It is well appreciated that the results of ligand-based virtual screening (LBVS) are much influenced by methodological details, given the generally strong compound class dependence of LBVS methods. ...It is less well understood to what extent structure−activity relationship (SAR) characteristics might influence the outcome of LBVS. We have assessed the hypothesis that the success of prospective LBVS depends on the SAR tolerance of screening targets, in addition to methodological aspects. In this context, SAR tolerance is rationalized as the ability of a target protein to specifically interact with series of structurally diverse active compounds. In compound data sets, SAR tolerance articulates itself as SAR continuity, i.e., the presence of structurally diverse compounds having similar potency. In order to analyze the role of SAR tolerance for LBVS, activity landscape representations of compounds active against 16 different target proteins were generated for which successful LBVS applications were reported. In all instances, the activity landscapes of known active compounds contained multiple regions of local SAR continuity. When analyzing the location of newly identified LBVS hits and their SAR environments, we found that these hits almost exclusively mapped to regions of distinct local SAR continuity. Taken together, these findings indicate the presence of a close link between SAR tolerance at the target level, SAR continuity at the ligand level, and the probability of LBVS success.
Modeling of activity landscapes provides a basis for the analysis of structure–activity relationships (SARs) in large compound data sets. Activity landscape models enable visual access to SAR ...features. Regardless of their specific details, these models generally have in common that they integrate molecular similarity and potency relationships between active compounds. Different two-dimensional (2D) landscape representations have been introduced and recently also the first detailed three-dimensional (3D) model. Herein we compare advanced 2D and 3D activity landscape models for compound data sets having different SAR character. Although the compared 2D and 3D representations are conceptually distinct, it is found that global SAR features of compound data sets can be equally well deduced from them. However, local SAR information is often captured in different ways by these representations. Since these 2D and 3D landscape modeling tools have been made freely available, the analysis also provides guidelines for how to best utilize these alternative landscape representations for practical SAR analysis.
Purpose: A computational approach is described to analyze structure–activity relationship (SAR) information contained in compound and screening data sets. The methodology is designed to explore SAR ...information in a systematic and compound-centric manner in order to aid in the selection of hits from high-throughput screening (HTS) data. Methods: Chemical neighborhood graphs integrate a graphical representation of the chemical environment of each active compound in a data set with the potency distribution within its neighborhood and information from a quantitative SAR analysis function. Environments are systematically generated and ranked by SAR information content. From these environments, key compounds and compound series can be selected. Results: The methodology is described in detail. In addition, the application to four screening data sets is reported, revealing different SAR characteristics. A number of different examples of compound environments are presented and discussed that have varying SAR information content. Conclusion: Chemical neighborhood graphs provide an intuitive graphical access to SAR information contained in hit sets. SAR information is analyzed in a compound-centric manner, with a focus on local SAR environments (microenvironments). It is anticipated that this approach will complement and help to further refine current hit selection strategies and trigger the development of additional graphical analysis methods to search for SAR information in HTS data.
Biogas production is a relevant component in renewable energy systems. The paper addresses modeling approaches from an energy system, as well as from a process optimization, point of view. Model ...approaches of biogas production show different levels of detail. They can be classified as white, gray, and black box, or bottom-up and top-down approaches. On the one hand, biogas modeling can supply dynamic information on the anaerobic digestion process, e.g., to predict biogas yields or to optimize the anaerobic digestion process. These models are characterized by a bottom-up approach with different levels of detail: the comprehensive ADM1 (white box), simplifications and abstractions of AD models (gray box), or highly simplified process descriptions (black box). On the other hand, biogas production is included in energy system models. These models usually supply aggregated information on regional biogas potentials and greenhouse gas emissions. They are characterized by a top-down approach with a low level of detail. Most energy system models reported in literature are based on black box approaches. Considering the strengths and weaknesses of the integration of detailed and deeply investigated process models in energy system models reveals the opportunity to develop dynamic and fluctuating business models of biogas usage.
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