QSAR Descriptors Research Articles

Exploring the electronic, optical, and bioactive properties for new modified fullerenes via molecular modeling

Surface decor of pure fullerene motifs is greatly impacted by COOH, NH2, and Li terminals. Using DFT, the electrical, vibrational, and optical characteristics of unadorned and decorated fullerenes are thoroughly examined. For suggested fullerenes, QSAR descriptors are met via the PM6 interface. Results indicated that C60–2Li might compete with current kesterite Cu2ZnGeSe4 solar cells, which have an efficiency of more than 14% and a band gap range of 1.5–1.7 eV. The C60-2Li can be a brand-new row of manufactured solar cells with exceptional efficiency. According to MESP contours, while additional COOH and NH2 retain the original nucleophilic additions, additive Li exhibits a blue shift in Fullerene electronegative behavior toward electrophilic adds. The C60–2NH2 is recognized as a possible bioactive substrate for drug delivery based on QSAR values. This paves the way towed multidisciplinary adoption for mentioned fullerene systems.

Modeling of Log Kow of a Series of PAHs using Computational Chemistry

The importance of Chemometrics Methods in Modeling (in QSAR analysis) of the mathematical model’s study of large datasets of molecules with huge numbers of physicochemical and structural parameters quantitative structure-Toxicity relationship (QSTR) are mainly based on multiple regression analysis in QSAR analysis The study of Least Square in deriving QSTR models for datasets of Quantitative Structure-Toxicity relationship on Log kow (Octanol-water partition coefficient) for 16 Hydrocarbons compounds has been using the software Hyperchem 6.3 for computing descriptors and MINITAB 16 for data modeling. A three -descriptors model [two electronics molecules’ descriptors (QSER descriptor), HOMO (is Highest occupied molecular orbital) and LUMO (Lowest unoccupied molecular orbital), one QSAR descriptor E_H (Hydration Energy) by Least Squares with correlation coefficient r=0.868, S=0.635, R2 = 75.4%, R2ajd=73.7% and Durbin-Watson statistic =1.85277 and graphical analysis by diagram of goodness of fit and line plot. The results statistical of new model after removing the aberrant compounds (Toxicity compounds) shows high Coefficient of correlation r=0.9581, S=0.4316, determination coefficient R2 =91. 8%, ajustemed R2ajd = 89.3%, Durbin-Watson statistic D=2.373, Three explanatory Variable model selected is robust and has good fitness. Two influential compounds detected and important the model and absence aberant compounds of the studied sample.

Chemical composition and QSAR descriptor of the dichloromethane extract of Moringa olifera (L.) flowers

The Moringa oleifera (Moringaceae) is one of these important plants, distributed in many tropical and sub-tropical countries, known with its medical uses of high nutritional value it is called the "tree of life". The Composition profile of the dichloromethane extract of the flowers of Moringa oleifera growing in Algerian desert was studied by capillary gas chromatography-mass spectrometry (GC-MS). thirty four Compounds were identified; Within the limits of the applied experimental conditions and by interpreting the mass spectra obtained and comparing it with the mass spectra of the reference materials, we record the possibility of the presence of the following chemical compounds: Hydrocarbons, Organic acids, Esters, Aldehydes Aromatic compounds and Nitrocyclic compounds, which major compounds are : (Z) - (9,17Octadecadienal (100%), Octadecanoic acid (60.4%), Heneicosane (10.186%), (Z)-9 Octadecenoic acid (12.711%), Heptadecanoic acid (13.742%), Hexadecane, 2,6,10,14-tetramethyl (18.988%), Heptadecane (14.866%) and Eicosane (17.535%). Analysis of QSAR descriptor from the results revealed that major constituents of the dichloromethane extract of Moringa olifera (L.) flowers.

