In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach

In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure-Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD50). An initial set of 4885 molecular descriptors was generated and applied to build Support Vector Regression (SVR) models. The best two SVR models, SVR_A and SVR_B, were selected to build an Ensemble Model by means of Multiple Linear Regression (MLR). The obtained Ensemble Model showed improved performance over the base SVR models in the training set (R-2 = 0.88), validation set (R-2 = 0.95), and true external test set (R-2 = 0.92). The models were also internally validated by 5-fold cross-validation and Y-scrambling experiments, showing that the models have high levels of goodness-of-fit, robustness and predictivity. The contribution of descriptors to the toxicity in the models was assessed using the Accumulated Local Effect (ALE) technique. The proposed approach provides an important tool to assess toxicity of nitroaromatic compounds, based on the ensemble QSAR model and the structural relationship to toxicity by analyzed contribution of the involved descriptors

1-(2-Hy­droxy­eth­yl)-3-(3-meth­oxy­phen­yl)thio­urea

In the title compound, C10H14N2O3S, the 3-meth­oxy­phenyl unit is almost planar, with an r.m.s. deviation of 0.013 Å. The dihedral angle between the benzene ring and the plane of the thio­urea unit is 62.57 (4)°. In the crystal, N—H⋯O and O—H⋯S hydrogen bonds link the mol­ecules into a three-dimensional network

1-(4-Hy­droxy­phen­yl)-3-(3,4,5-tri­methoxy­phen­yl)thio­urea

In the title compound, C16H18N2O4S, the dihedral angle between the hy­droxy­phenyl ring and the plane of the thio­urea moiety is 54.53 (8)°. The H atoms of the NH groups of thio­urea are positioned anti to each other. In the crystal, inter­molecular N—H⋯S, N—H⋯O, and O—H⋯S hydrogen bonds link the mol­ecules into a three-dimensional network

Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors

[Abstract] The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order kth (Wk). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the Wk(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated Wk(i) values were used as inputs for different ANNs in order to discriminate correct node connectivity patterns from incorrect random patterns. The MIANN models obtained present good values of Sensitivity/Specificity (%): MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary results are very promising from the point of view of a first exploratory study and suggest that the use of these models could be extended to the high-throughput re-evaluation of connectivity in known complex networks (collation)

The Hydrolysis Rate of Paraoxonase-1 Q and R Isoenzymes: An In Silico Study Based on In Vitro Data

Human serum paraoxonase-1 (PON1) is an important hydrolase-type enzyme found in numerous tissues. Notably, it can exist in two isozyme-forms, Q and R, that exhibit different activities. This study presents an in silico (QSAR, Docking, MD and QM/MM) study of a set of compounds on the activity towards the PON1 isoenzymes (QPON1 and RPON1). Different rates of reaction for the Q and R isoenzymes were analyzed by modelling the effect of Q192R mutation on active sites. It was concluded that the Q192R mutation is not even close to the active site, while it is still changing the geometry of it. Using the combined genetic algorithm with multiple linear regression (GA-MLR) technique, several QSAR models were developed and relative activity rates of the isozymes of PON1 explained. From these, two QSAR models were selected, one each for the QPON1 and RPON1. Best selected models are four-variable MLR models for both Q and R isozymes with squared correlation coefficient R2 values of 0.87 and 0.83, respectively. In addition, the applicability domain of the models was analyzed based on the Williams plot. The results were discussed in the light of the main factors that influence the hydrolysis activity of the PON1 isozymes

Ligand-based computer-aided discovery of tyrosinase inhibitors. Applications of the TOMOCOMD-CARDD method to the elucidation of new compounds

