Information Loss in Network Pharmacology.

With the advent of increasing computational power and large-scale data acquisition, network analysis has become an attractive tool to study the organisation of complex systems and the interrelation of their constituent entities in various scientific domains. In many cases, relations only occur between entities of two different subsets, thereby forming a bipartite network. Often, the analysis of such bipartite networks involves the consideration of its two monopartite projections in order to focus on each entity subset individually as a means to deduce properties of the underlying original network.

Systematic pharmacological screens uncover novel pathways involved in cerebral cavernous malformations.

Cerebral cavernous malformations (CCMs) are vascular lesions in the central nervous system causing strokes and seizures which currently can only be treated through neurosurgery. The disease arises through changes in the regulatory networks of endothelial cells that must be comprehensively understood to develop alternative, non-invasive pharmacological therapies. Here, we present the results of several unbiased small-molecule suppression screens in which we applied a total of 5,268 unique substances to mutant worm, zebrafish, mouse, or human endothelial cells.

A Novel Drug-Mouse Phenotypic Similarity Method Detects Molecular Determinants of Drug Effects.

The molecular mechanisms that translate drug treatment into beneficial and unwanted effects are largely unknown. We present here a novel approach to detect gene-drug and gene-side effect associations based on the phenotypic similarity of drugs and single gene perturbations in mice that account for the polypharmacological property of drugs. We scored the phenotypic similarity of human side effect profiles of 1,667 small molecules and biologicals to profiles of phenotypic traits of 5,384 mouse genes.

Organ system heterogeneity DB: a database for the visualization of phenotypes at the organ system level.

Perturbations of mammalian organisms including diseases, drug treatments and gene perturbations in mice affect organ systems differently. Some perturbations impair relatively few organ systems while others lead to highly heterogeneous or systemic effects. Organ System Heterogeneity DB (http://mips.

Molecularly and clinically related drugs and diseases are enriched in phenotypically similar drug-disease pairs.

Background: The incomplete understanding of disease causes and drug mechanisms of action often leads to ineffective drug therapies or side effects. Therefore, new approaches are needed to improve treatment decisions and to elucidate molecular mechanisms underlying pathologies and unwanted drug effects.

Methods: We present here the first analysis of phenotypically related drug-disease pairs.

Systematic analysis of gene properties influencing organ system phenotypes in mammalian perturbations.

Motivation: Diseases and adverse drug reactions are frequently caused by disruptions in gene functionality. Gaining insight into the global system properties governing the relationships between genotype and phenotype is thus crucial to understand and interfere with perturbations in complex organisms such as diseases states.

Results: We present a systematic analysis of phenotypic information of 5047 perturbations of single genes in mice, 4766 human diseases and 1666 drugs that examines the relationships between different gene properties and the phenotypic impact at the organ system level in mammalian organisms.

HitPick: a web server for hit identification and target prediction of chemical screenings.

Motivation: High-throughput phenotypic assays reveal information about the molecules that modulate biological processes, such as a disease phenotype and a signaling pathway. In these assays, the identification of hits along with their molecular targets is critical to understand the chemical activities modulating the biological system. Here, we present HitPick, a web server for identification of hits in high-throughput chemical screenings and prediction of their molecular targets.

Drug-Target Networks.

Far from the traditional view of selective drug-target interactions, the recent accumulation of large amounts of interaction data for small-molecule drugs and protein targets requires innovative visualisation and analysis tools that are able to deal with what has become a truly complex system. In this context, network theory offers both a robust and illustrative framework to investigate drug-target connections and has been swiftly and widely embraced by the chemical biology and molecular informatics communities. A survey of the most recent applications of drug-target networks to detect cross-pharmacology relationships among targets and to identify new targets for known drugs is provided.

Design and exploration of target-selective chemical space representations.

We report the design of target-selective chemical spaces using CA-DynaMAD, a mapping algorithm that generates and navigates flexible space representations for the identification of active or selective compounds. The algorithm iteratively increases the dimensionality of reference spaces in a controlled manner by evaluating a single descriptor per iteration. For seven sets of closely related biogenic amine G protein coupled receptor (GPCR) antagonists with different selectivity, target-selective reference spaces were designed and used to identify selective compounds by screening a biologically annotated database.

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.

Methods for computer-aided chemical biology. Part 2: Evaluation of compound selectivity using 2D molecular fingerprints.

We analyze 558 compounds with selectivity against members of different protein families using two-dimensional molecular fingerprint methods. The calculations target compounds selective for 13 targets belonging to three families. These compound sets were especially designed for selectivity studies.

Methods for computer-aided chemical biology. Part 1: Design of a benchmark system for the evaluation of compound selectivity.

Computational drug design and discovery methods have traditionally put much emphasis on the identification of novel active compounds and the optimization of their potency. For chemical genetics and genomics applications, an important task is the identification of small molecules that are selective against target families, subfamilies, or individual targets and can be used as molecular probes for specific functions. In order to develop or tune computational methods for such applications, there is a need for molecular benchmark systems that focus on compound selectivity, rather than biological activity (in qualitative terms) or potency.

Analysis of a high-throughput screening data set using potency-scaled molecular similarity algorithms.

Molecular similarity methods for ligand-based virtual screening (VS) generally do not take compound potency as a variable or search parameter into account. We have incorporated a logarithmic potency scaling function into two conceptually distinct VS algorithms to account for relative compound potency during search calculations. A high-throughput screening (HTS) data set containing cathepsin B inhibitors was analyzed to evaluate the effects of potency scaling.

Mapping algorithms for molecular similarity analysis and ligand-based virtual screening: design of DynaMAD and comparison with MAD and DMC.

Here, we introduce the DynaMAD algorithm that is designed to map database compounds to combinations of activity-class-dependent descriptor value ranges in order to identify novel active molecules. The method combines and extends key features of two previously developed algorithms, MAD and DMC. These methods were first described as compound-mapping algorithms for large-scale virtual screening applications.