Three dimensional quantitative structure activity relationship between diazabicyclo[4.2.0]octanes and nicotinic acetylcholine receptor (h alpha4beta2 and h alpha3beta4) agonists was studied using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). From 11 CoMFA and CoMSIA models, CoMSIA with steric and electrostatic fields gave the best predictive models (q2=0.926 and 0.945, r2(ncv)=0.983 and 0.988). This study can be used to develop potent h alpha4beta2 receptor agonists with low activity on h alpha3beta4 subtype.
Quantitative Structure-Activity Relationship ; Receptors, Nicotinic

The chemical characterization analysis of a medical device often results in chemical substances with unknown toxicities. While identification of each individual toxicity could result in a time-consuming hurdle with tremendous labor and financial burden, quantitative structure-activity relationship (QSAR) is of great significance for toxicity risk assessment of such chemical substances. By establishing quantitative relationship between the molecular structures or active groups of similar chemical compounds with their biological activities, QSAR can be utilized to predict the toxicity of such target compounds with significantly reduced cost and time. In this article, the authors generally summarized the mechanisms of QSAR approaches, current applications of QSAR modeling in the field of medical device, an introduction of the characteristics of publicly and commercially-available QSAR software, and briefly explored future trends of QSAR modeling in medical device toxicological risk assessment. The utilization of QSAR would undoubtedly further advance the toxicological risk assessment of medical devices.
Quantitative Structure-Activity Relationship ; Risk Assessment ; Software

OBJECTIVES: For successful adoption of legislation controlling registration and assessment of chemical substances, it is important to obtain sufficient toxicological experimental evidence and other related information. It is also essential to obtain a sufficient number of predicted risk and toxicity results. Particularly, methods used in predicting toxicities of chemical substances during acquisition of required data, ultimately become an economic method for future dealings with new substances. Although the need for such methods is gradually increasing, the-required information about reliability and applicability range has not been systematically provided. METHODS: There are various representative environmental and human toxicity models based on quantitative structure-activity relationships (QSAR). Here, we secured the 10 representative QSAR-based prediction models and its information that can make predictions about substances that are expected to be regulated. We used models that predict and confirm usability of the information expected to be collected and submitted according to the legislation. After collecting and evaluating each predictive model and relevant data, we prepared methods quantifying the scientific validity and reliability, which are essential conditions for using predictive models. RESULTS: We calculated predicted values for the models. Furthermore, we deduced and compared adequacies of the models using the Alternative non-testing method assessed for Registration, Evaluation, Authorization, and Restriction of Chemicals Substances scoring system, and deduced the applicability domains for each model. Additionally, we calculated and compared inclusion rates of substances expected to be regulated, to confirm the applicability. CONCLUSIONS: We evaluated and compared the data, adequacy, and applicability of our selected QSAR-based toxicity prediction models, and included them in a database. Based on this data, we aimed to construct a system that can be used with predicted toxicity results. Furthermore, by presenting the suitability of individual predicted results, we aimed to provide a foundation that could be used in actual assessments and regulations.
Humans ; Quantitative Structure-Activity Relationship* ; Reproducibility of Results ; Social Control, Formal

Computational virtual screening has become an essential platform of drug discovery for the efficient identification of active candidates. Moleculardocking, a key technology of receptor-centric virtual screening, is commonly used to predict the binding affinities of chemical compounds on target receptors. Despite the advancement and extensive application of these methods, substantial improvement is still required to increase their accuracy and time-efficiency. Here, we evaluate several advanced structure-based virtual screening approaches for elucidating the rank-order activity of chemical libraries, and the quantitative structureactivity relationship (QSAR). Our results show that the ensemble-average free energy estimation, including implicit solvation energy terms, significantly improves the hit enrichment of the virtual screening. We also demonstrate that the assignment of quantum mechanical-polarized (QM-polarized) partial charges to docked ligands contributes to the reproduction of the crystal pose of ligands in the docking and scoring procedure.
Drug Discovery* ; Ligands ; Mass Screening* ; Quantitative Structure-Activity Relationship ; Reproduction ; Small Molecule Libraries

By using the fuzzy min-max neural network, the quantitative structure-activity relationship (QSAR) of mutagenicity is studied. With the established QSAR model, the mutagenicity is predicted and the results showed that QASR is superior to linear-regression model. Further discussion on the models and the results is presented in this paper.
Algorithms ; Cluster Analysis ; Fuzzy Logic ; Models, Chemical ; Neural Networks (Computer) ; Quantitative Structure-Activity Relationship

