Design of combinatorial libraries for the exploration of virtual hits from fragment space searches with LoFT.

A case study is presented illustrating the design of a focused CDK2 library. The scaffold of the library was detected by a feature trees search in a fragment space based on reactions from combinatorial chemistry. For the design the software LoFT (Library optimizer using Feature Trees) was used. The special feature called FTMatch was applied to restrict the parts of the queries where the reagents are permitted to match. This way a 3D scoring function could be simulated. Results were compared with alternative designs by GOLD docking and ROCS 3D alignments.

Improving similarity-driven library design: customized matching and regioselective feature trees.

Reduced graph descriptors, like feature trees, are frequently applied in cases where the relative arrangement of functional groups is more important than exact substructure matches. Due to their ability to deal with fragmented molecules, they are well-suited for fragment space search and library design. We recently presented LoFT, a novel focused library design approach based on feature trees. During evaluation two drawbacks of the reduced graph descriptor were discovered: First, regioisomeric substructures cannot be distinguished in feature tree mappings which results in a large information loss especially when connecting R-groups to cores. Second, the automatic matching procedure might result in undesired alignments, since the knowledge on what is considered as core by the user is not taken into account. In the following, we will present two approaches to overcome those drawbacks. The generation of the feature trees is modified, so that different arene substitution patterns can be recognized and a customized matching is introduced, allowing the user to determine the parts of the query, where the reagents are allowed to match. Subsequently we investigate the improvements on library design by reviewing the design scenarios which were already used for the evaluation of LoFT.

NAOMI: on the almost trivial task of reading molecules from different file formats.

In most cheminformatics workflows, chemical information is stored in files which provide the necessary data for subsequent calculations. The correct interpretation of the file formats is an important prerequisite to obtain meaningful results. Consistent reading of molecules from files, however, is not an easy task. Each file format implicitly represents an underlying chemical model, which has to be taken into consideration when the input data is processed. Additionally, many data sources contain invalid molecules. These have to be identified and either corrected or discarded. We present the chemical file format converter NAOMI, which provides efficient procedures for reliable handling of molecules from the common chemical file formats SDF, MOL2, and SMILES. These procedures are based on a consistent chemical model which has been designed for the appropriate representation of molecules relevant in the context of drug discovery. NAOMI's functionality is tested by round robin file IO exercises with public data sets, which we believe should become a standard test for every cheminformatics tool.

LoFT: similarity-driven multiobjective focused library design.

We present LoFT, a tool for focused combinatorial library design. LoFT provides a set of algorithms, constructing a focused library from a chemical fragment space under optimization of multiple design criteria. A weighted multiobjective scoring function based on physicochemical descriptors is employed for traversing the chemical search space. The new aspect of LoFT is that a similarity-driven product-based library design approach is provided on fragment level. For this reason the feature tree descriptor is incorporated for similarity comparison of library compounds to given bioactive molecules as well as for diversifying the resulting libraries. The feature tree descriptor abstracts the molecular graph to a tree structure where the nodes are labeled with physicochemical properties. For comparison, the nodes of two trees are mapped onto each other. This strictly hierarchical mechanism is suitable for the efficient comparison of chemical fragments, allowing the evaluation of the resulting products on fragment level without explicitly enumerating them. LoFT was validated, applying three different data sets. Starting with a random reagent selection, we optimized the libraries using maximum similarity to known bioactive molecules and iteratively adding further criteria. Moreover, we compared these results with data we obtained with FTrees-FS.

SwiFT: an index structure for reduced graph descriptors in virtual screening and clustering.

A reduced graph descriptor represents molecules by small node-labeled graphs. They allow fast similarity calculation, while retaining the overall arrangement of functional groups. The feature tree as an example of this descriptor type abstracts a molecule by a node-labeled, unrooted tree. One available algorithm for pairwise feature tree comparison is the match-search algorithm, which matches the subtrees of two feature trees on each other and therefore creates an alignment. In this work, we document the extension to reuse partial results on the global level of the whole feature tree data set where a high number of identical subtrees exists. The method is based on indexing all occurring subtrees in a data set. On the basis of this index, the similarity value between every subtree combination has to be computed only once. While calculating identical similarities, this approach leads to a substantial reduction in run time by up to 80% and can be used in a parallel computation environment. The search tree built for indexing can also be used to identify duplicated feature trees.