Data-Driven Analysis of Fluorination of Ligands of Aminergic G Protein Coupled Receptors.

Currently, G protein-coupled receptors are the targets with the highest number of drugs in many therapeutic areas. Fluorination has become a common strategy in designing highly active biological compounds, as evidenced by the steadily increasing number of newly approved fluorine-containing drugs. Herein, we identified in the ChEMBL database and analysed 1554 target-based FSAR sets (non-fluorinated compounds and their fluorinated analogues) comprising 966 unique non-fluorinated and 2457 unique fluorinated compounds active against 33 different aminergic GPCRs.

Introducing the metacore concept for multi-target ligand design.

In this work, we introduce the concept of "metacores" (MCs) for the organization of analog series (ASs) and multi-target (MT) ligand design. Generating compounds that are active against distantly related or unrelated targets is a central task in polypharmacology-oriented drug discovery. MCs are obtained by two-stage extraction of structural cores from ASs.

Systematic Data Analysis and Diagnostic Machine Learning Reveal Differences between Compounds with Single- and Multitarget Activity.

Small molecules with multitarget activity are capable of triggering polypharmacological effects and are of high interest in drug discovery. Compared to single-target compounds, promiscuity also affects drug distribution and pharmacodynamics and alters ADMET characteristics. Features distinguishing between compounds with single- and multitarget activity are currently only little understood.

Advances in exploring activity cliffs.

The activity cliff (AC) concept is of comparable relevance for medicinal chemistry and chemoinformatics. An AC is defined as a pair of structurally similar compounds with a large potency difference against a given target. In medicinal chemistry, ACs are of interest because they reveal small chemical changes with large potency effects, a concept referred to as structure-activity relationship (SAR) discontinuity.

Current Trends, Overlooked Issues, and Unmet Challenges in Virtual Screening.

Virtual compound screening focusing on hit identification is among the most popular computational approaches in pharmaceutical research. Yet, its opportunities and limitations are often not fully understood. Herein, we critically discuss several aspects of virtual screening that are thought to be of particular relevance for the field going forward.

Computational method for the identification of third generation activity cliffs.

In medicinal chemistry and chemoinformatics, activity cliffs (ACs) are defined as pairs of structurally similar compounds that are active against the same target but have a large difference in potency. Accordingly, ACs are rich in structure-activity relationship (SAR) information, which rationalizes their relevance for medicinal chemistry. For identifying ACs, a compound similarity criterion and a potency difference criterion must be specified.

Evolving Concept of Activity Cliffs.

Activity cliffs (ACs) are generally defined as pairs or groups of structurally similar compounds that are active against the same target but have large differences in potency. Accordingly, ACs capture chemical modifications that strongly influence biological activity. Therefore, they are of particular interest in structure-activity relationship (SAR) analysis and compound optimization.

Systematic Extraction of Analogue Series from Large Compound Collections Using a New Computational Compound-Core Relationship Method.

Chemical optimization of organic compounds produces a series of analogues. In addition to considering an analogue series (AS) or multiple series on a case-by-case basis, which is often done in the practice of chemistry, the extraction of analogues from compound repositories is of high interest in organic and medicinal chemistry. In organic chemistry, ASs are a source of alternative synthetic routes and also aid in exploring relationships between compounds from different sources including synthetic vs.

Rationalizing the Formation of Activity Cliffs in Different Compound Data Sets.

Activity cliffs are formed by structurally analogous compounds with large potency variations and are highly relevant for the exploration of discontinuous structure-activity relationships and compound optimization. So far, activity cliffs have mostly been studied on a case-by-case basis or assessed by global statistical analysis. Different from previous investigations, we report a large-scale analysis of activity cliff formation with a strong focus on individual compound activity classes (target sets).

Introducing a new category of activity cliffs with chemical modifications at multiple sites and rationalizing contributions of individual substitutions.

Activity cliffs (ACs) are formed by structurally similar active compounds with large potency differences. In medicinal chemistry, ACs are of high interest because they reveal structure-activity relationship (SAR) information and SAR determinants. Herein, we introduce a new type of ACs that consist of analog pairs with different substitutions at multiple sites (multi-site ACs; msACs).

Exploration of Target Synergy in Cancer Treatment by Cell-Based Screening Assay and Network Propagation Analysis.

Computational approaches have previously been introduced to predict compounds with activity against multiple targets or compound combinations with synergistic functional effects. By contrast, there are no computational studies available that explore combinations of targets that might act synergistically upon small molecule treatment. Herein, we introduce an approach designed to identify synergistic target pairs on the basis of cell-based screening data and compounds with known target annotations.

Second-generation activity cliffs identified on the basis of target set-dependent potency difference criteria.

Aim: Activity cliffs (ACs) are formed by structurally similar compounds with large potency differences. Accordingly, ACs reveal determinants of structure-activity relationships. This makes ACs highly interesting and relevant for medicinal chemistry and chemoinformatics.

Systematic identification of target set-dependent activity cliffs.

Aim: Generating a knowledge base of new activity cliffs (ACs) defined on the basis of compound set-dependent potency distributions, also taking confirmed inactive compounds into account.