Fish as a bioindicator of polycyclic aromatic hydrocarbon pollution in aquatic ecosystem of Ogun and Eleyele Rivers, Nigeria, and risk assessment for consumer's health

Ogun and Eleyele Rivers in Nigeria are known to be prone to industrial wastes pollution. Therefore, assessing Polyaromatic Hydrocarbon (PAH) contamination in fish tissue harvested from the two rivers is considered highly important. Polycyclic aromatic hydrocarbon (PAH) in the fish tissue was extracted and identified by Gas-Chromatographic Flame Ionization Detector (GC-FID). Health Risk Assessment was done by estimating the Targeted Hazard Quotient (THQ), Daily Intake (EDI), and Carcinogenic Risk (CR) of the PAHs. The diagnostic ratio was used for the determination of source pollution of the PAH.Principal Component Analysis (PCA) was employed for the analysis of PAH distribution in the tissue of the fish species using Minitab Statistical Software. QSAR descriptor was generated with PADEL tool, and the analysis was done on the DTC laboratory. Fourteen PAHs were identified in the tissue of Sarotherodon melanotheron and Clarias gariepinus obtained from the two rivers. Benzo[a]pyrene concentration in most of the fish tissue exceeded 2 µg/g set by the European union. PCA showed mixed distribution of low and high molecular weight PAHs in the tissues of fish. The diagnostic ratio showed that the PAH are majorly of petrochemical and minor pyrogenic source. The EDI for naphthalene exceeded the set limit in the fish tissue. The incremental lifetime cancer risk exceeds 10−4 indicating possible cancer risk to the consumers. The QSAR analysis resulted in the generation of highly predictive and robust model with VR2-Dzs, VR1-Dt and SPMin7_Bhi descriptors serving as the major structural attributes contributing to the acute toxicity of the identified PAH. Our result showed that consumption of fish obtained from the study sites pose some health concerns, therefore necessary governmental interventions are required for pollution control of the Ogun and Eleyele rivers.

Extended continuous similarity indices: theory and application for QSAR descriptor selection.

Extended (or n-ary) similarity indices have been recently proposed to extend the comparative analysis of binary strings. Going beyond the traditional notion of pairwise comparisons, these novel indices allow comparing any number of objects at the same time. This results in a remarkable efficiency gain with respect to other approaches, since now we can compare N molecules in O(N) instead of the common quadratic O(N2) timescale. This favorable scaling has motivated the application of these indices to diversity selection, clustering, phylogenetic analysis, chemical space visualization, and post-processing of molecular dynamics simulations. However, the current formulation of the n-ary indices is limited to vectors with binary or categorical inputs. Here, we present the further generalization of this formalism so it can be applied to numerical data, i.e. to vectors with continuous components. We discuss several ways to achieve this extension and present their analytical properties. As a practical example, we apply this formalism to the problem of feature selection in QSAR and prove that the extended continuous similarity indices provide a convenient way to discern between several sets of descriptors.

Development of potential inhibitors of cell division protein kinase 2 by ligand based drug design

Cyclin-dependent kinases (CDKs) are commonly known by their role in cell cycle regulation which affects cancer mechanism. In many cancer types, CDKs show extreme activity or CDK inhibiting proteins are dysfunctional. Specifically, CDK2 plays an indispensable role in cell division especially in the G1/S phase and DNA damage repair. Therefore, it is important to find new potential CDK2 inhibitors. In this study, ligand-based drug design is used to design new potential CDK2 inhibitors. Y8 L ligand is obtained from the X-ray crystal structure of human CDK2 (PDB ID: 2XNB) (www.pdb.org) and used as a structure model. By adding hydrophilic and hydrophobic groups to the structure, a training set of 36 molecules is generated. Each molecule examined with Spartan’14 and optimized structures are used for docking to CDK2 structure by AutoDock and AutoDock Vina programs. Ligand-amino acid interactions are analysed with Discovery Studio Visualizer. Van der Waals, Pi-Pi T-shaped, alkyl, pi-alkyl, conventional hydrogen bond and carbon-hydrogen bond interactions are observed. By docking results and viewed interactions, some molecules are identified and discussed as potential CDK2 inhibitors. Additionally, 8 different QSAR descriptors obtained from Spartan’14, Preadmet and ALOGPS 2.1 programs are investigated with multiple linear regulation (MLR) analysis with SPSS program for their impact on affinity value.

Molecular dynamics simulation, 3D-pharmacophore and scaffold hopping analysis in the design of multi-target drugs to inhibit potential targets of COVID-19

