Chemical Similarity Using Geometric Atom Pair Descriptors

Quantitative Structure-Activity Relationship (QSAR) models have been used in Pharmaceutical design and Medicinal Chemistry for the discovery of anti-parasite drugs. QSAR models predict biological activity using as input different types of structural parameters of molecules. Topological Indices (TIs) are a very interesting class of these parameters. We can derive TIs from graph representations based on only nodes (atoms) and edges (chemical bonds). TIs are not time-consuming in terms of computational resources because they depend only on atom-atom connectivity information. This information expressed in the molecular graphs can be tabulated in the form of adjacency matrices easy to manipulate with computers. Consequently, TIs allow the rapid collection, annotation, retrieval, comparison and mining of molecular structures within large databases. The interest in TIs has exploded because we can use them to describe also macromolecular and macroscopic systems represented by complex networks of interactions (links) between the different parts of a system (nodes) such as: drug-target, protein-protein, metabolic, host-parasite, brain cortex, parasite disease spreading, Internet, or social networks. In this work, we review and comment on the following topics related to the use of TIs in anti-parasite drugs and target discovery. The first topic reviewed was: Topological Indices and QSAR for antiparasitic drugs. This topic included: Theoretical Background, QSAR for anti-malaria drugs, QSAR for anti-Toxoplasma drugs. The second topic was: TOMO-COMD approach to QSAR of antiparasitic drugs. We included in this topic: TOMO-COMD theoretical background and TOMO-COMD models for antihelmintic activity, Trichomonas, anti-malarials, anti-trypanosome compounds. The third section was inserted to discuss Topological Indices in the context of Complex Networks. The last section is devoted to the MARCH-INSIDE approach to QSAR of antiparasitic drugs and targets. This begins with a theoretical background for drugs and parameters for proteins. Next, we reviewed MARCH-INSIDE models for Pharmaceutical Design of antiparasitic drugs including: flukicidal drugs and anti-coccidial drugs. We close MARCH-NSIDE topic with a review of multi-target QSAR of antiparasitic drugs, MARCH-INSIDE assembly of complex networks of antiparasitic drugs. We closed the MARCH-INSIDE section discussing the prediction of proteins in parasites and MARCH-INSIDE web-servers for Protein-Protein interactions in parasites: Plasmod-PPI and Trypano-PPI web-servers. We closed this revision with an important section devoted to review some legal issues related to QSAR models.

Dengue is a viral disease transmitted to humans through mosquito bites. Researchers have investigated various drugs with potential antiviral properties against it. Some of the promising antiviral drugs include UV-4B (N-9-methoxynonyl-1-deoxynojirimycin), Lycorine, ST-148, 4-HPR, Silymarin, Baicalein, Quercetin, Naringenin, Nelfinavir, Ivermectin, Mosnodenvir (JNJ-1802), NITD-688, Metoclopramide, JNJ-A07 and Betulinic acid. The chemical structure of a drug can be modelled as an isomorphic molecular graph , considering the atoms as the vertex set and the bonds between the pair of atoms as the edge set . Graph coloring and topological indices serve as a powerful tools for analyzing the isomorphic molecular graph, providing the structural characterization and computational studies. In this article, two types of coloring-based topological indices viz., chromatic topological indices and induced color-based topological indices, are introduced. Linear regression is employed in the QSPR(Quantitative Structure Property Relationship) analysis to examine the dengue antivirals through the computed topological indices of the aforementioned drugs. The results of the QSPR analysis reveal that the induced color-based indices provide better predictions of the physicochemical properties of dengue-treating drugs.

BackgroundSimilaritysearch in chemical structure databases is an important problem with many applications in chemical genomics, drug design, and efficient chemical probe screening among others. It is widely believed that structure based methods provide an efficient way to do the query. Recently various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models, graph kernel functions can not be applied to large chemical compound database due to the high computational complexity and the difficulties in indexing similarity search for large databases. ResultsTo bridge graph kernel function and similarity search in chemical databases, we applied a novel kernel-based similarity measurement, developed in our team, to measure similarity of graph represented chemicals. In our method, we utilize a hash table to support new graph kernel function definition, efficient storage and fast search. We have applied our method, named G-hash, to large chemical databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Moreover, the similarity measurement and the index structure is scalable to large chemical databases with smaller indexing size, and faster query processing time as compared to state-of-the-art indexing methods such as Daylight fingerprints, C-tree and GraphGrep.ConclusionsEfficient similarity query processing method for large chemical databases is challenging since we need to balance running time efficiency and similarity search accuracy. Our previous similarity search method, G-hash, provides a new way to perform similarity search in chemical databases. Experimental study validates the utility of G-hash in chemical databases.