Methodology: Different AC definitions, representations and search criteria were rationalized and applied.

Data: For nearly 100 different target proteins, for which medicinal chemistry and biological screening data were available, target set-dependent ACs were identified.

Computational Assessment of Chemical Saturation of Analogue Series under Varying Conditions.

Assessing the degree to which analogue series are chemically saturated is of major relevance in compound optimization. Decisions to continue or discontinue series are typically made on the basis of subjective judgment. Currently, only very few methods are available to aid in decision making.

X-ray-Structure-Based Identification of Compounds with Activity against Targets from Different Families and Generation of Templates for Multitarget Ligand Design.

Compounds with multitarget activity (promiscuity) are increasingly sought in drug discovery. However, promiscuous compounds are often viewed controversially in light of potential assay artifacts that may give rise to false-positive activity annotations. We have reasoned that the strongest evidence for true multitarget activity of small molecules would be provided by experimentally determined structures of ligand-target complexes.

Series of screening compounds with high hit rates for the exploration of multi-target activities and assay interference.

Aim: Generation of a database of analog series (ASs) with high assay hit rates for the exploration of assay interference and multi-target activities of compounds.

Methodology: ASs were computationally extracted from extensively tested screening compounds with high hit rates.

Data: A total of 6941 ASs were assembled comprising 14,646 unique compounds that were tested in a total of 1241 different assays covering 426 specified targets.

Redundancy in two major compound databases.

Public repositories of compounds and activity data are of prime importance for pharmaceutical research in academic and industrial settings. Major databases have evolved over the years. Their growth is accompanied by an increasing tendency toward data sharing.

Computational design of new molecular scaffolds for medicinal chemistry, part II: generalization of analog series-based scaffolds.

Aim: Extending and generalizing the computational concept of analog series-based (ASB) scaffolds.

Materials & Methods: Methodological modifications were introduced to further increase the coverage of analog series (ASs) and compounds by ASB scaffolds. From bioactive compounds, ASs were systematically extracted and second-generation ASB scaffolds isolated.

Towards a systematic assessment of assay interference: Identification of extensively tested compounds with high assay promiscuity.

A large-scale statistical analysis of hit rates of extensively assayed compounds is presented to provide a basis for a further assessment of assay interference potential and multi-target activities. A special feature of this investigation has been the inclusion of compound series information in activity analysis and the characterization of analog series using different parameters derived from assay statistics. No prior knowledge of compounds or targets was taken into consideration in the data-driven study of analog series.

Analog series-based scaffolds: computational design and exploration of a new type of molecular scaffolds for medicinal chemistry.

Aim: Computational design of and systematic search for a new type of molecular scaffolds termed analog series-based scaffolds.

Materials & Methods: From currently available bioactive compounds, analog series were systematically extracted, key compounds identified and new scaffolds isolated from them.

Results: Using our computational approach, more than 12,000 scaffolds were extracted from bioactive compounds.

Recent Advances in Scaffold Hopping.

Scaffold hopping refers to the computer-aided search for active compounds containing different core structures, which is a topic of high interest in medicinal chemistry. Herein foundations and caveats of scaffold hopping approaches are discussed and recent methodological developments analyzed. Despite the conceptual prevalence of pharmacophore methods for scaffold hopping, a variety of computational approaches have been successfully applied.

Charting Biologically Relevant Spirocyclic Compound Space.

Spirocycles frequently occur in natural products and experience increasing interest in drug discovery, given their richness in sp centers and distinct three-dimensionality. We have systematically explored chemical space populated with currently available bioactive spirocycles. Compounds containing spiro systems were classified and their scaffolds and spirocyclic ring combinations analyzed.

Highly Promiscuous Small Molecules from Biological Screening Assays Include Many Pan-Assay Interference Compounds but Also Candidates for Polypharmacology.

In PubChem screening assays, 466 highly promiscuous compounds were identified that were examined for known pan-assay interference compounds (PAINS) and aggregators using publicly available filters. These filters detected 210 PAINS and 67 aggregators. Compounds passing the filters included additional PAINS that were not detected, mostly due to tautomerism, and a variety of other potentially reactive compounds currently not encoded as PAINS.

Computational Method for the Systematic Identification of Analog Series and Key Compounds Representing Series and Their Biological Activity Profiles.

A computational methodology is introduced for detecting all unique series of analogs in large compound data sets, regardless of chemical relationships between analogs. No prior knowledge of core structures or R-groups is required, which are automatically determined. The approach is based upon the generation of retrosynthetic matched molecular pairs and analog networks from which distinct series are isolated.

Lessons learned from the design of chemical space networks and opportunities for new applications.

The concept of chemical space is of fundamental relevance in chemical informatics and computer-aided drug discovery. In a series of articles published in the Journal of Computer-Aided Molecular Design, principles of chemical space design were evaluated, molecular networks proposed as an alternative to conventional coordinate-based chemical reference spaces, and different types of chemical space networks (CSNs) constructed and analyzed. Central to the generation of CSNs was the way in which molecular similarity relationships were assessed and a primary focal point was the network-based representation of biologically relevant chemical space.