A submerged Stone Age hunting architecture from the Western Baltic Sea.

The Baltic Sea basins, some of which only submerged in the mid-Holocene, preserve Stone Age structures that did not survive on land. Yet, the discovery of these features is challenging and requires cross-disciplinary approaches between archeology and marine geosciences. Here, we combine shipborne and autonomousunderwater vehicle hydroacoustic data with up to a centimeter range resolution, sedimentological samples, and optical images to explore a Stone Age megastructure located in 21 m water depth in the Bay of Mecklenburg, Germany. The structure is made of 1,673 individual stones which are usually less than 1 m in height, placed side by side over a distance of 971 m in a way that argues against a natural origin by glacial transport or ice push ridges. Running adjacent to the sunken shoreline of a paleolake (or bog), whose youngest phase was dated to 9,143 ±36 ka B.P., the stonewall was likely used for hunting the Eurasian reindeer (Rangifer tarandus) during the Younger Dryas or early Pre-Boreal. It was built by hunter-gatherer groups that roamed the region after the retreat of the Weichselian Ice Sheet. Comparable Stone Age megastructures have become known worldwide in recent times but are almost unknown in Europe. The site represents one of the oldest documented man-made hunting structures on Earth, and ranges among the largest known Stone Age structure in Europe. It will become important for understanding subsistence strategies, mobility patterns, and inspire discussions concerning the territorial development in the Western Baltic Sea region.

Molecular similarity concepts and search calculations.

The introduction of molecular similarity analysis in the early 1990s has catalyzed the development of many small-molecule-based similarity methods to mine large compound databases for novel active molecules. These efforts have profoundly influenced the field of computer-aided drug discovery and substantially widened the spectrum of available ligand-based virtual screening approaches. However, the principles underlying the computational assessment of molecular similarity are much more multifaceted and complex than it might appear at first glance. Accordingly, intrinsic features of molecular similarity analysis and its relationship to other methods are often not well understood. This chapter discusses critical aspects of molecular similarity, an understanding of which is essential for the evaluation of method development in this field. Then it describes studies designed to enhance the performance of molecular fingerprint searching, which is one of the most intuitive and widely used similarity-based methods.

Simulation of sequential screening experiments using emerging chemical patterns.

A method called "Emerging Chemical Patterns" (ECP) has recently been introduced as a novel approach to binary molecular classification (for example, "active" versus "inactive"). The underlying pattern recognition algorithm was first introduced in computer science and then adopted for applications in medicinal chemistry and compound screening. A special feature is its ability to accurately classify molecules on the basis of very small training sets containing only a few compounds. This feature is highly relevant for virtual compound screening when only very few experimental hits are available as templates. Here we adopt ECP calculations to simulate sequential screening using an experimental high-throughput screening (HTS) data set containing inhibitors of dihydrofolate reductase. In doing so, we focus on minimizing the number of database compounds that need to be evaluated in order to identify a substantial fraction of available hits. We demonstrate that iterative ECP calculations recover on average between approximately 19% and approximately 39% of available hits in the data set while dramatically reducing the number of compounds that need to be tested to between approximately 0.002% and approximately 9% of the screening database.

Distinguishing between bioactive and modeled compound conformations through mining of emerging chemical patterns.

To systematically compare bioactive and theoretically derived compound conformations, we have analyzed 18 different sets of active small molecules with experimentally determined binding conformations and modeled conformers using a pattern recognition approach. Compound class-specific descriptor value range patterns that accurately distinguish bioactive conformations from other low-energy conformers were identified for all 18 compound classes. Discriminatory patterns were often chemically intuitive and could be well rationalized on the basis of X-ray structures of the protein-ligand complexes. Target-specific descriptor patterns can be used as filters to screen conformational ensembles for bioactive conformations.

Formal concept analysis for the identification of molecular fragment combinations specific for active and highly potent compounds.

We introduce fragment formal concept analysis (FragFCA) to study complex relationships between fragments in active compounds taking potency information into account. Fragment combinations that are unique to active or highly potent compounds or that are shared by molecules having different or overlapping activity profiles are systematically identified using chemically intuitive queries of varying complexity. The methodology is applied to analyze fragment distributions in antagonists of seven G protein coupled receptor targets and identify signature fragments. Pairs or triplets of molecular fragments are found to be most specific for different activity profiles and compound potency levels. In addition, we demonstrate the ability of FragFCA to identify selective hits in high-throughput screening data sets.

Exploring structure-selectivity relationships of biogenic amine GPCR antagonists using similarity searching and dynamic compound mapping.

We design and analyze compound selectivity sets of antagonists with differential selectivity against seven biogenic amine G-protein coupled receptors. The selectivity sets consist of a total of 267 antagonists and contain a spectrum of in part closely related molecular scaffolds. Each set represents a different selectivity profile. Using these com- pound sets, a systematic computational analysis of structure-selectivity relationships is carried out with different 2D similarity methods including fingerprints, recursive partitioning, clustering, and dynamic compound mapping. Screening calculations are performed in a background database containing nearly four million molecules. Fingerprint searching and compound mapping are found to enrich target-selective antagonists over family-selective ones. Dynamic compound mapping effectively discriminates database compounds from GPCR antagonists and consistently retains target-selective antagonists during the final dimension extension levels. Furthermore, the widely used MACCS key fingerprint displays a strong tendency to distinguish between target- and family-selective GPCR antagonists. Taken together, the results indicate that different types of 2D similarity methods are capable of distinguishing closely related molecules having different selectivity. The reported compound benchmark system is made freely available in order to enable selectivity-oriented analyses using other computational approaches.

Emerging chemical patterns: a new methodology for molecular classification and compound selection.

A concept termed Emerging Chemical Patterns (ECPs) is introduced as a novel approach to molecular classification. The methodology makes it possible to extract key molecular features from very few known active compounds and classify molecules according to different potency levels. The approach was developed in light of the situation often faced during the early stages of lead optimization efforts: too few active reference molecules are available to build computational models for the prediction of potent compounds. The ECP method generates high-resolution signatures of active compounds. Predictive ECP models can be built based on the information provided by sets of only three molecules with potency in the nanomolar and micromolar range. In addition to individual compound predictions, an iterative ECP scheme has been designed. When applied to different sets of active molecules, iterative ECP classification produced compound selection sets with increases in average potency of up to 3 orders of magnitude.