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In silico studies toward the discovery of new anti-HIV nucleoside compounds through the use of TOPS-MODE and 2D/3D connectivity indices. 2. Purine derivatives.

The TOPological Substructural MOlecular DEsign (TOPS-MODE) approach has been used to predict the anti-HIV activity in MT-4 assays (Estrada et al., 2002) of a diverse range of purine-based nucleosides. A database of 206 nucleosides has been selected from the literature and a theoretical virtual screening model has been developed. The model is able of discriminating between compounds that have anti-HIV activity and those that do not, with a good classification level of 85% in the training and 82.8% in the cross-validation series. On the basis of the information generated by the model, the correct classification of practically 80% of compounds from an external prediction set has been achieved using the theoretical model. Furthermore, the contribution of a range of molecular fragments to the pharmacological action has been calculated and this could provide a powerful tool in the design of nucleoside analogues that show activity against the HIV. Finally, a QSAR model has been developed that allows quantitative data to be obtained regarding the pharmacological potency shown by this type of compound.

Anti-HIV Agents↗
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Linear and nonlinear methods in modeling the aqueous solubility of organic compounds.

Solubility data for 930 diverse compounds have been analyzed using linear Partial Least Square (PLS) and nonlinear PLS methods, Continuum Regression (CR), and Neural Networks (NN). 1D and 2D descriptors from MOE package in combination with E-state or ISIS keys have been used. The best model was obtained using linear PLS for a combination between 22 MOE descriptors and 65 ISIS keys. It has a correlation coefficient (r2) of 0.935 and a root-mean-square error (RMSE) of 0.468 log molar solubility (log S(w)). The model validated on a test set of 177 compounds not included in the training set has r2 0.911 and RMSE 0.475 log S(w). The descriptors were ranked according to their importance, and at the top of the list have been found the 22 MOE descriptors. The CR model produced results as good as PLS, and because of the way in which cross-validation has been done it is expected to be a valuable tool in prediction besides PLS model. The statistics obtained using nonlinear methods did not surpass those got with linear ones. The good statistic obtained for linear PLS and CR recommends these models to be used in prediction when it is difficult or impossible to make experimental measurements, for virtual screening, combinatorial library design, and efficient leads optimization.

Journal Article↗
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Prediction of chemical carcinogenicity from molecular structure.

Carcinogens represent a serious threat to human health. In vivo determination of carcinogenicity is time-consuming and expensive, thus in silico models to predict chemical carcinogenicity are highly desirable for virtual screening of compound libraries of both pharmaceutically and other commercially interesting molecules. In the present study, a PLS-DA (partial least squares discriminant analysis) model was developed to predict carcinogenicities in each of four rodent models: male mouse (MM), female mouse (FM), male rat (MR), and female rat (FR). The data set that was used contained over 520 compounds from both the NTP and the FDA databases. All the models were built from the same molecular descriptor system, which is based on atom typing [Sun, H. J. Chem. Inf. Comput. Sci. 2004, 44, 748-757], enabling the comparison of atomic contributions to carcinogenicity with respect to species and gender. Using four components, the models were able to achieve excellent fitting and prediction, with r(2) = 0.987 and q(2) = 0.944 for MM, r(2) = 0.985 and q(2) = 0.950 for FM, r(2) = 0.989 and q(2) = 0.962 for MR, and r(2) = 0.990 and q(2) = 0.965 for FR. The models were further validated by response permutation testing and external validation, and the results indicated that the models were both statistically significant and predictive. Variable influence on projection (VIP) analysis identified the key atom types and fragments that contributed to carcinogenicities and response differences across species and gender.

Animals↗
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A hierarchical clustering approach for large compound libraries.

