Efficient identification of Tanimoto nearest neighbors; All Pairs Similarity Search Using the Extended Jaccard Coefficient

Tanimoto, or extended Jaccard, is an important similarity measure which has seen prominent use in fields such as data mining and chemoinformatics. Many of the existing state-of-the-art methods for market basket analysis, plagiarism and anomaly detection, compound database search, and ligand-based virtual screening rely heavily on identifying Tanimoto nearest neighbors. Given the rapidly increasing size of data that must be analyzed, new algorithms are needed that can speed up nearest neighbor search, while at the same time providing reliable results. While many search algorithms address the complexity of the task by retrieving only some of the nearest neighbors, we propose a method that finds all of the exact nearest neighbors efficiently by leveraging recent advances in similarity search filtering. We provide tighter filtering bounds for the Tanimoto coefficient and show that our method, TAPNN, greatly outperforms existing baselines across a variety of real-world datasets and similarity thresholds

Comparison of chemical clustering methods using graph- and fingerprint-based similarity measures

This paper compares several published methods for clustering chemical structures, using both graph- and fingerprint-based similarity measures. The clusterings from each method were compared to determine the degree of cluster overlap. Each method was also evaluated on how well it grouped structures into clusters possessing a non-trivial substructural commonality. The methods which employ adjustable parameters were tested to determine the stability of each parameter for datasets of varying size and composition. Our experiments suggest that both graph- and fingerprint-based similarity measures can be used effectively for generating chemical clusterings; it is also suggested that the CAST and Yin–Chen methods, suggested recently for the clustering of gene expression patterns, may also prove effective for the clustering of 2D chemical structures

Entropy-scaling search of massive biological data

Many datasets exhibit a well-defined structure that can be exploited to design faster search tools, but it is not always clear when such acceleration is possible. Here, we introduce a framework for similarity search based on characterizing a dataset's entropy and fractal dimension. We prove that searching scales in time with metric entropy (number of covering hyperspheres), if the fractal dimension of the dataset is low, and scales in space with the sum of metric entropy and information-theoretic entropy (randomness of the data). Using these ideas, we present accelerated versions of standard tools, with no loss in specificity and little loss in sensitivity, for use in three domains---high-throughput drug screening (Ammolite, 150x speedup), metagenomics (MICA, 3.5x speedup of DIAMOND [3,700x BLASTX]), and protein structure search (esFragBag, 10x speedup of FragBag). Our framework can be used to achieve "compressive omics," and the general theory can be readily applied to data science problems outside of biology.Comment: Including supplement: 41 pages, 6 figures, 4 tables, 1 bo

Shaping a screening file for maximal lead discovery efficiency and effectiveness: elimination of molecular redundancy

High Throughput Screening (HTS) is a successful strategy for finding hits and leads that have the opportunity to be converted into drugs. In this paper we highlight novel computational methods used to select compounds to build a new screening file at Pfizer and the analytical methods we used to assess their quality. We also introduce the novel concept of molecular redundancy to help decide on the density of compounds required in any region of chemical space in order to be confident of running successful HTS campaigns

Identification of Novel Antimalarial Chemotypes via Chemoinformatic Compound Selection Methods for a High-Throughput Screening Program against the Novel Malarial Target, PfNDH2: Increasing Hit Rate via Virtual Screening Methods

Malaria is responsible for approximately 1 million deaths annually; thus, continued efforts to discover new antimalarials are required. A HTS screen was established to identify novel inhibitors of the parasite's mitochondrial enzyme NADH:quinone oxidoreductase (PfNDH2). On the basis of only one known inhibitor of this enzyme, the challenge was to discover novel inhibitors of PfNDH2 with diverse chemical scaffolds. To this end, using a range of ligand-based chemoinformatics methods, ~17000 compounds were selected from a commercial library of ~750000 compounds. Forty-eight compounds were identified with PfNDH2 enzyme inhibition IC(50) values ranging from 100 nM to 40 μM and also displayed exciting whole cell antimalarial activity. These novel inhibitors were identified through sampling 16% of the available chemical space, while only screening 2% of the library. This study confirms the added value of using multiple ligand-based chemoinformatic approaches and has successfully identified novel distinct chemotypes primed for development as new agents against malaria

