Saadi Saaidpour

Bio: Saadi Saaidpour is an academic researcher from Islamic Azad University. The author has contributed to research in topics: Molecular descriptor & Quantitative structure–activity relationship. The author has an hindex of 9, co-authored 21 publications receiving 384 citations. Previous affiliations of Saadi Saaidpour include Islamic Azad University Sanandaj Branch & Razi University.

QSPR study for estimation of acidity constants of some aromatic acids derivatives using multiple linear regression (MLR) analysis

Abstract: A very simple, strong, descriptive and interpretable model, based on a quantitative structure–property relationship (QSPR), is developed using multiple linear regression approach and quantum chemical descriptors derived from AM1-based calculations (MOPAC7.0) for determination of the acidity constants of some aromatic acid derivatives. By molecular modeling and calculation of descriptors, three significant descriptors related to the pKa values of the acids, were identified. These are related to the partial charges at each atom in Oδ−–Hδ+ bond (pchgHδ+ and pchgOδ− −) and the changing of bond length in O–H molecular structures. A multiple linear regression (MLR) model based on 74 molecules as a training set has been developed for the prediction of the acidity constants of some aromatic acids using these quantum chemical descriptors. The effects of these theoretical descriptors on the acidity constants are discussed. The pKa values of aromatic acids generally decreased with increasing positive partial charges of acidic hydrogen atom. A model with low prediction error and high correlation coefficient was obtained. This model was used for the prediction of the pKa values of some aromatic acids (33 test acids) which were not used in the modeling procedure. The model obtained demonstrates excellent fit statistics and gives accurate predictions. The average relative error ( RE ¯ % ) of prediction set is lower than 1% and square correlation coefficient (R2) is 0.9882.

QSPR Prediction of Aqueous Solubility of Drug-Like Organic Compounds

Abstract: A quantitative structure property relationship (QSPR) study was performed to develop a model that relates the structures of 150 drug organic compounds to their aqueous solubility (log Sw). Molecular descriptors derived solely from 3D structure were used to represent molecular structures. A subset of the calculated descriptors selected using stepwise regression that used in the QSPR model development. Multiple linear regression (MLR) is utilized to construct the linear QSPR model. The applied multiple linear regression is based on a variety of theoretical molecular descriptors selected by the stepwise variable subset selection procedure. Stepwise regression was employed to develop a regression equation based on 110 training compounds, and predictive ability was tested on 40 compounds reserved for that purpose. The final regression equation included three parameters that consisted of octanol/water partition coefficient (log P), molecular volume (MV) and hydrogen bond forming ability (HB), of the drug molecules, all of which could be related to solubility property. Application of the developed model to a testing set of 40 drug organic compounds demonstrates that the new model is reliable with good predictive accuracy and simple formulation. The use of descriptors calculated only from molecular structure eliminates the need for experimental determination of properties for use in the correlation and allows for the estimation of aqueous solubility for molecules not yet synthesized. The prediction results are in good agreement with the experimental values. The root mean square error of prediction (RMSEP) and square correlation coefficient (R2) of prediction of log Sw were 0.0959 and 0.9954, respectively.

QSRR Prediction of the Chromatographic Retention Behavior of Painkiller Drugs

Abstract: Quantitative structure-retention relationship (QSRR) analysis is a useful technique capable of relating chromatographic retention time to the chemical structure of a solute. A QSRR study has been carried out on the reversed-phase high-performance liquid chromatography retention times (log tR) of 62 diverse drugs (painkillers) by using molecular descriptors. Multiple linear regression (MLR) is utilized to construct the linear QSRR model. The applied MLR is based on a variety of theoretical molecular descriptors selected by the stepwise variable subset selection procedure. Stepwise regression was employed to develop a regression equation based on 50 training compounds, and predictive ability was tested on 12 compounds reserved for that purpose. The geometry of all drugs was optimized by the semi-empirical method AM1 and used to calculate different molecular descriptors. The regression equation included three parameters: n-octanol-water partition coefficient (log P), molecular surface area, and hydrophilic-lipophilic balance of the drug molecules, all of which could be related to retention time property. Modeling of retention times of these compounds as a function of the theoretically derived descriptors was established by MLR. The results indicate that a strong correlation exists between the log tR and the previously mentioned descriptors for drug compounds. The prediction results are in good agreement with the experimental values.

QSPR modeling of stability constants of diverse 15-crown-5 ethers complexes using best multiple linear regression

Abstract: Quantitative structure–property relationships (QSPR) models for the stability constants of 58 complexes of 1,4,7,10,13-pentaoxacyclopentadecane ethers (15C5) were established with the Comprehensive Descriptors for Structural and Statistical Analysis (CODESSA) program. Experimental stability constants (log K) for a diverse set of 58 complexes of 15C5 structures are correlated with computed structural descriptors using CODESSA. Stability constants for complexes of 15C5 ethers with potassium cation (K+) have been determined at 25 °C in methanol solution. Standard quantum chemistry packages are used for optimizing the molecular geometry and for semi-empirical quantum computations. The QSPR model for the stability constants (log K) is obtained by selecting descriptors from a wide diversity of constitutional, geometrical, topological, electrostatic, quantum chemical, and thermodynamic molecular descriptors. The best multilinear regression method (BMLR) encoded in CODESSA software was used to select significant descriptors for building multilinear QSPR model and the predictive power of model is estimated with the leave-one-out (LOO) cross-validation method. The proposed model can be used for the prediction of the stability constants of 15C5 complexes. The best QSPR model with five descriptors has R 2 = 0.9452, s 2 = 0.0110, and F = 67.0312.

QSAR Modeling: Where have you been? Where are you going to?

Abstract: Quantitative structure–activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR stu...

Design of Antibacterial Agents: Alkyl Dihydroxybenzoates against Xanthomonas citri subsp. citri .

Abstract: Xanthomonas citri subsp. citri (Xcc) causes citrus canker, affecting sweet orange-producing areas around the world. The current chemical treatment available for this disease is based on cupric compounds. For this reason, the objective of this study was to design antibacterial agents. In order to do this, we analyzed the anti-Xcc activity of 36 alkyl dihydroxybenzoates and we found 14 active compounds. Among them, three esters with the lowest minimum inhibitory concentration values were selected; compounds 4 (52 μM), 16 (80 μM) and 28 (88 μM). Our study demonstrated that alkyl dihydroxybenzoates cause a delay in the exponential phase. The permeability capacity of alkyl dihydroxybenzoates in a quarter of MIC was compared to nisin (positive control). Compound 28 was the most effective (93.8), compared to compound 16 (41.3) and compound 4 (13.9) by percentage values. Finally, all three compounds showed inhibition of FtsZ GTPase activity, and promoted changes in protofilaments, leading to depolymerization, which prevents bacterial cell division. In conclusion, heptyl dihydroxybenzoates (compounds 4, 16 and 28) are promising anti-Xcc agents which may serve as an alternative for the control of citrus canker.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Abstract: Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.