A modified version of the k-means clustering algorithm was developed that is able to analyze large compound libraries. A distance threshold determined by plotting the sum of radii of leaf clusters was used as a termination criterion for the clustering process. Hierarchical trees were constructed that can be used to obtain an overview of the data distribution and inherent cluster structure. The approach is also applicable to ligand-based virtual screening with the aim to generate preferred screening collections or focused compound libraries. Retrospective analysis of two activity classes was performed: inhibitors of caspase 1 [interleukin 1 (IL1) cleaving enzyme, ICE] and glucocorticoid receptor ligands. The MDL Drug Data Report (MDDR) and Collection of Bioactive Reference Analogues (COBRA) databases served as the compound pool, for which binary trees were produced. Molecules were encoded by all Molecular Operating Environment 2D descriptors and topological pharmacophore atom types. Individual clusters were assessed for their purity and enrichment of actives belonging to the two ligand classes. Significant enrichment was observed in individual branches of the cluster tree. After clustering a combined database of MDDR, COBRA, and the SPECS catalog, it was possible to retrieve MDDR ICE inhibitors with new scaffolds using COBRA ICE inhibitors as seeds. A Java implementation of the clustering method is available via the Internet (http://www.modlab.de).

Algorithms↗
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Classification of substrates and inhibitors of P-glycoprotein using unsupervised machine learning approach.

P-glycoprotein (P-gp), a drug efflux pump, affects the bioavailability of therapeutic drugs and plays a potentially important role in clinical drug-drug interactions. Classification of candidate drugs as substrates or inhibitors of the carrier protein is of crucial importance in drug development. Accurate classification is difficult to achieve due to two major factors: i. The extreme diversity of substrates and the presence of multiple binding sites complicate the understanding of the mechanisms behind and hinder the development of a true, conclusive quantitative structure-activity relationship (QSAR) for P-gp substrates. ii. Both inhibitors and substrates interact with the same binding site of P-gp, as a result, it is not surprising that both share many common structural features. In this work, an unsupervised machine learning approach based on the Kohonen self-organizing maps (SOM) was explored, which incorporated a predefined set of physicochemical descriptors encoding the key molecular properties capable of discerning a substrate from an inhibitor. The SOM model can discriminate between substrates and inhibitors with an average accuracy of 82.3%. The current results show that the SOM-based method provides a potential in silico model for virtual screening.

ATP Binding Cassette Transporter, Subfamily B, Mem↗
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Molecular modeling studies focused on 5-HT7 versus 5-HT1A selectivity. Discovery of novel phenylpyrrole derivatives with high affinity for 5-HT7 receptors.

The present study discusses the well-known 5-HT7/5-HT1A selectivity issue through a new series of phenylpyrrole derivatives. The first hits emerged from a virtual screening performed on a chemolibrary. Further study led to an optimization of a preliminary 5-HT7 pharmacophore model. The importance of each pharmacophoric feature is confirmed, but these characteristics have to be coupled to geometric constraints in order to achieve a 5-HT7 selectivity. Indeed, 5-HT1A affinity probably arises from extended conformations, whereas a bent one appears to be best suited for 5-HT7 selectivity.

Databases as Topic↗
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Ligand-based molecular modeling study on a chemically diverse series of cholecystokinin-B/gastrin receptor antagonists: generation of predictive model.

Pharmacophore hypotheses were developed for six structurally diverse series of cholecystokinin-B/gastrin receptor (CCK-BR) antagonists. A training set consisting of 33 compounds was carefully selected. The activity spread of the training set molecules was from 0.1 to 2100 nM. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, two hydrogen bond donors, one hydrophobic aliphatic, and one hydrophobic aromatic feature, had a correlation (r) of 0.884 and a root-mean-square deviation of 1.1526, and the cost difference between null cost and fixed cost was 81.5 bits. The model was validated on a test set consisting of six different series of 27 structurally diverse compounds and performed well in classifying active and inactive molecules correctly. This validation approach provides confidence in the utility of the predictive pharmacophore model developed in this work as a 3D query tool in the virtual screening of drug-like molecules to retrieve new chemical entities as potent CCK-BR antagonists. The model can also be used to predict the biological activities of compounds prior to their costly and time-consuming synthesis.

Algorithms↗
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Scaffold hopping using clique detection applied to reduced graphs.