Large amount of hydrological data set is a kind of big data, which has much hidden and potentially useful knowledge. Hydrological prediction is important for the state flood control and drought relief. How to forecast accurately and timely with hydrological big data becomes a big challenge. There are some forecasting techniques used widely. However, they are limited by their adaptability, the data volume and the data feature. The most important problems are the high time consumption, low accuracy and bad adaptability of prediction. In this paper, a new forecasting approach based on an integration of two tasks of data mining is put forward. This approach which is called S LMDBP combines similarity search and Levenberg-Marquardt(LM) algorithm improved Double-hidden layer Back Propagation(BP) neural network. A specialized data pretreatment including three parts is applied to process the hydrological data. The results of similarity search are then input into the improved BP neural network, which not only reduces dimensionality of training data without losing important patterns, but also improves the accuracy of the prediction. A set of experiments are conducted to validate the proposed approach. The data in the experiment is the daily water level data of three stations in Jiangxi province of China from 1950 to 2010. The experimental results demonstrate the real-timing, accuracy and robustness of our approach.

Phylostratigraphy is a widely used computational approach for predicting gene age by analyzing protein sequence similarities across evolutionary lineages. In this study, we used the Homo sapiens proteome as a reference to analyze the effect of false homology in gene age prediction. We implemented a workflow using state-of-the-art algorithms for sequence similarity searches enhanced by dynamic setting of an E-value threshold. This threshold was calculated for every protein individually using results of similarity searches with reversed database, which identified proteins prone to false homology. We demonstrated that dynamically adjusted E-values effectively reduced false positive rates compared to fixed threshold. However, the results indicate that, while certain proteins are more prone to false homology, they constitute a small proportion of the dataset and, in general, current sequence search algorithms are robust against false homology and thus reliable for phylostratigraphy.

Mathematical and Chemical Analysis of Wiener's Polarity Number

We report the development and application of the Topological Fragment Index (ToFI), a measure for the complexity of the topological environment of defined molecular fragments in active compounds. On the basis of ToFI calculations, RECAP fragments are organized in dependency hierarchies that capture fragment co-occurrence and facilitate the identification of topology clusters and activity class characteristic RECAP fragments. By combining structural and topological environment information through ToFI, RECAP fragments that are a signature of compounds active against one of several closely related targets are consistently identified.

Sequence data is being produced by genomic sequencing laboratories at ever-increasing rates, making it impossible for individual researchers to keep track of all the new data that might afkt their research. Computer systems are needed so that researchers can access this data. The systems must support high-level interfaces that communicate in the language of the researchers, database systems that guarantee availability and consistency of the data, and powerful search systems that rapidly scan for similarities between sequences. We have developed a prototype system that includes a graphical user interface, an object-oriented database management system, and high-performance similarity search algorithms. The prototype has the potential to increase researchers’ productivity by automating ermy of amotated sequence fragments as they are produced by sequencing machines, storing the fragmenta in the database, and automatically prcducing and displaying similarity search results of new sequences against the large public sequence datsbsses GenBank and PIR. This paper describes the prototype, discusses the kme!its of object-oriented databases for complex and changing sequence da@ and presents an object-oriented schema for genetic information. Graphical tools for annotating sequences, storing them in the database, automating similarity searches, and viewing similarity search results are presented. A new suffix tieebased data stnscture that supports rapid similarity searches on sequence data is introduced. Finally, future plans for the system are discussed.

Interaction signatures of drug candidates are characteristic to off-target (neutral) and antitarget (negative) effects, inferring reduced efficiency, side-effects and high attrition rate. Today’s retroactive scaled-down virtual screening (VS) experiments relying on benchmarking datasets are extensively involved to assess ligand enrichment in the real-world problem. In recent years, unbiased benchmarking sets turned into a tremendous need to assist virtual screening methodologies for emerging drug targets. To date, the benchmarking datasets are quite limited, whereas glycogen synthase kinase-3 (GSK-3) is not included into directories of benchmarking datasets such as DUD-e, MUV, etc. Herein we introduced our in-house algorithm to build an unbiased benchmarking dataset, including highly selective, moderately selective and nonselective inhibitors for a significant therapeutic target - GSK-3, suitable for both ligand-based and structure-based VS approaches. These datasets are unbiased in terms of physico-chemical properties and topological descriptors, as resulted from mean(ROC–AUC) leave-one-out cross-validation (LOO CV). and additional 2 D similarity search. Moreover, we investigated the gradual selectivity dataset by application of multiple 2 D similarity coefficients and distances, 3 D similarity and docking. Besides the resulted links between the enrichment of selective GSK-3 inhibitors and their chemical structures, a database of compounds and their 3 D similarity signatures including cut-off thresholds for enhanced selectivity was generated. 2 D similarity space analysis revealed that selectivity problem cannot be evaluated appropriately with 2 D similarity searching alone. The current analysis provided useful, comprehensive insights, which may facilitate the knowledge-based identification of novel selective GSK-3 inhibitors. Communicated by Ramaswamy H. Sarma

Connectivity polynomial and long-range contributions in the molecular connectivity model

