Quantum probability ranking principle for ligand-based virtual screening

Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.     

Development of a minimal kinase ensemble receptor (MKER) for surrogate AutoShim

The target-tailored 3-D virtual screening (VS) method "Surrogate AutoShim" adds pharmacophoric shims to a 16-kinase crystal structure "Universal Kinase Ensemble Receptor" (UKER) to generate highly predictive, target-customized docking models. Predocking a corporate archive of millions of compounds into the 16-structure ensemble takes months. However, since the 16 UKER structures are always the same, docking need only be done once. The predocked results are then "shimmed" to reproduce experimental training data for any number of additional kinases far more accurately than conventional docking. Training new kinase models and predicting activity for millions of predocked compounds against dozens of kinases takes only hours. However reducing the predocking time would make the method even more advantageous. Sequential Floating Forward Search (SFFS) was employed to rationally identify a reduced subset using only 8 of the 16 structures, a "Minimal Kinase Ensemble Receptor" (MKER) that preserved the predictive accuracy for 20 kinase models. Furthermore, a performance evaluation of this subset on an extended set of 52 kinase targets and 100,000 compounds showed statistical model performance comparable to the original UKER. The MKER has halved the time for predocking large databases of internal and commercial compounds. For ad hoc virtual libraries, where predocking is not possible, 2- or 3-kinases "Approximate Kinase Ensemble Receptors" (AKER) were also identified with only a modest loss of prediction accuracy.     

Joint QSAR analysis using the Free-Wilson approach and quantum chemical parameters

A new quantitative structure-activity relationship (QSAR) technique combining the Free-Wilson method and constructed quantum chemical parameters was used to simulate the aqueous solubility (Sw), 1-octanol/water partition coefficient (Kow) of 14 new synthesized benzanilide derivatives and their 96 h acute toxicity (EC50) to Daphnia magna. The mode of action of the 14 selected compounds to Daphnia magna was shown to be a complex process involving a physical partition stage and a bio-chemical reaction stage. The results also indicated that the joint (QSAR) analysis was much effective than the original Free-Wilson method and Hansch method not only in predicting properties/toxicity, but also in investigating the mode of action of chemicals.     

Identification of a minimal subset of receptor conformations for improved multiple conformation docking and two-step scoring

Docking and scoring are critical issues in virtual drug screening methods. Fast and reliable methods are required for the prediction of binding affinity especially when applied to a large library of compounds. The implementation of receptor flexibility and refinement of scoring functions for this purpose are extremely challenging in terms of computational speed. Here we propose a knowledge-based multiple-conformation docking method that efficiently accommodates receptor flexibility thus permitting reliable virtual screening of large compound libraries. Starting with a small number of active compounds, a preliminary docking operation is conducted on a large ensemble of receptor conformations to select the minimal subset of receptor conformations that provides a strong correlation between the experimental binding affinity (e.g., Ki, IC50) and the docking score. Only this subset is used for subsequent multiple-conformation docking of the entire data set of library (test) compounds. In conjunction with the multiple-conformation docking procedure, a two-step scoring scheme is employed by which the optimal scoring geometries obtained from the multiple-conformation docking are re-scored by a molecular mechanics energy function including desolvation terms. To demonstrate the feasibility of this approach, we applied this integrated approach to the estrogen receptor alpha (ERalpha) system for which published binding affinity data were available for a series of structurally diverse chemicals. The statistical correlation between docking scores and experimental values was significantly improved from those of single-conformation dockings. This approach led to substantial enrichment of the virtual screening conducted on mixtures of active and inactive ERalpha compounds.     