SARS-CoV-2 has posed serious threat to the health and has inflicted huge costs in the world. Discovering potent compounds is a critical step to inhibit coronavirus. 3CLpro and RdRp are the most conserved targets associated with COVID-19. In this study, three-dimensional pharmacophore modeling, scaffold hopping, molecular docking, structure-based virtual screening, QSAR-based ADMET predictions and molecular dynamics analysis were used to identify inhibitors for these targets. Binding free energies estimated by molecular docking for each ligand in different binding sites of RdRp were used to predict the active site. Previously reported active 3CLpro and RdRp inhibitors were used to build a pharmacophore model to develop different scaffolds. Structure-based simulations and pharmacophore modeling based on Hip Hop algorithm converged in a state that suggest hydrogen bond acceptor and donor features have a critical role in the two binding sites. Further validations indicated that the best pharmacophore model has fairly good correlation values compared with approved inhibitors. Structure-based simulation results approved that GLu166 and Gln189 in 3CLpro and Lys551 and Glu811 in RdRp, are critical residues for dual activities. Ten compounds were extracted from pharmacophore-based virtual screening in six databases. The results, gained by repurposing approach, suggest the effectiveness of these ten compounds with different scaffolds as possible inhibitors of the two targets. Some quinoline-based hybrid derivatives also were designed. QSAR descriptors plot predicted that the scaffolds have had accepted pharmacokinetic profiles. Multiple molecular dynamics simulations in 100 ns and MM/PBSA studies of some reference inhibitors and the novel compounds in complex with both targets demonstrated stable complexes and confirmed the interaction modes. Based on different computational methods, COVID-19 multi-target inhibitors are proposed. Communicated by Ramaswamy H. Sarma

Molecular Modeling of Substances Isolated from the Essential Oil of the Species Drimys angustifolia and Drimys brasiliensis

Objectives: Carry out an in silico study of chemical substances isolated from the species: Drimys angustifolia and Drimys brasiliensis. Methods: A theoretical study of global reactivity, QSAR descriptors, MEP construction and molecular docking was performed to analyze the interaction of substances with acetylcholinesterase of Drosophila melanogaster and the prediction of skin permeation and toxicological properties of the substances. Results: Chemical reactivity and molecular stability investigation suggest that the substance which presents stability values similar to the standard substance D-limonene, was Terpinen-4-ol. The MEPs of the investigated substances were evenly distributed along the hydrogens and oxygens. The molecular docking studies suggest interesting and promising results for the substance Myristicin. Regarding skin permeability, the results suggests low skin absorption for all substances. Regarding toxicological properties, bicyclogermacrene indicated non-carcinogenic and mutagenic activity. Conclusion: Our results suggest that the substance, bicyclogermacrene, is a potential candidate for usage as a repellent.

RETRACTED: Development of a simple QSAR model for reliable evaluation of acetylcholinesterase inhibitor potency

With the aging of the western population, more and more people are affected by the neurodegenerative Alzheimer's and Parkinson's disease. Inhibitors of acetylcholinesterase (AChE) have proven to be effective in the treatment of disease symptoms. We report the QSAR regression model for the estimation of potency of a set of 94 structurally diverse compounds (oximes, N-hydroxyiminoacetamides, 4-aminoquinolines and flavonoids) to inhibit AChE, pKi (AChE). The model is based on three simple descriptors: the valence molecular connectivity index of the zero-order, 0χv, combined with the number of 10-membered rings (nR10) and number of hydroxyl groups in a molecule (nOH). QSAR model yielded r = 0.947, S.E. = 0.51 and S.E.cv= 0.53; the range of pKi (exp) = 6.03. It showed its stability when the set of 94 compounds was enlarged, comprising 184 compounds in total (r = 0.886, S.E. = 0.85 and S.E.cv = 0.88; the range of pKi (exp) = 10.21), resulting in regression parameters which were similar, although only for 0χv coefficients within the limits of S.E. (0.167(13) and 0.172(16) for the set with 94 and 184 compounds, respectively. The predictive power of the model was shown by the prediction of pKi values for 61 randomly chosen compounds (S.E.test = 0.86) from the calibration model made on the other 123 compounds (S.E. = 0.85), all taken from the pool of 184 compounds. QSAR descriptors 0χv, nR10 and nOH were well chosen for describing the interactions of the AChE active site (amino acid interaction) with ligands through the estimation of the inhibitory potency.