BackgroundThe Pubchem Database is a large-scale resource for chemical information, containing millions of chemical compound activities derived by high-throughput screening (HTS). The ability to extract characteristic substructures from such enormous amounts of data is steadily growing in importance. Compounds with shared basic active structures (BASs) exhibiting G-protein coupled receptor (GPCR) activity and repeated dose toxicity have been mined from small datasets. However, the mining process employed was not applicable to large datasets owing to a large imbalance between the numbers of active and inactive compounds. In most datasets, one active compound will appear for every 1000 inactive compounds. Most mining techniques work well only when these numbers are similar.ResultsThis difficulty was overcome by sampling an equal number of active and inactive compounds. The sampling process was repeated to maintain the structural diversity of the inactive compounds. An interactive KNIME workflow that enabled effective sampling and data cleaning processes was created. The application of the cascade model and subsequent structural refinement yielded the BAS candidates. Repeated sampling increased the ratio of active compounds containing these substructures. Three samplings were deemed adequate to identify all of the meaningful BASs. BASs expressing similar structures were grouped to give the final set of BASs. This method was applied to HIV integrase and protease inhibitor activities in the MDL Drug Data Report (MDDR) database and to procaspase-3 activators in the PubChem BioAssay database, yielding 14, 12, and 18 BASs, respectively.ConclusionsThe proposed mining scheme successfully extracted meaningful substructures from large datasets of chemical structures. The resulting BASs were deemed reasonable by an experienced medicinal chemist. The mining itself requires about 3 days to extract BASs with a given physiological activity. Thus, the method described herein is an effective way to analyze large HTS databases.

In this paper we demonstrated that by an adequate combination of different topological indices it is possible to select and design new active compounds in different therapeutical scopes, with a very high efficiency level. Particularly successful in the search of new "lead drugs", the results show the surprising ability of the topological methods to describe molecular structures.

In modern drug discovery, 2-D similarity searching is widely employed as a cost-effective way to screen large compound collections and select subsets of molecules that may have interesting biological activity prior to experimental screening. Nowadays, there is a growing interest in applying the existing 2-D similarity searching methods to combinatorial chemistry libraries to search for novel hits or to evolve lead series. A dilemma thus arises when many identical substructures recur in library products and they have to be considered repeatedly in descriptor calculations. The dilemma is exacerbated by the astronomical number of combinatorial products. This problem imposes a major barrier to similarity searching of large combinatorial chemistry spaces. An efficient approach, termed Monomer-based Similarity Searching (MoBSS), is proposed to remedy the problem. MoBSS calculates atom pair (AP) descriptors based on interatomic topological distances, which lend themselves to pair additivity. A fast algorithm is employed in MoBSS to rapidly compute product atom pairs from those of the constituent fragments. The details of the algorithm are presented along with a series of proof-of-concept studies, which demonstrate the speed, accuracy, and utility of the MoBSS approach.

A method is proposed, and shown to be able to find active compounds in a Large Database by iterative selection/screening cycles. The method is derived from genetic algorithms. Starting from an initial small parent population the next compounds to screen are selected by similarity search based on the structural features of two high rating parents. The performance of the method is demonstrated using 20000 compounds with biological data from the NCI1 database.

Many studies have been conducted on seeking an efficient solution for graph similarity search over certain (deterministic) graphs due to its wide application in many fields, including bioinformatics, social network analysis, and Resource Description Framework data management. All prior work assumes that the underlying data is deterministic. However, in reality, graphs are often noisy and uncertain due to various factors, such as errors in data extraction, inconsistencies in data integration, and for privacy-preserving purposes. Therefore, in this paper, we study similarity graph containment search on large uncertain graph databases. Similarity graph containment search consists of subgraph similarity search and supergraph similarity search. Different from previous works assuming that edges in an uncertain graph are independent of each other, we study uncertain graphs where edges' occurrences are correlated. We formally prove that subgraph or supergraph similarity search over uncertain graphs is $$\#$&#P-hard; thus, we employ a filter-and-verify framework to speed up these two queries. For the subgraph similarity query, in the filtering phase, we develop tight lower and upper bounds of subgraph similarity probability based on a probabilistic matrix index (PMI). PMI is composed of discriminative subgraph features associated with tight lower and upper bounds of subgraph isomorphism probability. Based on PMI, we can filter out a large number of uncertain graphs and maximize the pruning capability. During the verification phase, we develop an efficient sampling algorithm to validate the remaining candidates. For the supergraph similarity query, in the filtering phase, we propose two pruning algorithms, one lightweight and the other strong, based on maximal common subgraphs of query graph and data graph. We run the two pruning algorithms against a probabilistic index that consists of powerful graph features. In the verification, we design an approximate algorithm based on the Horvitz---Thompson estimator to fast validate the remaining candidates. The efficiencies of our proposed solutions to the subgraph and supergraph similarity search have been verified through extensive experiments on real uncertain graph datasets.