Use of monomer fraction data in the parametrization of association theories

Association theories such as the CPA (cubic-plus-association), NRHB (non-random hydrogen bonding) equations of state and the various variants of SAFT (statistical associating fluid theory) have been extensively applied to phase equilibrium calculations. Such models can also be used for estimating the monomer fraction of hydrogen bonding compounds and their mixtures. Monomer fraction data are obtained from spectroscopic measurements and they are available for a few compounds such as pure water and alcohols as well as for some alcohol–alkane and similar mixtures. These data are useful for an understanding of the capabilities and limitations of association models. The purpose of this work is two-fold: (i) to compare the performance of three models, CPA, NRHB and sPC-SAFT, in predicting the monomer fraction of water, alcohols and mixtures of alcohol-inert compounds and (ii) to investigate whether “improved” model parameters can be obtained if monomer fraction data are included in the parameter estimation together with vapor pressures and liquid densities. The expression “improved” implies parameters which can represent several pure compound properties as well as monomer fraction data for pure compounds and mixtures. The accuracy of experimental monomer fraction data is discussed, as well as the role of monomer fraction data in clarifying which association scheme should be used in these equations of state. The results reveal that the investigated association models (CPA, sPC-SAFT and NRHB) can predict, at least qualitatively correct, monomer fractions of associating compounds and mixtures. Only, small differences are observed between the models. In addition, it has been shown that, using a suitable association scheme, a single set of parameters can describe satisfactorily vapor pressures, liquid densities and monomer fractions of water and alcohols. The 4C scheme is the best choice for water, while for methanol there is small difference between the 2B and 3B association schemes.     

Optimising reversed-phase liquid chromatographic separation of an acidic mixture on a monolithic stationary phase with the aid of response surface methodology and experimental design

An optimization strategy for the separation of an acidic mixture by employing a monolithic stationary phase is presented, with the aid of experimental design and response surface methodology (RSM). An orthogonal array design (OAD) OA(16) (2(15)) was used to choose the significant parameters for the optimization. The significant factors were optimized by using a central composite design (CCD) and the quadratic models between the dependent and the independent parameters were built. The mathematical models were tested on a number of simulated data set and had a coefficient of R(2) > 0.97 (n = 16). On applying the optimization strategy, the factor effects were visualized as three-dimensional (3D) response surfaces and contour plots. The optimal condition was achieved in less than 40 min by using the monolithic packing with the mobile phase of methanol/20 mM phosphate buffer pH 2.7 (25.5/74.5, v/v). The method showed good agreement between the experimental data and predictive value throughout the studied parameter space and were suitable for optimization studies on the monolithic stationary phase for acidic compounds.     

A system for encoding and searching markush structures

The encoding and searching of generic chemical structures, so-called Markush structures, have received little attention in the literature of late. The ability to encode and search these complex entities is of use in various branches of chemoinformatics. We describe a general language for encoding Markush structures and algorithms for searching them and give three examples of the utility of such a system: development of general Free-Wilson analyses of chemical series, detection of controlled substances within a large database of molecular structures, and searching of large databases of virtual compounds.     

A Critical Evaluation of Bond Energy/ Group Contribution Methods of Calculating the Heat of Formation: Development of a New Generalized Scheme. Part I. Alkanes∗

Results of application of seven well-known bond energy/group contribution methods to the experimental data on heats of formation of 70 alkanes, including a few polymers, are reported. The earlier claims of accuracy of many of these schemes become untenable with the emergence of new data on nonanes and polymers, calling for more parameters to cope with the steric interaction energy in higher branched alkanes. A new general bond energy scheme is developed with low standard error of ±0.28 kcal/mole which is close to the experimental uncertainty. Heats of formation of some polyolefin structures are predicted for the experimental verification in the future. The energy terms of the new scheme are transferable to other non-hydrocarbon organic compounds for which a general scheme is under way.     

