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. Although a relatively small number of activity cliffs (defined as a pair of structurally similar compounds showing significant differences of activity -ΔpPot > 1.7) was found in FSAR sets, it is clear that appropriately introduced fluorine can increase ligand potency more than 50-fold. The analysis of matched molecular pairs (MMPs) networks indicated that the fluorination of the aromatic ring showed no clear trend towards a positive or negative effect on affinity; however, a favourable site for a positive potency effect of fluorination was the ortho position. Fluorination of aliphatic fragments more often led to a decrease in biological activity. The results may constitute the rules of thumb for fluorination of aminergic receptor ligands and provide insights into the role of fluorine substitutions in medicinal chemistry.

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. The methodology is chemically intuitive and generally applicable. Each MC represents a set of related ASs and a template for the generation of new structures. We have systematically identified ASs that exclusively consisted of analogs with MT activity and determined their target profiles. From these ASs, a large set of 317 structurally diverse MCs was extracted, 127 of which were associated with different target families. The newly generated MCs were characterized and further prioritized on the basis of AS, compound, and target coverage. The analysis indicated that 260 MCs were pharmaceutically relevant. These MCs and the compound and target information they capture are made freely available for medicinal chemistry applications.

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. On the basis of systematic data analysis, we have assembled large sets of promiscuous compounds with activity against related or functionally distinct targets and the corresponding compounds with single-target activity. Machine learning predicted promiscuous compounds with surprisingly high accuracy. Molecular similarity analysis combined with control calculations under varying conditions revealed that accurate predictions were largely determined by structural nearest-neighbor relationships between compounds from different classes. We also found that large proportions of promiscuous compounds with activity against related or unrelated targets and corresponding single-target compounds formed analog series with distinct chemical space coverage, which further rationalized the predictions. Moreover, compounds with activity against proteins from functionally distinct classes were often active against unique targets that were not covered by other promiscuous compounds. The results of our analysis revealed that nearest-neighbor effects determined the prediction of promiscuous compounds and that preferential partitioning of compounds with single- and multitarget activity into structurally distinct analog series was responsible for such effects, hence providing a rationale for the presence of different structure-promiscuity relationships.

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. Computationally, ACs can be systematically identified, going far beyond individual compound series considered during lead optimization. Large-scale analysis of ACs has revealed characteristic features across many different compound activity classes. The way in which the molecular similarity and potency difference criteria have been addressed for defining ACs distinguishes between different generations of ACs and mirrors the evolution of the AC concept. We discuss different stages of this evolutionary path and highlight recent advances in AC research.

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. So far a constant potency difference between AC partner compounds has mostly been set, e.g. 100-fold, irrespective of the specific activity (targets) of cliff-forming compounds. Herein, we introduce a computational methodology for AC identification and analysis that includes three novel components: •ACs are identified on the basis of variable target set-dependent potency difference criteria (a 'target set' represents a collection of compounds that are active against a given target protein).•ACs are extracted from computationally determined analog series (ASs) and consist of pairs of analogs with single or multiple substitution sites.•For multi-site ACs, a search for analogs with individual substitutions is performed to analyze their contributions to AC formation and determine if multi-site ACs can be represented by single-site ACs.

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. The AC concept is much more complex than it may appear at a first glance, especially if one aims to represent ACs computationally and identify them systematically. To these ends, molecular similarity and potency difference criteria must be carefully considered for AC assessment. Furthermore, ACs are often perceived differently in medicinal and computational chemistry, depending on whether they are studied on a case-by-case basis or systematically. For practical applications, intuitive access to AC information plays a major role. Over the years, the AC concept has been further refined and extended. Herein, we review the evolution of the AC concept, emphasizing new analysis schemes and findings that help to better understand ACs and extract SAR knowledge from them.

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. naturally occurring molecules. In medicinal chemistry, ASs are the major source of structure-activity relationship information and of hits or leads for drug development. ASs might be identified in different ways. For a given reference compound, a substructure search can be carried out using its scaffold. Alternatively, matched molecular pairs can be calculated to retrieve analogues from a compound repository. However, if no query compounds are used, the identification of ASs in databases is a difficult task. Herein, we introduce a computational approach to systematically identify ASs in collections of organic compounds. The approach involves compound decomposition on the basis of well-established retrosynthetic rules, organization of compound-core relationships, and identification of analogues sharing the same core. The method was applied on a large scale to extract ASs from the ChEMBL database, yielding more than 30 000 distinct series.

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). A systematic search for msACs across different classes of bioactive compounds identified more than 4000 of such ACs, most of which had substitutions at two sites (dual-site ACs; dsACs). A hierarchical analog data structure was designed to analyze contributions of individual substitutions to AC formation. Single substitutions were frequently found to determine potency differences captured by dsACs. Hence, in such cases, there was redundancy of AC information. In instances where both substitutions made significant contributions to dsACs, additive, synergistic, and compensatory effects were observed. Taken together, the results of our analysis revealed the prevalence of single-site ACs (ssACs) in analog series, followed by dsACs, which reveal different ways in which paired substitutions contribute to the formation of ACs and modulate SARs.

