Combinatorial Library Design Research Articles

ProSAR: A New Methodology for Combinatorial Library Design

A method is introduced for performing reagent selection for chemical library design based on topological (2D) pharmacophore fingerprints. Optimal reagent selection is achieved by optimizing the Shannon entropy of the 2D pharmacophore distribution for the reagent set. The method, termed ProSAR, is therefore expected to enumerate compounds that could serve as a good starting point for deriving a structure activity relationship (SAR) in combinatorial library design. This methodology is exemplified by library design examples where the active compounds were already known. The results show that most of the pharmacophores on the substituents for the active compounds are covered by the designed library. This strategy is further expanded to include product property profiles for aqueous solubility, hERG risk assessment, etc. in the optimization process so that the reagent pharmacophore diversity and the product property profile are optimized simultaneously via a genetic algorithm. This strategy is applied to a two-dimensional library design example and compared with libraries designed by a diversity based strategy which minimizes the average ensemble Tanimoto similarity. Our results show that by using the PSAR methodology, libraries can be designed with simultaneously good pharmacophore coverage and product property profile.

Superaugmented Pendentic Indices: Novel Topological Descriptors for QSAR/QSPR

Four pendenticity based topological descriptors termed as superaugmented pendentic indices have been conceptualized in the present study. An in-house computer program was utilized to compute index values of all the possible structures (with at least one pendent vertex) containing four, five and six vertices. The sensitivity towards branching, discriminating power, degeneracy and mathematical properties of the proposed superaugmented pendentic indices were investigated. All the four proposed indices exhibited exceptionally high sensitivity towards branching, high discriminating power and extremely low degeneracy. Superaugmented pendentic index-4 (SA∫P-4) exhibited exceptionally high discriminating power of 114 in structures containing only six vertices. Statistical significance of the proposed indices was investigated using intercorrelation analysis with Wiener’s index, Balaban’s mean square distance index, molecular connectivity index, Zagreb indices (M1 and M2), superpendentic index and eccentric connectivity index. The exceptionally high sensitivity towards branching, high discriminating power amalgamated with extremely low degeneracy offer proposed indices a vast potential for isomer discrimination, similarity/dissimilarity, drug design, quantitative structureactivity/ structure-property relationships, lead optimization and combinatorial library design.

ChemInform Abstract: Scaffold Diversity of Natural Products: Inspiration for Combinatorial Library Design

AbstractChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.

Discovery of Multitarget Inhibitors by Combining Molecular Docking with Common Pharmacophore Matching

Multitarget drugs have been to be found effective in controlling complex diseases. However, how to design multitarget drugs presents a great challenge. We have developed a computer-assisted strategy to screen for multitarget inhibitors using a combination of molecular docking and common pharmacophore matching. This strategy was successfully applied to screen for dual-target inhibitors against both the human leukotriene A(4) hydrolase (LTA4H-h) and the human nonpancreatic secretory phospholipase A2 (hnps-PLA2). Three compounds screened from the chemical database MDL Available Chemical Directory were found to inhibit these two enzymes at the 10 microM level. Moreover, one synthetic compound matching the common pharmacophores inhibits LTA4H-h and hnps-PLA2 with IC(50) values of 35 nM and 7.3 microM, respectively. The common pharmacophore model can also be used to search small molecule databases or collections of existing inhibitors, as well as to guide combinatorial library design to search for ideal multitarget inhibitors.

Global Bayesian Models for the Prioritization of Antitubercular Agents

To aid the creation of novel antituberculosis (antiTB) compounds, Bayesian models were derived and validated on a data set of 3779 compounds which have been measured for minimum inhibitory concentration (MIC) in the Mycobacterium tuberculosis H37Rv strain. The model development and validation involved exploring six different training sets and 15 fingerprint types which resulted in a total of 90 models, with active compounds defined as those with MIC < 5 microM. The best model was derived using Extended Class Fingerprints of maximum diameter 12 (ECFP_12) and a few global descriptors on a training set derived using Functional Class Fingerprints of maximum diameter 4 (FCFP_4). This model demonstrated very good discriminant ability in general, with excellent discriminant statistics for the training set (total accuracy: 0.968; positive recall: 0.967) and a good predictive ability for the test set (total accuracy: 0.869; positive recall: 0.789). The good predictive ability was maintained when the model was applied to a well-separated test set of 2880 compounds derived from a commercial database (total accuracy: 0.73; positive recall: 0.72). The model revealed several conserved substructures present in the active and inactive compounds which are believed to have incremental and detrimental effects on the MIC, respectively. Strategies for enhancing the repertoire of antiTB compounds with the model, including virtual screening of large databases and combinatorial library design, are proposed.