Quantitative structure-property relationships (QSPR) were developed to predict the pK(a) values of sulfa drugs via heuristic method (HM) and gene expression programming (GEP). The descriptors of 31 sulfa drugs were calculated by the software CODESSA, which can calculate constitutional, topological, geometrical, electrostatic, and quantum chemical descriptors. HM was also used for the preselection of 4 appropriate molecular descriptors. Linear and nonlinear QSPR models were developed based on the HM and GEP separately and two prediction models lead to a good correlation coefficient (R) of 0.90 and 0.95. The two QSPR models are tseful in predicting pK(a) during the discovery of new drugs and providing theory information for studying the new drugs.
Algorithms ; Gene Expression ; Models, Chemical ; Quantitative Structure-Activity Relationship ; Software ; Sulfonamides ; chemistry

The molecular electronegativity-distance vector (MEDV) was used to describe the chemical structural characterization of 46 components of essential oils in the flower of Rosa banksiae. Various multiple linear regression (MLR) models were created with variable screening by the stepwise multiple regression technique and statistics. The QSRR models of 10 and 6 variables were built by MLR with the correlation coefficients (R) of molecular modeling being 0.906 and 0.903. Cross-validation of the models, which contain selected vectors were performed by leave-one -out procedure (LOO) and the satisfied results with correlation coefficients (Rcv) of 0.904 and 0.903, respectively. The results showed that the models constructed can provide estimation stability and favorable predictive ability.
Flowers ; chemistry ; Linear Models ; Molecular Structure ; Oils, Volatile ; chemistry ; Plants, Medicinal ; chemistry ; Quantitative Structure-Activity Relationship ; Regression Analysis ; Rosa ; chemistry

Based on ninety three acetylcholinesterase inhibitors (AChEIs) which have the same mechanism of action but are different in structural characteristics, the pharmacophore model for acetylcholinesterase inhibitor was constructed by the CATALYST system. The optimal pharmacophore model with three hydrophobic units, a ring aromatic unit and a hydrogen-bond acceptor unit were confirmed (Weight = 3.29, RMS = 0.53, total cost-null cost = 62.75, Correl = 0.93, Config = 19.05). This pharmacophore model will act on the double active site of acetylcholinesterase and is able to predict the activity of known acetylcholinesterase inhibitors that are used for clinical treatment of Alzheimer's disease (AD), and can be further used to identify structurally diverse compounds that have higher activity treating with Alzheimer's disease (AD) by virtual screening.
Acetylcholinesterase ; chemistry ; metabolism ; Alzheimer Disease ; enzymology ; prevention & control ; Cholinesterase Inhibitors ; chemistry ; classification ; therapeutic use ; Drug Design ; Humans ; Models, Chemical ; Models, Molecular ; Molecular Structure ; Quantitative Structure-Activity Relationship ; Structure-Activity Relationship

Both AM1 semi-empirical quantum chemistry method and HF/3-21g* ab initio method were employed to get related parameters or descriptors, particularly, the parameters of the solvation energy delta G with polarizable continuum model, for 42 anti-HIV 5-phenyl-1-phenylamino-1H-imidazole derivatives with known cytotoxicity. With parameters of quantum chemical calculation and traditional ones, 2 multiple linear regression models were obtained. The better regression equation has a high correlation coefficient (r = 0.938) and a low standard deviation (s = 0.125) and the squared correlation coefficient Q2 of the cross-validation is 0.799 (literaure: 0.740) by leave-one-out method. The results have certain significance for the design of new anti-HIV-1 drugs with lower cytotoxicity.
Anti-HIV Agents ; chemistry ; pharmacology ; toxicity ; Imidazoles ; chemistry ; pharmacology ; toxicity ; Linear Models ; Models, Chemical ; Quantitative Structure-Activity Relationship

Today, the understanding of the sequence and structure of biologically relevant targets is growing rapidly and researchers from many disciplines, physics and computational science in particular, are making significant contributions to modern biology and drug discovery. However, it remains challenging to rationally design small molecular ligands with desired biological characteristics based on the structural information of the drug targets, which demands more accurate calculation of ligand binding free-energy. With the rapid advances in computer power and extensive efforts in algorithm development, physics-based computational chemistry approaches have played more important roles in structure-based drug design. Here we reviewed the newly developed computational chemistry methods in structure-based drug design as well as the elegant applications, including binding-site druggability assessment, large scale virtual screening of chemical database, and lead compound optimization. Importantly, here we address the current bottlenecks and propose practical solutions.
Computational Biology ; Drug Design ; Drug Discovery ; High-Throughput Screening Assays ; Molecular Docking Simulation ; Molecular Dynamics Simulation ; Quantitative Structure-Activity Relationship