Protein surface representation and analysis by dimension reduction

<p>Abstract</p> <p>Background</p> <p>Protein structures are better conserved than protein sequences, and consequently more functional information is available in structures than in sequences. However, proteins generally interact with other proteins and molecules via their surface regions and a backbone-only analysis of protein structures may miss many of the functional and evolutionary features. Surface information can help better elucidate proteins' functions and their interactions with other proteins. Computational analysis and comparison of protein surfaces is an important challenge to overcome to enable efficient and accurate functional characterization of proteins.</p> <p>Methods</p> <p>In this study we present a new method for representation and comparison of protein surface features. Our method is based on mapping the 3-D protein surfaces onto 2-D maps using various dimension reduction methods. We have proposed area and neighbor based metrics in order to evaluate the accuracy of this surface representation. In order to capture functionally relevant information, we encode geometric and biochemical features of the protein, such as hydrophobicity, electrostatic potential, and curvature, into separate color channels in the 2-D map. The resulting images can then be compared using efficient 2-D image registration methods to identify surface regions and features shared by proteins.</p> <p>Results</p> <p>We demonstrate the utility of our method and characterize its performance using both synthetic and real data. Among the dimension reduction methods investigated, SNE, LandmarkIsomap, Isomap, and Sammon's mapping provide the best performance in preserving the area and neighborhood properties of the original 3-D surface. The enriched 2-D representation is shown to be useful in characterizing the functional site of chymotrypsin and able to detect structural similarities in heat shock proteins. A texture mapping using the 2-D representation is also proposed as an interesting application to structure visualization.</p

NOVEL ALGORITHMS AND TOOLS FOR LIGAND-BASED DRUG DESIGN

Computer-aided drug design (CADD) has become an indispensible component in modern drug discovery projects. The prediction of physicochemical properties and pharmacological properties of candidate compounds effectively increases the probability for drug candidates to pass latter phases of clinic trials. Ligand-based virtual screening exhibits advantages over structure-based drug design, in terms of its wide applicability and high computational efficiency. The established chemical repositories and reported bioassays form a gigantic knowledgebase to derive quantitative structure-activity relationship (QSAR) and structure-property relationship (QSPR). In addition, the rapid advance of machine learning techniques suggests new solutions for data-mining huge compound databases. In this thesis, a novel ligand classification algorithm, Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS), was reported for the prediction of diverse categorical pharmacological properties. LiCABEDS was successfully applied to model 5-HT1A ligand functionality, ligand selectivity of cannabinoid receptor subtypes, and blood-brain-barrier (BBB) passage. LiCABEDS was implemented and integrated with graphical user interface, data import/export, automated model training/ prediction, and project management. Besides, a non-linear ligand classifier was proposed, using a novel Topomer kernel function in support vector machine. With the emphasis on green high-performance computing, graphics processing units are alternative platforms for computationally expensive tasks. A novel GPU algorithm was designed and implemented in order to accelerate the calculation of chemical similarities with dense-format molecular fingerprints. Finally, a compound acquisition algorithm was reported to construct structurally diverse screening library in order to enhance hit rates in high-throughput screening

EFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands

Molecular structures and functions of the majority of proteins across different species are yet to be identified. Much needed functional annotation of these gene products often benefits from the knowledge of protein-ligand interactions. Towards this goal, we developed eFindSite, an improved version of FINDSITE, designed to more efficiently identify ligand binding sites and residues using only weakly homologous templates. It employs a collection of effective algorithms, including highly sensitive meta-threading approaches, improved clustering techniques, advanced machine learning methods and reliable confidence estimation systems. Depending on the quality of target protein structures, eFindSite outperforms geometric pocket detection algorithms by 15-40 % in binding site detection and by 5-35 % in binding residue prediction. Moreover, compared to FINDSITE, it identifies 14 % more binding residues in the most difficult cases. When multiple putative binding pockets are identified, the ranking accuracy is 75-78 %, which can be further improved by 3-4 % by including auxiliary information on binding ligands extracted from biomedical literature. As a first across-genome application, we describe structure modeling and binding site prediction for the entire proteome of Escherichia coli. Carefully calibrated confidence estimates strongly indicate that highly reliable ligand binding predictions are made for the majority of gene products, thus eFindSite holds a significant promise for large-scale genome annotation and drug development projects. eFindSite is freely available to the academic community at http://www.brylinski.org/efindsite. © 2013 Springer Science+Business Media Dordrecht