ICMMS-2: Application of Cs/ZnO/GO Hybrid Nanocomposite for Enhanced Inter-behavior of Electronic Properties and Thermal Stability as Corrosion Inhibitor

Polymer hybrid nanocomposites are considered to be one of the most promising materials which in turn acquires new, multiple and improved properties such as optical properties and electrical conductivity used for various applications in the fields of optical integrated circuits, sensors, adhesives, coatings and corrosion inhibitors for metal protection. At the present time graphene oxide hybridization with metal oxide modified carbohydrate polymers performance for corrosion inhibition for metal. Hybrid nanocomposite Cs/ZnO/GO was considered to be studied by DFT theory. The considered Cs/ZnO/GO model structure were studied in the two interaction states once as adsorb state and once as complex state. Hybrid nanocomposite electronic properties and thermal stability in all designated states have been evaluated. B3LYP/LANL2DZ was used to calculate TDM, HOMO/LUMO band gap energy and MESP for hybrid Cs/ZnO/GO nano composite assumptions. Additionally, QSAR descriptors for thermal stability study were also calculated for the same interactions. The most certainly stable structure of the hybrid nanocomposite Cs/ZnO/GO was found to occur through OH of the carboxyl group COOH of GO in complex mechanism and through O of the carboxyl group COOH of GO in adsorb mechanism. It is also found that the structure Cs/ZnO/GO occurs by O of the carboxyl group COOH of GO in adsorb mechanism has a significant improvement in electronic properties and thermal stability with band gap 0.1750 eV that it could be used as Corrosion Inhibitor.

Development of natural polymer/metal oxide nanocomposite reinforced with graphene oxide for optoelectronic applications

ABSTRACT Nanocomposite natural polymer/metal oxide (NP/MO) is an attractive new technology for numerous applications as sensors, coatings, electronic devices, adhesives and optical circuits. NP includes cellulose Cel, chitosan Cs and sodium alginate NaAlg while MO are ZnO and CuO. Furthermore, modification of NP/MO with graphene oxide (GO) increases efficiency and stability of NP/MO in all its applications and fields. Theoretical calculations using density functional theory (DFT) were conducted to study the effect of using GO on electronic properties and thermal stability of NP/MO. Total dipole moment (TDM), HOMO/LUMO band gap energy (∆E) and molecular electrostatic potential (MESP) were computed for the interaction of GO with NP/MO at B3LYP/LANL2DZ. Moreover, QSAR descriptors were also calculated for the same interactions. It was found that GO/NP/MO structure is auspicious for new materials with the efficient expense, more stable (physically and chemically), thermally stability and more sensitive, particularly GO/Cel/MO and GO/Cs/MO, while GO/Nav Alg/MO did not make significant changes.

Molecular Modeling Analyses for Modified Biopolymers

Biopolymer blends and structural modifications with phenol and hydroxymethylcarbonyl (HMC) are studied to show the ability to interact with amino acids as promising to act as HIV protease inhibitors. Chitosan (Cs), cellulose (Cel), starch (Str) and gelatin (Gel) as well as their blends as Cs/Cel; Cs/Str; Cs/Gel with ratios 3:1; 2:2; 1:3 were subjected to molecular modeling. These biopolymers, as well as their blends, are calculated with quantum mechanical calculations at PM6 level of theory. QSAR, surface area, and volume properties of the interaction of phenol and HMC upon Cs/Cel; Cs/Str; Cs/Gel in the different positions of the four units are calculated at the same level of theory. QSAR descriptors for studied polymers show a change in their physical properties as result of blending. Depending on QSAR calculations, the interaction of phenol and HMC with Cs/Cel and Cs/Str blends for ratio 1:3 through the first unit increases the reactivity of these modified structures. The solubility of modified blends is increased by increasing chitosan units in the proposed modified blends. The surface area of modified Cs/Cel ratios increases comparing with modified Cs/Str and Cs/Gel ratios. This recommends the modified blends of Cs/Cel ratio can be used as promising HIV protease inhibitors drugs.

Molecular modeling analyses for graphene functionalized with Fe3O4 and NiO

Graphene has attracted great concern in recent years as one of the potential 2D materials in various applications. This work is devoted for assessing the feasibility of functionalizing 2D graphene sheets with ferromagnetic and antiferromagnetic metal oxides namely magnetite (Fe3O4) and nickel oxide (NiO). Molecular models of the proposed candidates are exposed to energy calculations at DFT level, in addition to geometry optimization processes at PM6 method. HOMO/LUMO orbitals, MESP maps and QSAR descriptors are calculated. Results ensure that graphene doped with NiO has the highest reactivity since it possesses the largest TDM and the smallest HOMO/LUMO band gap. MESP maps illustrate that the benzene rings of graphene are most probable to undergo nucleophilic interactions. Addition of Fe3O4 creates new negatively charged active sites that are ready for nucleophilic interactions. The calculated QSAR parameters demonstrate a hydrophobic nature for pure and modified graphene suggesting that they need further modification with further groups for usage in biological applications.

