Crystal structure databases offer ample opportunities to derive small molecule conformation preferences, but the derived knowledge is not systematically applied in drug discovery research. We address this gap by a comprehensive and extendable expert system enabling quick assessment of the probability of a given conformation to occur. It is based on a hierarchical system of torsion patterns that cover a large part of druglike chemical space. Each torsion pattern has associated frequency histograms generated from CSD and PDB data and, derived from the histograms, traffic-light rules for frequently observed, rare, and highly unlikely torsion ranges. Structures imported into the corresponding software are annotated according to these rules. We present the concept behind the tree of torsion patterns, the design of an intuitive user interface for the management and usage of the torsion library, and we illustrate how the system helps analyze and understand conformation properties of substructures widely used in medicinal chemistry.

The generation of sets of low-energy conformations for a given molecule is a central task in drug design. Herein we present a new conformation generator called CONFECT that builds on our previously published library of torsion patterns. Conformations are generated as well as ranked by means of normalized frequency distributions derived from the Cambridge Structural Database (CSD). Following an incremental construction approach, conformations are selected from a systematic enumeration within energetic boundaries. The new tool is benchmarked in several different ways, indicating that it allows the efficient generation of high-quality conformation ensembles. These ensembles are smaller than those produced by state-of-the-art tools, yet they effectively cover conformational space.

Advantages like intuitive interpretation, objectivity, general applicability, and its easy, automated calculation make the rmsd (root-mean-squared deviation) the measure of choice for the investigation of the accuracy of conformational model generators. For comparing conformations of a single molecule this is a clearly superior method. Single molecule analysis is, however, a rare scenario. Typically, conformations are generated for huge corporate or external vendor databases of high diversity which are then further investigated with high-throughput computational methods like docking or pharmacophore searching, in virtual screening campaigns. Representative subsets for accuracy investigations of computational methods need to mimic this diversity. Averaged rmsd values over these data sets are frequently used to assess the accuracy of the methods. There are, however, significant weaknesses in rmsd comparisons for such kind of data sets. The interpretation is for example no longer intuitive because what can be expected in terms of good or bad rmsd values crucially depends on the data set composition like size or number of rotatable bonds of the underlying molecules. Further, rmsd lacks normalization which might result in very high averaged rmsd values for highly flexible molecules and thus might completely skew results. We have developed a novel measure to compare conformations of molecules called Torsion Fingerprint Deviation (TFD). It extracts, weights, and compares Torsion Fingerprints from a query molecule and generated conformations under consideration of acyclic bonds as well as ring systems. TFD is alignment-free and overcomes major limitations of rmsd while retaining its advantages.

Summary:To analyse the vast amount of genome annotation data available today, a visual representation of genomic features in a given sequence range is required. We developed a C library which provides layout and drawing capabilities for annotation features. It supports several common input and output formats and can easily be integrated into custom C applications. To exemplify the use of AnnotationSketch in other languages, we provide bindings to the scripting languages Ruby, Python and Lua.

The inside cover picture shows the conformational ensemble of AMP generated by CONFECT, a novel knowledge‐based conformer generator. Conformations are generated by using lists of “usual” torsion angles derived from CSD‐based frequency histograms (top left). One of the bioactive conformations of AMP is shown in the binding site of the protein from PDB complex 3TV2 (bottom right). For more details, see the Full Paper by Matthias Rarey et al. on