On the Suitability of Different Representations of Solid Catalysts for Combinatorial Library Design by Genetic Algorithms

Genetic algorithms are widely used to solve and optimize combinatorial problems and are more often applied for library design in combinatorial chemistry. Because of their flexibility, however, their implementation can be challenging. In this study, the influence of the representation of solid catalysts on the performance of genetic algorithms was systematically investigated on the basis of a new, constrained, multiobjective, combinatorial test problem with properties common to problems in combinatorial materials science. Constraints were satisfied by penalty functions, repair algorithms, or special representations. The tests were performed using three state-of-the-art evolutionary multiobjective algorithms by performing 100 optimization runs for each algorithm and test case. Experimental data obtained during the optimization of a noble metal-free solid catalyst system active in the selective catalytic reduction of nitric oxide with propene was used to build up a predictive model to validate the results of the theoretical test problem. A significant influence of the representation on the optimization performance was observed. Binary encodings were found to be the preferred encoding in most of the cases, and depending on the experimental test unit, repair algorithms or penalty functions performed best.

In SilicoPrediction of Blood–Brain Partitioning Using a Chemometric Method Called Genetic Algorithm Based Variable Selection

AbstractA chemometric method called Genetic Algorithm Based Variable Selection (GAVS) was developed for Quantitative Structure–Property Relationship (QSPR) study here. Different from other GA based techniques, improvements have been made in GAVS, including fitness function and the strategy of elite warehouse. In the case of modeling the Blood–Brain Barrier (BBB) penetration, GAVS performed very well in variable selection. The training set consists of 151 structurally diverse compounds 154 molecular descriptors of which were calculated by E‐Dragon. The best model generated by GAVS contained six descriptors withr=0.848,q=0.817,F=61.40. The model was further validated by two external test sets, with the predicted correlation coefficient of 0.837 for Test set I which contained 28 compounds and correct rate of 82.42% for Test set II which included 91 drugs or drug‐like compounds with known BBB indication. To the CNS library containing 1283 CNS activities, the model performed with a success rate of 97.3% in predicting CNS active compounds. In comparison with other methods, GAVS performed best among Genetic Function Approximate (GFA) method and other published models. The established predictive model could be applied in design of combinatorial libraries or virtual screening against Central Nervous System (CNS) targets.

ChemInform Abstract: A Scalable Approach to Combinatorial Library Design for Drug Discovery.

AbstractChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.

Structure-directed combinatorial library design

In recent years pharmaceutical companies have utilized structure-based drug design and combinatorial library design techniques to speed up their drug discovery efforts. Both approaches are routinely used in the lead discovery and lead optimization stages of the drug discovery process. Fragment-based drug design, a new power tool in the drug design toolbox, is also gaining acceptance across the pharmaceutical industry. This review will focus on the interplay between these three design techniques and recent developments in computational methodologies that enhance their integration. Examples of successful synergistic applications of these three techniques will be highlighted. Opinion regarding possible future directions of the field will be given.

Scaffold diversity of natural products: inspiration for combinatorial library design

Natural products contain scaffold structures that can be systematically exploited for the design of combinatorial compound libraries with druglike properties. We review approaches for scaffold identification, and compare properties and pharmacophoric features of drugs and natural products. In particular, an application of the self-organizing map technique is presented for natural product-derived compound and library design.

A Scalable Approach to Combinatorial Library Design for Drug Discovery

In this paper, we propose an algorithm for the design of lead generation libraries required in combinatorial drug discovery. This algorithm addresses simultaneously the two key criteria of diversity and representativeness of compounds in the resulting library and is computationally efficient when applied to a large class of lead generation design problems. At the same time, additional constraints on experimental resources are also incorporated in the framework presented in this paper. A computationally efficient scalable algorithm is developed, where the ability of the deterministic annealing algorithm to identify clusters is exploited to truncate computations over the entire data set to computations over individual clusters. An analysis of this algorithm quantifies the tradeoff between the error due to truncation and computational effort. Results applied on test data sets corroborate the analysis and show improvement by factors as large as 10 or more, depending on the data sets.