In this review an overview of the application of computational approaches is given. Specifically, the uses of Quantitative Structure-Activity Relationship (QSAR) methods for in silico identification of new families of compounds as novel tyrosinase inhibitors are revised. Assembling, validation of models through prediction series, and virtual screening of external data sets are also shown, to prove the accuracy of the QSAR models obtained with the TOMOCOMD-CARDD (TOpological MOlecular COMputational Design Computer-Aided Rational Drug Design) software and Linear Discriminant Analysis (LDA) as statistical technique. Together with this, a database is collected for these QSAR studies, and could be considered a useful tool in future QSAR modeling of tyrosinase activity and for scientists that work in the field of this enzyme and its inhibitors. Finally, a translation to real world applications is shown by the use of QSAR models in the identification and posterior in-vitro evaluation of different families of compounds. Several different classes of compounds from various sources (natural and synthetic) were identified. Between them, we can find tetraketones, cycloartanes, ethylsteroids, lignans, dicoumarins and vanilloid derivatives. Finally, some considerations are discussed in order to improve the identification of novel drug-like compounds based on the use of QSAR-Ligand-Based Virtual Screening (LBVS

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

Various coordination complexes have been the subject of experimental or theoretical studies in recent decades because of their fascinating photophysical properties. In this work a combined experimental and computational approach applied to investigate the optical properties of monocationic Ir(III) complexes. In result, an interpretative machine learning-based Quantitative Structure-Activity Relationship (QSAR) model was successfully developed, which can reliably predict the emission wavelength of the Ir(III) complexes and provides foundations for theoretical evaluation of the optical properties of Ir(III) complexes. A hypothesis was proposed to mechanistically explain the differences in emission wavelengths between structurally different individual Ir(III) complexes. To the best of our knowledge, this is the first attempt to develop predictive machine learning (QSAR) model for the optical properties of Ir(III) complexes. The efficacy of the developed model was demonstrated by high R2 values for the training and test sets of 0.84 and 0.87, respectively, and by performing the validation using y-scrambling techniques. A notable relationship between the N-N distance in the diimine ligands of the Ir(III) complexes and emission wavelengths was revealed. This combined experimental and computational approach shows a great potential for rational design of new Ir(III) complexes with desired optical properties. Moreover, the developed methodology could be extended to other octahedral transition-metal complexes

Atom- and Bond-Based 2D TOMOCOMD-CARDD Approach and Ligand-Based Virtual Screening for the Drug Discovery of New Tyrosinase Inhibitors

Two-dimensional atom- and bond-based TOMOCOMD-CARDD descriptors and linear discriminant analysis (LDA) are used in this report to perform a quantitative structure-activity relationship (QSAR) study of tyrosinase-inhibitory activity. A database of inhibitors of the enzyme is collected for this study, within 246 highly dissimilar molecules presenting antityrosinase activity. In total, 7 discriminant functions are obtained by using the whole set of atom- and bond-based 2D indices. All the LDA-based QSAR models show accuracies above 90% in the training set and values of the Matthews correlation coefficient (C) varying from 0.85 to 0.90. The external validation set shows globally good classifications between 89% and 91% and C values ranging from 0.75 to 0.81. Finally, QSAR models are used in the selection/identification of the 20 new dicoumarins subset to search for tyrosinase inhibitory activity. Theoretical and experimental results show good correspondence between one anothe..

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

Novel coumarin-based tyrosinase inhibitors discovered by OECD principles-validated QSAR approach from an enlarged, balanced database

The present work is devoted to the development and application of a multi-agent Quantitative Structure-Activity Relationship (QSAR) classification system for tyrosinase inhibitor identification, in which the individual QSAR outputs are the inputs of a fusion approach based on the voting mechanism. The individual models are based on TOMOCOMD-CARDD (TOpological Molecular COMputational Design-Computer Aided Rational Drug Design) atom-based bilinear descriptors and Linear Discriminant Analysis (LDA) on a novel enlarged, balanced database of 1,429 compounds within 701 greatly dissimilar molecules presenting anti-tyrosinase activity. A total of 21 adequate models are obtained taking into account the requirements of the Organization for Economic Cooperation and Development (OECD) principles for QSAR validation and present global accuracies (Q) above 84.50 and 79.27% in the training and test sets, respectively. The resulted fusion system is used for the in silico identification of synthesized coumarin derivatives as novel tyrosinase inhibitors. The 7-hydroxycoumarin (compound C07) shows potent activity for the inhibition of monophenolase activity of mushroom tyrosinase giving a value of inhibition percentage close to 100% in vitro assays, by means of spectrophotometric analysis. The current report could help to shed some clues in the identification of new chemicals that inhibit tyrosinase enzyme, for entering in the pipeline of drug discovery development