Working with small‐molecule datasets is a routine task for cheminformaticians and chemists. The analysis and comparison of vendor catalogues and the compilation of promising candidates as starting points for screening campaigns are but a few very common applications. The workflows applied for this purpose usually consist of multiple basic cheminformatics tasks such as checking for duplicates or filtering by physico‐chemical properties. Pipelining tools allow to create and change such workflows without much effort, but usually do not support interventions once the pipeline has been started. In many contexts, however, the best suited workflow is not known in advance, thus making it necessary to take the results of the previous steps into consideration before proceeding.To support intuition‐driven processing of compound collections, we developed MONA, an interactive tool that has been designed to prepare and visualize large small‐molecule datasets. Using an SQL database common cheminformatics tasks such as analysis and filtering can be performed interactively with various methods for visual support. Great care was taken in creating a simple, intuitive user interface which can be instantly used without any setup steps. MONA combines the interactivity of molecule database systems with the simplicity of pipelining tools, thus enabling the case‐to‐case application of chemistry expert knowledge. The current version is available free of charge for academic use and can be downloaded at http://www.zbh.uni‐hamburg.de/mona.

The accurate handling of different chemical file formats and the consistent conversion between them play important roles for calculations in complex cheminformatics workflows. Working with different cheminformatic tools often makes the conversion between file formats a mandatory step. Such a conversion might become a difficult task in cases where the information content substantially differs. This paper describes UNICON, an easy-to-use software tool for this task. The functionality of UNICON ranges from file conversion between standard formats SDF, MOL2, SMILES, PDB, and PDBx/mmCIF via the generation of 2D structure coordinates and 3D structures to the enumeration of tautomeric forms, protonation states, and conformer ensembles. For this purpose, UNICON bundles the key elements of the previously described NAOMI library in a single, easy-to-use command line tool.

Because of the availability of large compound collections on the Web, elementary cheminformatics tasks such as chemical library browsing, analyzing, filtering, or unifying have become widespread in the life science community. Furthermore, the high performance of desktop hardware allows an interactive, problem-driven approach to these tasks, avoiding rigid processing scripts and workflows. Here, we present MONA 2, which is the second major release of our cheminformatics desktop application addressing this need. Using MONA requires neither complex database setups nor expert knowledge of cheminformatics. A new molecular set concept purely based on structural entities rather than individual compounds has allowed the development of an intuitive user interface. Based on a chemically precise, high-performance software library, typical tasks on chemical libraries with up to one million compounds can be performed mostly interactively. This paper describes the functionality of MONA, its fundamental concepts, and a collection of application scenarios ranging from file conversion, compound library curation, and management to the post-processing of large-scale experiments.

The classification of molecules with respect to their inhibiting, activating, or toxicological potential constitutes a central aspect in the field of cheminformatics. Often, a discriminative feature is needed to distinguish two different molecule sets. Besides physicochemical properties, substructures and chemical patterns belong to the descriptors most frequently applied for this purpose. As a commonly used example of this descriptor class, SMARTS strings represent a powerful concept for the representation and processing of abstract chemical patterns. While their usage facilitates a convenient way to apply previously derived classification rules on new molecule sets, the manual generation of useful SMARTS patterns remains a complex and time-consuming process. Here, we introduce SMARTSminer, a new algorithm for the automatic derivation of discriminative SMARTS patterns from preclassified molecule sets. Based on a specially adapted subgraph mining algorithm, SMARTSminer identifies structural features that are frequent in only one of the given molecule classes. In comparison to elemental substructures, it also supports the consideration of general and specific SMARTS features. Furthermore, SMARTSminer is integrated into an interactive pattern editor named SMARTSeditor. This allows for an intuitive visualization on the basis of the SMARTSviewer concept as well as interactive adaption and further improvement of the generated patterns. Additionally, a new molecular matching feature provides an immediate feedback on a pattern's matching behavior across the molecule sets. We demonstrate the utility of the SMARTSminer functionality and its integration into the SMARTSeditor software in several different classification scenarios.

In many practical applications of structure-based virtual screening (VS) ligands are already known. This circumstance requires that the obtained hits need to satisfy initial made expectations i.e., they have to fulfill a predefined binding pattern and/or lie within a predefined physico-chemical property range. Based on the RApid Index-based Screening Engine (RAISE) approach, we introduce CRAISE-a user-controllable structure-based VS method. It efficiently realizes pharmacophore-guided protein-ligand docking to assess the library content but thereby concentrates only on molecules that have a chance to fulfill the given binding pattern. In order to focus only on hits satisfying given molecular properties, library profiles can be utilized to simultaneously filter compounds. CRAISE was evaluated on a range of strict to rather relaxed hypotheses with respect to its capability to guide binding-mode predictions and VS runs. The results reveal insights into a guided VS process. If a pharmacophore model is chosen appropriately, a binding mode below 2 Å is successfully reproduced for 85% of well-prepared structures, enrichment is increased up to median AUC of 73%, and the selectivity of the screening process is significantly enhanced leading up to seven times accelerated runtimes. In general, CRAISE supports a versatile structure-based VS approach allowing to assess hypotheses about putative ligands on a large scale.