Multicomponent Au/MgO catalysts designed for selective oxidation of carbon monoxideApplication of a combinatorial approach

A complex combinatorial approach has been applied for the design of multicomponent Au/MgO catalysts for CO oxidation in the presence of hydrogen. The combinatorial library design led to unique earlier unknown catalyst compositions. The best catalysts contained significant amount of Pb and Sm, which had never been reported before as useful components of PROX catalysts. In order to increase the diversity of experimental space different pretreatment conditions have been applied prior to catalytic tests. Actually, two catalyst libraries have been tested after (i) a reductive, and (ii) a combined reductive pretreatment. Significantly different optimum compositions have been obtained upon using these two pretreatment procedures. The combined reductive treatment resulted in a more cost effective optimum composition with significantly less components and smaller gold content in comparison to the best catalyst obtained upon using a simple reductive treatment. Further studies showed different ways of promoting action of modifiers. After reductive pretreatment the modifiers suppress the hydrogen consumption, while after combined reductive pretreatment the modifiers lead to the promotion of both oxidation reactions.

A Pharmacophore Map of Small Molecule Protein Kinase Inhibitors

Protein kinases have emerged as one of the major drug target classes that are amenable to the development of small molecule inhibitors. They share a conserved structural similarity in the region of the ATP binding site, where most inhibitors interact. Using a pharmacophore approach, we have explored the features of protein-ligand interactions for a set of 220 kinase crystal structures from the Protein Data Bank (PDB). The resulting "pharmacophore map" shows the interactions made by all ligands with their receptors simultaneously. This gives insight that has been applied in the design of kinase screening sets and combinatorial libraries. An algorithm is described that scores small molecule structures for goodness of fit to the resulting binding site description obtained from the analysis. The algorithm identifies a pose that is close to the crystal structure pose for the majority of ligands, with only the 2D chemical structure as input and no knowledge of the crystal structure from which it was derived. Application of the algorithm to a test screening set gave a useful enrichment of active compounds.

Superaugmented eccentric connectivity indices: new-generation highly discriminating topological descriptors for QSAR/QSPR modeling

New-generation highly discriminating topological descriptors, termed superaugmented eccentric connectivity indices (SAξc), have been conceptualized in the present study. The values of SAξc for all the possible structures of three, four, and five vertices were computed using an in-house computer program. The sensitivity to branching, discriminating power, and degeneracy of SAξc were investigated for all the possible structures of three, four, and five vertices and subsequently compared with those of the eccentric and augmented eccentric connectivity indices. The proposed superaugmented eccentric connectivity indices exhibited high sensitivity towards branching, exceptionally high discriminating power, and extremely low degeneracy. The superaugmented eccentric connectivity index-3 (SAξc3) exhibited an exceptionally high discriminating power of > 4000 for all possible structures containing only five vertices. The statistical significance of the proposed indices was investigated using intercorrelation analysis with Wiener’s index; Balaban’s J index (the average distance sum connectivity); the molecular connectivity index; the eccentric connectivity index; and the augmented eccentric connectivity index values. The high sensitivity to branching and exceptionally high discriminating power amalgamated with extremely low degeneracy give these indices vast potential for isomer discrimination, similarity/dissimilarity, drug design, quantitative structure–activity/structure–property relationships, lead optimization, and combinatorial library design.

Predicting anti-HIV-1 activity of 6-arylbenzonitriles: Computational approach using superaugmented eccentric connectivity topochemical indices

Highly discriminating adjacency-cum-distance based topochemical indices termed as superaugmented eccentric connectivity topochemical indices for quantitative structure–activity and structure–property relationships (QSAR/QSPR) have been conceptualized in the present study. These indices were found to exhibit high sensitivity towards the presence and relative position of heteroatom(s), exceptionally high discriminating power and negligible degeneracy for all possible structures of five vertices containing one heteroatom. Utility of these indices was investigated for development of models for prediction of anti-human immunodeficiency virus (HIV)-1 activity using a data set comprising 81 differently substituted 6-arylbenzonitriles. The values of the superaugmented eccentric connectivity topochemical indices of all the analogues comprising the data set were computed using an in-house computer program. The resultant data was analyzed and suitable models were developed after identification of the active ranges. Subsequently, a biological activity was assigned to each analogue using these models which was then compared with the reported anti-HIV-1 activity. The accuracy of prediction was found to be ∼81% for all the three topochemical models. High sensitivity towards presence and relative position of heteroatom(s), exceptionally high discriminating power amalgamated with low degeneracy offer proposed topochemical indices vast potential for isomer discrimination, similarity/dissimilarity, drug design, quantitative structure–activity/structure–property relationships, lead optimization and combinatorial library design.