Similarity-based methods for virtual screening are widely used. However, conventional searching using 2D chemical fingerprints or 2D graphs may retrieve only compounds which are structurally very similar to the original target molecule. Of particular current interest then is scaffold hopping, that is, the ability to identify molecules that belong to different chemical series but which could form the same interactions with a receptor. Reduced graphs provide summary representations of chemical structures and, therefore, offer the potential to retrieve compounds that are similar in terms of their gross features rather than at the atom-bond level. Using only a fingerprint representation of such graphs, we have previously shown that actives retrieved were more diverse than those found using Daylight fingerprints. Maximum common substructures give an intuitively reasonable view of the similarity between two molecules. However, their calculation using graph-matching techniques is too time-consuming for use in practical similarity searching in larger data sets. In this work, we exploit the low cardinality of the reduced graph in graph-based similarity searching. We reinterpret the reduced graph as a fully connected graph using the bond-distance information of the original graph. We describe searches, using both the maximum common induced subgraph and maximum common edge subgraph formulations, on the fully connected reduced graphs and compare the results with those obtained using both conventional chemical and reduced graph fingerprints. We show that graph matching using fully connected reduced graphs is an effective retrieval method and that the actives retrieved are likely to be topologically different from those retrieved using conventional 2D methods.

Algorithms↗
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A database-centric virtual chemistry system.

We describe an Oracle database application for general use within virtual chemistry. The application functions as a central hub and repository for chemical data with interfaces to external calculators. It deals with the general problems of merging data from disparate sources and with scheduling of computational tasks for parallel or sequential execution in a mixed environment. The central database is used for the storage of input, intermediary, and final data as well as for job control. A calculation job is split into distinct tasks, or units of work, which are put in a queue. Tasks are dequeued and handled by specialized calculators. These calculators are in-house or commercial programs for which adaptor modules for connection to the database must be written. Tasks are handled in a transactional fashion, so that uncompleted or failed tasks are left in the queue. This makes the system stable to many types of disturbances. Sorting, filtering, and merging operations are handled by the database itself. Usage is very general, but some specific examples are (1) as a back end for a chemical property calculator Web page, (2) in an automated quantitative structure-activity relationship system, and (3) in virtual screens.

Journal Article↗
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Scalable partitioning and exploration of chemical spaces using geometric hashing.

Virtual screening (VS) has become a preferred tool to augment high-throughput screening(1) and determine new leads in the drug discovery process. The core of a VS informatics pipeline includes several data mining algorithms that work on huge databases of chemical compounds containing millions of molecular structures and their associated data. Thus, scaling traditional applications such as classification, partitioning, and outlier detection for huge chemical data sets without a significant loss in accuracy is very important. In this paper, we introduce a data mining framework built on top of a recently developed fast approximate nearest-neighbor-finding algorithm(2) called locality-sensitive hashing (LSH) that can be used to mine huge chemical spaces in a scalable fashion using very modest computational resources. The core LSH algorithm hashes chemical descriptors so that points close to each other in the descriptor space are also close to each other in the hashed space. Using this data structure, one can perform approximate nearest-neighbor searches very quickly, in sublinear time. We validate the accuracy and performance of our framework on three real data sets of sizes ranging from 4337 to 249 071 molecules. Results indicate that the identification of nearest neighbors using the LSH algorithm is at least 2 orders of magnitude faster than the traditional k-nearest-neighbor method and is over 94% accurate for most query parameters. Furthermore, when viewed as a data-partitioning procedure, the LSH algorithm lends itself to easy parallelization of nearest-neighbor classification or regression. We also apply our framework to detect outlying (diverse) compounds in a given chemical space; this algorithm is extremely rapid in determining whether a compound is located in a sparse region of chemical space or not, and it is quite accurate when compared to results obtained using principal-component-analysis-based heuristics.

Journal Article↗
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Quantitative structure-activity relationship studies on inhibition of HERG potassium channels.