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. The targets involved in forming synergistic pairs were analyzed through a novel network propagation algorithm for rationalizing possible common synergy mechanisms. This algorithm enabled further analysis of each synergistic target pair and the identification of "interactors", i.e., proteins with higher propagation scores than would be expected by adding the individual contributions of each target in the synergistic pair. We detected 137 synergistic target pairs including 51 unique targets. A global network analysis of these 51 targets made it possible to derive a subnetwork of proteins with significant synergy. Furthermore, interactors were identified for 87 synergistic target pairs upon individual analysis of the network propagation of each pair. These interactors were associated with pathways related to cancer and apoptosis, membrane transport, and steroid metabolism and provided possible explanations of synergistic effects.

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. More than 20,000 target set-dependent ACs and associated information are made freely available. LIMITATIONS & NEXT STEPS: As more compound data become available for new targets, the search for target set-dependent ACs, including confirmed inactive compounds will continue. Second-generation ACs will be subjected to systematic structure-activity relationship analysis.

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. So far, ACs have been defined on the basis of generally applied molecular similarity and potency difference criteria. RESULTS: We present the first assessment of ACs taking target set-dependent compound potency distributions into account, leading to a new target set-dependent definition of ACs. The formation of these ACs is analyzed in detail. CONCLUSION: Second-generation ACs are obtained on the basis of target set-dependent potency difference thresholds. Compared with generally defined ACs, target set-dependent ACs have further increased medicinal chemistry relevance.

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. We further investigate and extend a computational concept to quantitatively assess the progression and chemical saturation of a series. To these ends, existing analogues and virtual candidates are compared in chemical space and compound neighborhoods are systematically analyzed. A large number of analogue series from different sources are studied, and alternative chemical space representations and virtual analogues of different designs are explored. Furthermore, evolving analogue series are distinguished computationally according to different saturation levels. Taken together, our findings provide a basis for practical applications of computational saturation analysis in compound optimization.

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. Therefore, we have carried out a systematic search of currently available X-ray structures for compounds forming complexes with different targets. Rather unexpectedly, 1418 such crystallographic ligands were identified, including 702 that formed complexes with targets from different protein families (multifamily ligands). About half of these multifamily ligands originated from the medicinal chemistry literature, making it possible to consider additional target annotations and search for analogues. From 168 distinct series of analogues containing one or more multifamily ligands, 133 unique analogue-series-based scaffolds were isolated that can serve as templates for the design of new compounds with multitarget activity. As a part of our study, all of the multifamily ligands we have identified and the analogue-series-based scaffolds are made freely available.

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. These ASs were organized and prioritized on the basis of different activity and assay frequency criteria. All ASs and associated information are made available in an open access deposition. NEXT STEPS: The large set of ASs will be further analyzed computationally and from a chemical perspective to identify assay interference compounds and candidates for exploring target promiscuity.

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. This is a positive development but not without potential problems. Using ChEMBL and PubChem as examples, we show that crosstalk between databases also leads to substantial data redundancy that might not be obvious. Redundancy is an important issue because it biases data analysis and knowledge extraction and leads to inflated views of available compounds, assays and activity data. Going forward it will be important to further refine data exchange and deposition criteria and make redundancy as transparent as possible.

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. RESULTS: More than 20,000 second-generation ASB scaffolds with single or multiple substitution sites were extracted from active compounds, achieving more than 90% coverage of ASs. CONCLUSION: Generalization of the ASB scaffold approach has yielded a large knowledge base of scaffold-capturing compound series and target information.

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). Compound potency distributions were systematically analyzed and categorized, and structural relationships were dissected and visualized on a per-set basis. Our study uncovered target set-dependent interplay of potency distributions and structural relationships and revealed the presence of activity cliffs and origins of cliff formation in different structure-activity relationship environments.

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. It was anticipated that taking large volumes of activity data, assay frequency, and assay overlap information into account would lead to statistically sound and chemically meaningful results. More than 6000 unique series of analogs with high hit rates were identified, more than 5000 of which did not contain known interference candidates, hence providing ample opportunities for follow-up analyses from a medicinal chemistry perspective.

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. In recent years, scaffold hopping calculations are increasingly carried out at the level of scaffolds rather than compounds, and scaffold queries increasingly abstract from chemical structures. In addition, relationships between compounds, scaffolds, and biological activities are beginning to be globally explored, beyond individual applications. Going forward, computational scaffold hopping is thought to benefit from the consideration of new scaffold concepts and the development of methods capable of guiding search calculations toward scaffolds that are likely to represent potent compounds.