Removal of pharmaceuticals from aquatic environment using modified biomaterials

Pharmaceuticals in the environment are of growing interest as a result of continuous occurrence through human into municipal wastewater without adequate treatment. Green methods are needed to overcome this problem without adverse impact on the environment. In this work, modified nanobiopolymers have been dedicated to solve this problem. Accordingly, modified chitosan with nanosilica is proposed to mediate ibuprofen. Spectroscopic analysis was carried outbased on both experimental techniques such as FTIR spectrophotometry and theoretical approach represented in semiemprical calculations. QSAR descriptors were also conducted in order to quantify biological activities of both structures. Results of FTIR ensure that chitosan can be modified using silica through the formation of both chemical and physical bonds. Furthermore, FTIR shows that spectra of chitosansilica after interaction with ibuprofen suffers from a general reduction in all of its peaks and shifting in many bands. Significant reduction in bands of –NH bending and –CH stretching proposed that chitosan may interact with ibuprofen through electrostatic and hydrophobic mechanisms. PM6 calculations illustrate that amine group of modified chitosan is the most probable active site for interaction either in the complex or adsorb forms. The calculated vibrational frequencies ensure that the calculated structures correspond to minimum energy structures.

Exploring the Use of Compound-Induced Transcriptomic Data Generated From Cell Lines to Predict Compound Activity Toward Molecular Targets.

Pharmaceutical or phytopharmaceutical molecules rely on the interaction with one or more specific molecular targets to induce their anticipated biological responses. Nonetheless, these compounds are also prone to interact with many other non-intended biological targets, also known as off-targets. Unfortunately, off-target identification is difficult and expensive. Consequently, QSAR models predicting the activity on a target have gained importance in drug discovery or in the de-risking of chemicals. However, a restricted number of targets are well characterized and hold enough data to build such in silico models. A good alternative to individual target evaluations is to use integrative evaluations such as transcriptomics obtained from compound-induced gene expression measurements derived from cell cultures. The advantage of these particular experiments is to capture the consequences of the interaction of compounds on many possible molecular targets and biological pathways, without having any constraints concerning the chemical space. In this work, we assessed the value of a large public dataset of compound-induced transcriptomic data, to predict compound activity on a selection of 69 molecular targets. We compared such descriptors with other QSAR descriptors, namely the Morgan fingerprints (similar to extended-connectivity fingerprints). Depending on the target, active compounds could show similar signatures in one or multiple cell lines, whether these active compounds shared similar or different chemical structures. Random forest models using gene expression signatures were able to perform similarly or better than counterpart models built with Morgan fingerprints for 25% of the target prediction tasks. These performances occurred mostly using signatures produced in cell lines showing similar signatures for active compounds toward the considered target. We show that compound-induced transcriptomic data could represent a great opportunity for target prediction, allowing to overcome the chemical space limitation of QSAR models.

Effect of nano metal oxides on heme molecule: molecular and biomolecular approaches

Interaction of components of living cells with various nanomaterials in the gas phase has been one of extensive concern since they become intensively utilized in various life aspects. This work is carried out to investigate the interaction between heme molecule, as the main component of hemoglobin, with several familiar and non-familiar divalent structures such as O2, CO2, CO, MgO, CoO, NiO, CuO and ZnO. Geometry optimization processes as well as QSAR descriptors are conducted using semiemprical quantum mechanical calculations at PM6 level. Results illustrate that adsorbing O2 and CO on heme lowers their TDM helping heme in performing its transportation function and not interacting with other species. On the other hand, when CoO and ZnO interacting with heme the TDM of the resultant structures increase greatly reflecting high reactivity which may interact with other species more than performing its function. Therefore, interacting species other than O2 may disturb the transportation function of heme structure. QSAR data of IP regarding interaction of O2 with heme ensure the TDM result that reflects lowering its activity. IP of H-CO adsorbed is the lowest indicating high reactivity while those of H-O2, H-CO2, H-MgO and H-NiO in the complex form are the highest values indicating that it is difficult to form a complex structure with them. Therefore, heme interactions with structures rather than O2 and CO2 affects negatively its function as gas transporter.

Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images.