The human ether-a-go-go-related gene (HERG) protein forms the ion channel responsible for the rapidly acting delayed rectifier potassium current, I(Kr), and its blockade is a significant contributor to prolongation of the QT interval. Using descriptors which have clear physicochemical meanings and are familiar to medicinal chemists, we have carried out 2D-quantitative structure-activity relationship (2D-QSAR) studies on 104 HERG channel blockers with diverse structures collected from the literature, and we have formulated interpretable models to guide chemical-modification studies and virtual screening. Statistically significant descriptors were selected by a genetic algorithm, and the final model included the octanol/water partition coefficient, topological polar surface area, diameter, summed surface area of atoms with partial charges from -0.25 to -0.20, and an indicator variable representing the experimental conditions. The statistics were r = 0.839, r2 = 0.704, q2 = 0.671, s = 0.763, and F = 46.6. The correspondence of the molecular determinants derived from the 2D-QSAR models with the 3D structural characteristics of the putative binding site in a homology-modeled HERG channel is also discussed.

Ether-A-Go-Go Potassium Channels↗
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Training similarity measures for specific activities: application to reduced graphs.

Reduced graph representations of chemical structures have been shown to be effective in similarity searching applications where they offer comparable performance to other 2D descriptors in terms of recall experiments. They have also been shown to complement existing descriptors and to offer potential to scaffold hop from one chemical series to another. Various methods have been developed for quantifying the similarity between reduced graphs including fingerprint approaches, graph matching, and an edit distance method. The edit distance approach quantifies the degree of similarity of two reduced graphs based on the number and type of operations required to convert one graph to the other. An attractive feature of the edit distance method is the ability to assign different weights to different operations. For example, the mutation of an aromatic ring node to an acyclic node may be assigned a higher weight than the mutation of an aromatic ring to an aliphatic ring node. In this paper, we describe a genetic algorithm (GA) for training the weights of the different edit distance operations. The method is applied to specific activity classes extracted from the MDDR database to derive activity-class specific weights. The GA-derived weights give substantially improved results in recall experiments as compared to using weights assigned on intuition. Furthermore, such activity specific weights may provide useful structure--activity information for subsequent design efforts. In a virtual screening setting when few active compounds are known, it may be more useful to have weights that perform well across a variety of different activity classes. Thus, the GA is also trained on multiple activity classes simultaneously to derive a generalized set of weights. These more generally applicable weights also represent a substantial improvement on previous work.

Algorithms↗
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Multifingerprint based similarity searches for targeted class compound selection.

Molecular fingerprints are widely used for similarity-based virtual screening in drug discovery projects. In this paper we discuss the performance and the complementarity of nine two-dimensional fingerprints (Daylight, Unity, AlFi, Hologram, CATS, TRUST, Molprint 2D, ChemGPS, and ALOGP) in retrieving active molecules by similarity searching against a set of query compounds. For this purpose, we used biological data from HTS screening campaigns of four protein families (GPCRs, kinases, ion channels, and proteases). We have established threshold values for the similarity index (Tanimoto index) to be used as starting points for similarity searches. Based on the complementarities between the selections made by using different fingerprints we propose a multifingerprint approach as an efficient tool to balance the strengths and weaknesses of various fingerprints.

Journal Article↗
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A distance function for retrieval of active molecules from complex chemical space representations.

The concept of chemical space is of fundamental importance for chemoinformatics research. It is generally thought that high-dimensional space representations are too complex for the successful application of many compound classification or virtual screening methods. Here, we show that a simple "activity-centered" distance function is capable of accurately detecting molecular similarity relationships in "raw" chemical spaces of high dimensionality.

Computational Biology↗
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Comparative protein modeling of 1-deoxy-D-xylulose-5-phosphate reductoisomerase enzyme from Plasmodium falciparum: a potential target for antimalarial drug discovery.