计算饱和醇异构体标准生成焓的新方法

基于极性叠加原理,在成功设计烷烃异构体和多氯代烷烃生成焓计算新方法的基础上,设计了一种计算多元醇异构体生成焓的新方法,并合理地假定任一异构体的原子化焓等于三种键(C-C、C-H和C-O-H键)的键能、极性叠加能项以及氢键能项的加和.用这一模型拟合24种原子化焓数据,得到了标准生成焓的估算公式.为了检验预测的精确性,又设计了一种预测方法,使用在排除被预测的化合物条件下回归得到的参数,预测该化合物的生成焓.按这种方法,预测了24种异构体的生成焓.通过该5参数预测的相对于实验值的各种误差(平均绝对误差、均方根误差和最大绝对误差)不仅比7参数的基团法预测的对应误差小得多,而且比相应实验数据的误差还要小.与键加和法比较,该方法的模型包含了极性叠加能和氢键能量,该两项代表了主要的非键相互作用能,表征了不同异构体的结构差异,并大大减少了参数.     

Evaluation of library ranking efficacy in virtual screening

We present the results of a comprehensive study in which we explored how the docking procedure affects the performance of a virtual screening approach. We used four docking engines and applied 10 scoring functions to the top-ranked docking solutions of seeded databases against six target proteins. The scores of the experimental poses were placed within the total set to assess whether the scoring function required an accurate pose to provide the appropriate rank for the seeded compounds. This method allows a direct comparison of library ranking efficacy. Our results indicate that the LigandFit/Ligscore1 and LigandFit/GOLD docking/scoring combinations, and to a lesser degree FlexX/FlexX, Glide/Ligscore1, DOCK/PMF (Tripos implementation), LigandFit1/Ligscore2 and LigandFit/PMF (Tripos implementation) were able to retrieve the highest number of actives at a 10% fraction of the database when all targets were looked upon collectively. We also show that the scoring functions rank the observed binding modes higher than the inaccurate poses provided that the experimental poses are available. This finding stresses the discriminatory ability of the scoring algorithms, when better poses are available, and suggests that the number of false positives can be lowered with conformers closer to bioactive ones.     

Novel approach to structure-based pharmacophore search using computational geometry and shape matching techniques

Computationally efficient structure-based virtual screening methods have recently been reported that seek to find effective means to utilize experimental structure information without employing detailed molecular docking calculations. These tools can be coupled with efficient experimental screening technologies to improve the probability of identifying hits and leads for drug discovery research. Commercial software ROCS (rapid overlay of chemical structures) from Open Eye Scientific is such an example, which is a shape-based virtual screening method using the 3D structure of a ligand, typically from a bound X-ray costructure, as the query. We report here the development of a new structure-based pharmacophore search method (called Shape4) for virtual screening. This method adopts a variant of the ROCS shape technology and expands its use to work with an empty crystal structure. It employs a rigorous computational geometry method and a deterministic geometric casting algorithm to derive the negative image (i.e., pseudoligand) of a target binding site. Once the negative image (or pseudoligand) is generated, an efficient shape comparison algorithm in the commercial OE SHAPE Toolkit is adopted to compare and match small organic molecules with the shape of the pseudoligand. We report the detailed computational protocol and its computational validation using known biologically active compounds extracted from the WOMBAT database. Models derived for five selected targets were used to perform the virtual screening experiments to obtain the enrichment data for various virtual screening methods. It was found that our approach afforded similar or better enrichment ratios than other related methods, often with better diversity among the top ranking computational hits.     

Joint QSAR Analysis Using the Free-Wilson Approach and Quantum Chemical Parameters

Abstract

A new quantitative structure-activity relationship (QSAR) technique combining the Free-Wilson method and constructed quantum chemical parameters was used to simulate the aqueous solubility (S _w), 1-octanol/water partition coefficient (K _ow) of 14 new synthesized benzanilide derivatives and their 96 h acute toxicity (EC₅₀) to Daphnia magna. The mode of action of the 14 selected compounds to Daphnia magna was shown to be a complex process involving a physical partition stage and a biochemical reaction stage. The results also indicated that the joint (QSAR) analysis was much effective than the original Free-Wilson method and Hansch method not only in predicting properties/toxicity, but also in investigating the mode of action of chemicals.     

