The Valence State Combination Model: A Generic Framework for Handling Tautomers and Protonation States

The consistent handling of molecules is probably the most basic and important requirement in the field of cheminformatics. Reliable results can only be obtained if the underlying calculations are independent of the specific way molecules are represented in the input data. However, ensuring consistency is a complex task with many pitfalls, an important one being the fact that the same molecule can be represented by different valence bond structures. In order to achieve reliability, a cheminformatics system needs to solve two fundamental problems. First, different choices of valence bond structures must be identified as the same molecule. Second, for each molecule all valence bond structures relevant to the context must be taken into consideration. The latter is especially important with regard to tautomers and protonation states, as these have considerable influence on physicochemical properties of molecules. We present a comprehensive method for the rapid and consistent generation of reasonable tautomers and protonation states for molecules relevant in the context of drug design. This method is based on a generic scheme, the Valence State Combination Model, which has been designed for the enumeration and scoring of valence bond structures in large data sets. In order to ensure our method’s consistency, we have developed procedures which can serve as a general validation scheme for similar approaches. The analysis of both the average number of generated structures and the associated runtimes shows that our method is perfectly suited for typical cheminformatics applications. By comparison with frequently used and curated public data sets, we can demonstrate that the tautomers and protonation state produced by our method are chemically reasonable

Unique Ring Families: A Chemically Meaningful Description of Molecular Ring Topologies

The perception of a set of rings forms the basis for a number of chemoinformatics applications, e.g. the systematic naming of compounds, the calculation of molecular descriptors, the matching of SMARTS expressions, and the generation of atomic coordinates. We introduce the concept of unique ring families (URFs) as an extension of the concept of relevant cycles (RCs)., URFs are consistent for different atom orders and represent an intuitive description of the rings of a molecular graph. Furthermore, in contrast to RCs, URFs are polynomial in number. We provide an algorithm to efficiently calculate URFs in polynomial time and demonstrate their suitability for real-time applications by providing computing time benchmarks for the PubChem Database. URFs combine three important properties of chemical ring descriptions, for the first time, namely being unique, chemically meaningful, and efficient to compute. Therefore, URFs are a valuable alternative to the commonly used concept of the smallest set of smallest rings (SSSR) and would be suited to become the standard measure for ring topologies of small molecules

Reading PDB: Perception of Molecules from 3D Atomic Coordinates

The analysis of small molecule crystal structures is a common way to gather valuable information for drug development. The necessary structural data is usually provided in specific file formats containing only element identities and three-dimensional atomic coordinates as reliable chemical information. Consequently, the automated perception of molecular structures from atomic coordinates has become a standard task in cheminformatics. The molecules generated by such methods must be both chemically valid and reasonable to provide a reliable basis for subsequent calculations. This can be a difficult task since the provided coordinates may deviate from ideal molecular geometries due to experimental uncertainties or low resolution. Additionally, the quality of the input data often differs significantly thus making it difficult to distinguish between actual structural features and mere geometric distortions. We present a method for the generation of molecular structures from atomic coordinates based on the recently published NAOMI model. By making use of this consistent chemical description, our method is able to generate reliable results even with input data of low quality. Molecules from 363 Protein Data Bank (PDB) entries could be perceived with a success rate of 98%, a result which could not be achieved with previously described methods. The robustness of our approach has been assessed by processing all small molecules from the PDB and comparing them to reference structures. The complete data set can be processed in less than 3 min, thus showing that our approach is suitable for large scale applications

Fast Protein Binding Site Comparison via an Index-Based Screening Technology

We present TrixP, a new index-based method for fast protein binding site comparison and function prediction. TrixP determines binding site similarities based on the comparison of descriptors that encode pharmacophoric and spatial features. Therefore, it adopts the efficient core components of TrixX, a structure-based virtual screening technology for large compound libraries. TrixP expands this technology by new components in order to allow a screening of protein libraries. TrixP accounts for the inherent flexibility of proteins employing a partial shape matching routine. After the identification of structures with matching pharmacophoric features and geometric shape, TrixP superimposes the binding sites and, finally, assesses their similarity according to the fit of pharmacophoric properties. TrixP is able to find analogies between closely and distantly related binding sites. Recovery rates of 81.8% for similar binding site pairs, assisted by rejecting rates of 99.5% for dissimilar pairs on a test data set containing 1331 pairs, confirm this ability. TrixP exclusively identifies members of the same protein family on top ranking positions out of a library consisting of 9802 binding sites. Furthermore, 30 predicted kinase binding sites can almost perfectly be classified into their known subfamilies