Plasmodium falciparum 1-deoxy-D-xylulose-5-phosphate reductoisomerase (Pf-DXR) is a potential target for antimalarial chemotherapy. The three-dimensional model (3D) of this enzyme was determined by means of comparative modeling through multiple alignment followed by intensive optimization, minimization, and validation. The resulting model demonstrates a reasonable topology as gauged from the Ramachandran plot and acceptable three-dimensional structure compatibility as assessed by the Profiles-3D score. The modeled monomeric subunit consists of three domains: (1) N-terminal NADPH binding domain, (2) connective or linker domain (with most of the active site residues located in this domain), and (3) a C-terminal domain. This structure proved to be consistent with known DXR crystal structures from other species. The predicted active site compared favorably with those of the templates and appears to have an active site with a highly conserved architecture. Additionally, the model explains several site-directed mutagenesis data. Besides using several protein structure-checking programs to validate the model, a set of known inhibitors of DXR were also docked into the active site of the modeled Pf-DXR. The docked scores correlated reasonably well with experimental pIC50 values with a regression coefficient (R2) equal to 0.84. Results of the current study should prove useful in the early design and development of inhibitors by either de novo drug design or virtual screening of large small-molecule databases leading to development of new antimalarial agents.

Aldose-Ketose Isomerases↗
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R-NN curves: an intuitive approach to outlier detection using a distance based method.

Libraries of chemical structures are used in a variety of cheminformatics tasks such as virtual screening and QSAR modeling and are generally characterized using molecular descriptors. When working with libraries it is useful to understand the distribution of compounds in the space defined by a set of descriptors. We present a simple approach to the analysis of the spatial distribution of the compounds in a library in general and outlier detection in particular based on counts of neighbors within a series of increasing radii. The resultant curves, termed R-NN curves, appear to follow a logistic model for any given descriptor space, which we justify theoretically for the 2D case. The method can be applied to data sets of arbitrary dimensions. The R-NN curves provide a visual method to easily detect compounds lying in a sparse region of a given descriptor space. We also present a method to numerically characterize the R-NN curves thus allowing identification of outliers in a single plot.

Journal Article↗
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Flexible docking of ligands into synthetic receptors using a two-sided incremental construction algorithm.

We present a new algorithm for the fast and reliable structure prediction of synthetic receptor-ligand complexes. Our method is based on the protein-ligand docking program FlexX and extends our recently introduced docking technique for synthetic receptors, which has been implemented in the program FlexR. To handle the flexibility of the relevant molecules, we apply a novel docking strategy that uses an adaptive two-sided incremental construction algorithm which incorporates the structural flexibility of both the ligand and synthetic receptor. We follow an adaptive strategy, in which one molecule is expanded by attaching its next fragment in all possible torsion angles, whereas the other (partially assembled) molecule serves as a rigid binding partner. Then the roles of the molecules are exchanged. Geometric filters are used to discard partial conformations that cannot realize a targeted interaction pattern derived in a graph-based precomputation phase. The process is repeated until the entire complex is built up. Our algorithm produces promising results on a test data set comprising 10 complexes of synthetic receptors and ligands. The method generated near-native solutions compared to crystal structures in all but one case. It is able to generate solutions within a couple of minutes and has the potential of being used as a virtual screening tool for searching for suitable guest molecules for a given synthetic receptor in large databases of guests and vice versa.

Algorithms↗
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Characterization of DNA primary sequences by a new similarity/diversity measure based on the partial ordering.

The similarity/diversity measures play a fundamental role in library searching, virtual screening, and quantitative structure-activity relationship/quantitative structure-property relationship modeling as well as in genomics and proteomics. In this paper, a new similarity/diversity measure is proposed as a new approach for the analysis of sequential data, where useful information can be also obtained by the ordering relationships between the sequence elements. This methodology can be applied for evaluating molecular similarity/diversity, using sets of sequential descriptors, and for evaluating the similarity between spectra, sensor arrays, and other sequential data such as DNA and protein sequences. The new proposed distance (weighted standardized Hasse distance) is evaluated between pairs of Hasse matrices derived from the classical partial-ordering rules. It can be naturally standardized, thus allowing the interpretation of these distances as absolute values (e.g., percentage) and deriving simple similarity and correlation indices. A simple example is taken to highlight the behavior of the new similarity/diversity measure on DNA sequences taken from the first exons of the beta-globins for eight different species. Sensitivity analysis has been also performed, showing the high capability of this measure to take into account small modifications of the DNA sequences. Finally, a comparison with results obtained from the literature is given, together with a comparison with matrix invariants derived from the Hasse matrix.

Base Sequence↗