The majority of computational methods for predicting toxicity of chemicals are typically based on "nonmechanistic" cheminformatics solutions, relying on an arsenal of QSAR descriptors, often vaguely associated with chemical structures, and typically employing "black-box" mathematical algorithms. Nonetheless, such machine learning models, while having lower generalization capacity and interpretability, typically achieve a very high accuracy in predicting various toxicity endpoints, as unambiguously reflected by the results of the recent Tox21 competition. In the current study, we capitalize on the power of modern AI to predict Tox21 benchmark data using merely simple 2D drawings of chemicals, without employing any chemical descriptors. In particular, we have processed rather trivial 2D sketches of molecules with a supervised 2D convolutional neural network (2DConvNet) and demonstrated that the modern image recognition technology results in prediction accuracies comparable to the state-of-the-art cheminformatics tools. Furthermore, the performance of the image-based 2DConvNet model was comparatively evaluated on an external set of compounds from the Prestwick chemical library and resulted in experimental identification of significant and previously unreported antiandrogen potentials for several well-established generic drugs.

The Quantum Chemical and QSAR Studies on Acinetobacter Baumannii Oxphos Inhibitors.

Acinetobacter is a Gram-negative, catalase-positive, oxidase-negative, non-motile, and no fermenting bacteria. In this study, some of the electronic and molecular properties, such as the highest occupied molecular orbital energy (EHOMO), lowest unoccupied molecular orbital energy (ELUMO), the energy gap between EHOMO and ELUMO, Mulliken atomic charges, bond lengths, of molecules having impact on antibacterial activity against A. baumannii were studied. In addition, calculations of some QSAR descriptors such as global hardness, softness, electronegativity, chemical potential, global electrophilicity, nucleofugality, electrofugality were performed. The descriptors having impact on antibacterial activity against A. baumannii have been investigated based on the usage of 29 compounds employing two statistical methods called Linear Regression and Artificial Neural Networks. Artificial Neural Networks obtained accuracies in the range of 83-100% (for active/inactive classifications) and q2=0.63 for regression. Three ANN models were built using various types of descriptors with publicly available structurally diverse data set. QSAR methodologies used Artificial Neural Networks. The predictive ability of the models was tested with cross-validation procedure, giving a q2=0.62 for regression model and overall accuracy 70-95 % for classification models.

Docking and QSAR Studies of Aryl-valproic Acid Derivatives to Identify Antiproliferative Agents Targeting the HDAC8.

Histone deacetylase 8 (HDAC8) is a plausible target for the development of novel anticancer drugs using a metal-chelating group and hydrophobic moieties as pharmacophores. It is known that valproic acid (administered as its salt, sodium valproate; VPANa+) is an HDAC8 inhibitor characterized by its hydrophobic chains. Nevertheless, VPA is hepatotoxic and VPA analogues might be explored for less hepatotoxic antiproliferative compounds. In this work, docking and QSAR studies of 500 aryl-VPA derivatives as possible HDAC8 inhibitors were performed in order to explore and select potential anti-proliferative compounds. Docking results identified π-π, hydrogen bonds as the most important noncovalent interactions between HDAC8 (PDB: 3F07) and the ligands tested, whereas Belm4 was the best QSAR descriptor and classified as a 2D-BCUT descriptor. Based on theoretical studies, compound DAVP042 was synthesized and evaluated in vitro for its antiproliferative activities on several cancer cell lines (A549-lung, MCF-7-breast, HCT116-colon and U937- lymphoid tissue) in comparison to VPA, as well as for its inhibitory activity on HDAC8 using in vitro models. DAVP042 demonstrated to have antiproliferative activity on all cancer cell lines employed, not only suggesting that this compound should be further studied, but also demonstrating that the methodology herein employed is appropriated to identify new therapeutic candidates.

Spectroscopic Analyses of Modified Fulleropyrrolidine Derivatives

Fullerene (C60) is enhanced with pyrrolidine group to produce fulleropyrrolidine which is considered as one of the most important derivatives of fullerene. Fulleropyrrolidine is further modified in order to enhance its solubility which in turn could enhance its biological applications. Accordingly this work is dedicated to modify fulleropyrrolidine carbodithioic acid as NO2 group introduced at meta position. Quantitative structure-activity relationship models (QSAR) was utilized to evaluate the biological activates of the investigated compounds through some descriptors. Later on chalcogenide could be subtitled in order to form three derivative groups. The QSAR descriptors were compared with the QSAR of the parent compound. Results indicate that, NO2 group enhances the biological activity.