/Al2O3 ratio in zeolite (denoted as Si/Al2), calcination temperature (Tc), reaction temperature (Tr) and nominal weight loadings (Lm) of three metals (Ag, Cu, Co) were modeled simultaneously by using combined experimental data and physicochemical descriptors of metals. The obtained optimum model was used as fitness function for hybrid genetic algorithm to find the optimum catalyst. The optimum catalyst was Cu/H-ZSM-5 with Si/Al2 = 40, Tc = 490 °C, Tr = 340 °C, and Lm = 9.24 wt% with methanol conversion of 96.23%, and gasoline yield of 13% that resulted in 4.88% increase in gasoline yield compared to H-ZSM-5 catalyst at the same conditions. The higher experimental yield of gasoline for predicted catalyst along with the fact that this approach can be applied easily for a large number of catalysts, confirms that this approach can be used for design of heterogeneous catalysts. UV–Vis DR spectra confirmed the possibility of ⋯O2−⋯Cu2+⋯O2−⋯Cu2+⋯O2−⋯ chain structures in the channels of zeolite for Cu/HZSM-5 catalyst as active components in MTG reaction.

Structure and dynamics of the somatostatin receptor 3-ligand binding in the presence of lipids examined using computational structural biology methods.

In the past two decades, the structural biology studies on G-protein coupled receptors (GPCRs) are on the rise. Understanding the relation between the structure and function of GPCRs is important as they play a huge role in various signaling mechanisms in a eukaryotic cell. Somatostatin receptor 3 (SSTR3), one of the GPCRs, is one such important receptor which oversees different cellular processes including cell-to-cell signaling. However, the information available regarding the structural features of SSTR3 responsible for their bioactivity is scarce. In this study, we report a structural understanding of SSTR3-ligand binding that could be helpful in demystifying the structural complexities related to functioning of the receptor. An integrated protocol consisting of different computational structural biology tools including protein structure prediction via comparative modeling, binding site characterization, three-dimensional quantitative structure-activity relationship based on comparative molecular field analysis and comparative molecular similarity indices analysis, density functional theory, and molecular dynamics simulations were performed. Different understandings from the simulation of SSTR3-ligand complexes, mainly the conditions that are favorable for the formation of lowest bioactive state of SSTR3 ligands are reported. In addition to that, we report the important physicochemical descriptors of SSTR3 ligands that could significantly influence their bioactivity. The results of the study could be helpful in developing novel SSTR3 ligands (both agonists and antagonists) with high potency and receptor selectivity.

Calculation of stability constants of new metal-thiosemicarbazone complexes based on the QSPR modeling using MLR and ANN methods

In this study, the stability constants (logβ11) of twenty-eight new complexes between several ion metals and thiosemicarbazone ligands were predicted on the basis of the quantitative structure property relationship (QSPR) modeling. The stability constants were calculated from the results of the QSPR models. The QSPR models were built by using the multivariate least regression (QSPRMLR) and artificial neural network (QSPRANN). The molecular descriptors, physicochemical and quantum descriptors of complexes were generated from molecular geometric structure and semi-empirical quantum calculation PM7 and PM7/sparkle. The best linear model QSPRMLR involves five descriptors, namely Total energy, xch6, xp10, SdsN, and Maxneg. The quality of the QSPRMLR model was validated by the statistical values that were R2train = 0.860, Q2LOO = 0.799, SE = 1.242, Fstat = 54.14 and PRESS = 97.46. The neural network model QSPRANN with architecture I(5)-HL(9)-O(1) was presented with the statistical values: R2train = 0.8322, Q2CV = 0.9935 and Q2test = 0.9105. Also, the QSPR models were evaluated externally and achieved good performance results with those from the experimental literature. In addition, the results from the QSPR models could be used to predict the stability constants of other new metal-thiosemicarbazones.

Evaluation of a new series of pyrazole derivatives as a potent epidermal growth factor receptor inhibitory activity: QSAR modeling using quantum-chemical descriptors.

Pyrazole derivatives correspond to a family of heterocycle molecules with important pharmacological and physiological applications. At present, we perform a density functional theory (DFT) calculations and a quantitative structure-activity relationship (QSAR) evaluation on a series of 1-(4,5-dihydro-1H-pyrazol-1-yl) ethan-1-one and 4,5-dihydro-1H-pyrazole-1-carbothioamide derivatives as an epidermal growth factor receptor (EGFR) inhibitory activity. We thus propose a virtual screening protocol based on a machine-learning study. This theoretical model relates the studied compounds' biological activity to their calculated physicochemical descriptors. Moreover, the linear regression function is used to validate the model via the evaluation of Q2ext and Q2cv parameters for external and internal validations, respectively. Our QSAR model shows a good correlation between observed activities IC50 and predicted ones. Our model allows us to mitigate time-consuming problems and waste chemical and biological products in the preclinical phases.