Virtual Screening in Drug Discovery - A Computational Perspective

Virtual screening emerged as an important tool in our quest to access novel drug like compounds. There are a wide range of comparable and contrasting methodological protocols available in screening databases for the lead compounds. The number of methods and software packages which employ the target and ligand based virtual screening are increasing at a rapid pace. However, the general understanding on the applicability and limitations of these methodologies is not emerging as fast as the developments of various methods. Therefore, it is extremely important to compare and contrast various protocols with practical examples to gauge the strength and applicability of various methods. The review provides a comprehensive appraisal on several of the available virtual screening methods to-date. Recent developments of the docking and similarity based methods have been discussed besides the descriptor selection and pharmacophore based searching. The review touches upon the application of statistical, graph theory based methods machine learning tools in virtual screening and combinatorial library design. Finally, several case studies are undertaken where the virtual screening technology has been applied successfully. A critical analysis of these case studies provides a good platform to estimate the applicability of various virtual screening methods in the new lead identification and optimization.

Design and spectroscopic reflectometry characterization of pulsed laser deposition combinatorial libraries

The goal of the design of pulsed laser deposition (PLD) combinatorial library films is to optimize the compositional coverage of the films while maintaining a uniform thickness. The deposition pattern of excimer laser PLD films can be modeled with a bimodal cos n distribution. Deposited films were characterized using a spectroscopic reflectometer (250–1000 nm) to map the thickness of both single composition calibration films and combinatorial library films. These distribution functions were used to simulate the composition and thickness of multiple target combinatorial library films. The simulations were correlated with electron-probe microanalysis wavelength-dispersive spectroscopy (EPMA-WDS) composition maps. The composition and thickness of the library films can be fine-tuned by adjusting the laser spot size, fluence, background gas pressure, target geometry and other processing parameters which affect the deposition pattern. Results from compositionally graded combinatorial library films of the ternary system Al 2O 3–HfO 2–Y 2O 3 are discussed.

Assessment of Predictive Ability of Artificial Neural Networks Using Holographic Mapping

In this study, artificial neural networks (ANNs) were used to reveal a quantitative relationship between catalytic composition and catalytic activity. This relationship was predefined using a hypothetical experimental space described by a multidimensional polynomial. The predictive ability of ANNs was investigated, i.e. an attempt was done to evaluate how ANNs can envisage a given hypothetical experimental space. Data sets for training, validation and testing of ANNs were obtained from the hypothetical experimental space using two different ways of sampling. Data were selected, (i) by means of our optimization algorithm called Holographic Research Strategy (HRS); and (ii) randomly. In order to model real experimentation, data were also generated with error. The relationship between the complexity of different network topologies and their predictive ability was investigated. It was shown that when data used for training have been perturbed with a given level of noise, less complex network architectures give acceptable accuracy. Additionally, estimated experimental spaces were visualized in a 2D layout by means of Holographic Mappings (HMs). Analysis of HMs revealed that ANNs trained by data sets obtained upon an optimization procedure provides better description of the experimental space in the vicinity of the optimum than ANNs trained by randomly selected data sets. This fact indicates again the importance of the optimization in combinatorial catalyst library design.

Characterization of Trimetallic Pt-Pd-Au/CeO2 Catalysts Combinatorial Designed for Methane Total Oxidation

In the present work, the role and the effect of platinum and gold on the catalytic performance of ceria supported tri-metallic Pt-Pd-Au catalysts have been studied. The optimum composition of these tri-metallic supported catalysts has been discovered using methods and tools of combinatorial catalyst library design. Detailed catalytic, spectroscopic and physico-chemical characterization of catalysts in the vicinity of the optimum in the given compositional space has been performed. The temperature-programmed oxidation of methane revealed that the addition of Pt and Au to Pd/CeO2 catalyst resulted in higher conversion values in the whole investigated temperature range compared to the monometallic Pd catalyst. The time-on-stream experiments provided further evidence for the high-stability of tri-metallic catalysts compared to the monometallic one. Kinetic studies revealed the stronger adsorption of methane on Pt-Pd/CeO2 catalysts than over Pd/CeO2. XPS analysis showed that Pt and Au stabilize Pd in a more reduced form even under condition of methane oxidation. FTIR spectroscopy of adsorbed CO and hydrogen TPD measurements provided indirect evidences for alloying of Pt and Au with Pd. CO chemisorption data indicated that tri-metallic catalysts have increased accessible metallic surface area. It is suggested that advantageous catalytic properties of tri-metallic Pt-Au-Pd/CeO2 catalysts compared to the monometallic one can be attributed to (i) suppression of the formation of ionic forms of Pd(II), (ii) reaching an optimum ratio between Pd0 and PdO species, and (iii) stabilization of Pd in high dispersion. The results also indicate that Pd0 - PdO ensemble sites are required for methane activation.

GLARE: A New Approach for Filtering Large Reagent Lists in Combinatorial Library Design Using Product Properties.

AbstractChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract, please click on HTML or PDF.