Introduction of the conditional correlated Bernoulli model of similarity value distributions and its application to the prospective prediction of fingerprint search performance

A statistical approach named the conditional correlated Bernoulli model is introduced for modeling of similarity scores and predicting the potential of fingerprint search calculations to identify active compounds. Fingerprint features are rationalized as dependent Bernoulli variables and conditional distributions of Tanimoto similarity values of database compounds given a reference molecule are assessed. The conditional correlated Bernoulli model is utilized in the context of virtual screening to estimate the position of a compound obtaining a certain similarity value in a database ranking. Through the generation of receiver operating characteristic curves from cumulative distribution functions of conditional similarity values for known active and random database compounds, one can predict how successful a fingerprint search might be. The comparison of curves for different fingerprints makes it possible to identify fingerprints that are most likely to identify new active molecules in a database search given a set of known reference molecules.     

Total ranking models by the genetic algorithm variable subset selection (GA–VSS) approach for environmental priority settings

Total order ranking (TOR) strategies, which are mathematically based on elementary methods of discrete mathematics, seem to be attractive and simple tools for performing data analysis. Moreover order-ranking strategies seem to be a very useful tool not only to perform data exploration but also to develop order ranking models, a possible alternative to conventional quantitative structure–activity relationship (QSAR) methods. In fact, when data material is characterised by uncertainties, order methods can be used as alternative to statistical methods such as multilinear regression (MLR), because they do not require specific functional relationships between the independent and dependent variables (responses). A ranking model is a relationship between a set of dependent attributes, experimentally investigated, and a set of independent attributes, i.e. model attributes, which are calculated attributes. As in regression and classification models, the variable selection model is one of the main steps in finding predictive models. In this work the genetic algorithm–variable subset selection (GA–VSS) approach is proposed as the variable selection method for searching for the best ranking models within a wide set of variables. The models based on the selected subsets of variables are compared with the experimental ranking and evaluated by the Spearmans rank index. A case study application is presented on a TOR model developed for polychlorinated biphenyl (PCB) compounds, which have been analysed according to some of their physicochemical properties which play an important role in their environmental impact.     

A simple,fast, and accurate thermodynamic‐based approach for transfer and prediction of gas chromatography retention times between columns and instruments Part III: Retention time prediction on target column

This is the third part of a three‐part series of papers. In Part I, we presented a method for determining the actual effective geometry of a reference column as well as the thermodynamic‐based parameters of a set of probe compounds in an in‐house mixture. Part II introduced an approach for estimating the actual effective geometry of a target column by collecting retention data of the same mixture of probe compounds on the target column and using their thermodynamic parameters, acquired on the reference column, as a bridge between both systems. Part III, presented here, demonstrates the retention time transfer and prediction from the reference column to the target column using experimental data for a separate mixture of compounds. To predict the retention time of a new compound, we first estimate its thermodynamic‐based parameters on the reference column (using geometric parameters determined previously). The compound's retention time on a second column (of previously determined geometry) is then predicted. The models and the associated optimization algorithms were tested using simulated and experimental data. The accuracy of predicted retention times shows that the proposed approach is simple, fast, and accurate for retention time transfer and prediction between gas chromatography columns.     

Thermophysical properties of polypropylene/aluminum composites

This article is dedicated to the study of the thermal parameters of composite materials. A nonlinear least‐squares criterion is used on experimental transfer functions to identify the thermal conductivity and the diffusivity of aluminum‐polymer composite materials. The density measurements were achieved to deduce the specific heat and thereafter they were compared to values given by differential scanning calorimetry measurement. The thermal parameters of the composite material polypropylene/aluminum were investigated for the two different types of aluminum filler sizes. The experimental data were compared with several theoretical thermal conductivity prediction models. It was found that both the Agari and Bruggeman models provide a good estimation for thermal conductivity. The experimental values of both thermal conductivity and diffusivity have shown a better heat transport for the composite filled with large particles. © 2004 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 42: 722–732, 2004