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.

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.

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.

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.

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.

Charting Biologically Relevant Spirocyclic Compound Space.

Spirocycles frequently occur in natural products and experience increasing interest in drug discovery, given their richness in sp3 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. Nearly 47 000 compounds were identified that contained spirocycles in different structural contexts and were active against roughly 200 targets, among which several pharmaceutically relevant members of the G protein-coupled receptor (GPCR) family were identified. Spirocycles and corresponding compounds displayed notable scaffold diversity but contained only limited numbers of combinations of differently sized rings. These observations indicate that there should be significant potential to further expand spirocyclic chemical space for drug discovery, exploiting the privileged substructure concept. Inspired by those findings, we embarked on the design and chemical synthesis of three distinct novel spirocyclic scaffolds that qualify for downstream library synthesis, thus exploring principally new chemical space with high potential for pharmaceutical research.

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. The design and comparison of CSNs based upon alternative similarity measures can be viewed as an evolutionary path with interesting lessons learned along the way. CSN design has matured to the point that such chemical space representations can be used in practice. In this contribution, highlights from the sequence of CSN design efforts are discussed in context, providing a perspective for future practical applications.

Computational polypharmacology analysis of the heat shock protein 90 interactome.

The design of a single drug molecule that is able to simultaneously and specifically interact with multiple biological targets is gaining major consideration in drug discovery. However, the rational design of drugs with a desired polypharmacology profile is still a challenging task, especially when these targets are distantly related or unrelated. In this work, we present a computational approach aimed at the identification of suitable target combinations for multitarget drug design within an ensemble of biologically relevant proteins. The target selection relies on the analysis of activity annotations present in molecular databases and on ligand-based virtual screening. A few target combinations were also inspected with structure-based methods to demonstrate that the identified dual-activity compounds are able to bind target combinations characterized by remote binding site similarities. Our approach was applied to the heat shock protein 90 (Hsp90) interactome, which contains several targets of key importance in cancer. Promising target combinations were identified, providing a basis for the computational design of compounds with dual activity. The approach may be used on any ensemble of proteins of interest for which known inhibitors are available.

Systematic assessment of scaffold hopping versus activity cliff formation across bioactive compound classes following a molecular hierarchy.

Scaffold hopping and activity cliff formation define opposite ends of the activity landscape feature spectrum. To rationalize these events at the level of scaffolds, active compounds involved in scaffold hopping were required to contain topologically distinct scaffolds but have only limited differences in potency, whereas compounds involved in activity cliffs were required to share the same scaffold but have large differences in potency. A systematic search was carried out for compounds involved in scaffold hopping and/or activity cliff formation. Results obtained for compound data sets covering more than 300 human targets revealed clear trends. If scaffolds represented multiple but fewer than 10 active compounds, nearly 90% of all scaffolds were exclusively involved in hopping events. With increasing compound coverage, the fraction of scaffolds involved in both scaffold hopping and activity cliff formation significantly increased to more than 50%. However, ∼40% of the scaffolds representing large numbers of active compounds continued to be exclusively involved in scaffold hopping. More than 200 scaffolds with broad target coverage were identified that consistently represented potent compounds and yielded an abundance of scaffold hops in the low-nanomolar range. These and other subsets of scaffolds we characterized are of prime interest for structure-activity relationship (SAR) exploration and compound design. Therefore, the complete scaffold classification generated in the course of our analysis is made freely available.

Composition and topology of activity cliff clusters formed by bioactive compounds.

The assessment of activity cliffs has thus far mostly focused on compound pairs, although the majority of activity cliffs are not formed in isolation but in a coordinated manner involving multiple active compounds and cliffs. However, the composition of coordinated activity cliff configurations and their topologies are unknown. Therefore, we have identified all activity cliff configurations formed by currently available bioactive compounds and analyzed them in network representations where activity cliff configurations occur as clusters. The composition, topology, frequency of occurrence, and target distribution of activity cliff clusters have been determined. A limited number of large cliff clusters with unique topologies were identified that were centers of activity cliff formation. These clusters originated from a small number of target sets. However, most clusters were of small to moderate size. Three basic topologies were sufficient to describe recurrent activity cliff cluster motifs/topologies. For example, frequently occurring clusters with star topology determined the scale-free character of the global activity cliff network and represented a characteristic activity cliff configuration. Large clusters with complex topology were often found to contain different combinations of basic topologies. Our study provides a first view of activity cliff configurations formed by currently available bioactive compounds and of the recurrent topologies of activity cliff clusters. Activity cliff clusters of defined topology can be selected, and from compounds forming the clusters, SAR information can be obtained. The SAR information of activity cliff clusters sharing a/one specific activity and topology can be compared.

Activity cliff networks for medicinal chemistry.

Network representations are widely used in bioinformatics but have only been little explored in chemistry. Thus far, only a few attempts have been made to generate and analyze compound networks. Among these are the first activity cliff networks. In medicinal chemistry, activity cliffs are focal points of structure-activity relationships (SAR) analysis. Activity cliffs have generally been defined as pairs of structurally similar or analogous active compounds that have a large difference in potency against their target. However, most activity cliffs are not formed in isolation but in a coordinated manner involving multiple highly and weakly potent compounds. Recently, a comprehensive activity cliff network has been generated for current public domain bioactive compounds, hence providing a first global view of activity cliff formation. The design of activity cliff networks is discussed herein. From the global activity cliff network, local networks can be extracted for individual compound activity classes that provide graphical access to high-level SAR information for compound optimization efforts.

New chemotypes for wALADin1-like inhibitors of delta-aminolevulinic acid dehydratase from Wolbachia endobacteria.

Substituted benzimidazoles of the wALADin1-family have recently been identified as a new class of species-selective inhibitors of delta-aminolevulinic acid dehydratase (ALAD) from Wolbachia endobacteria of parasitic filarial worms. Due to its Wolbachia-dependent antifilarial activity, wALADin1 is a starting point for the development of new drugs against filarial nematodes. We now present several other chemotypes of ALAD inhibitors that have been identified based upon their molecular similarity to wALADin1. A tricyclic quinoline derivative (wALADin2) with a different inhibitory mechanism and improved inhibitory potency and selectivity may represent an improved drug lead candidate.

Quantifying the fingerprint descriptor dependence of structure-activity relationship information on a large scale.

It is well-known that different molecular representations, e.g., graphs, numerical descriptors, fingerprints, or 3D models, change the numerical results of molecular similarity calculations. Because the assessment of structure-activity relationships (SARs) requires similarity and potency comparisons of active compounds, this representation dependence inevitably also affects SAR analysis. But to what extent? How exactly does SAR information change when alternative fingerprints are used as descriptors? What is the proportion of active compounds with substantial changes in SAR information induced by different fingerprints? To provide answers to these questions, we have quantified changes in SAR information across many different compound classes using six different fingerprints. SAR profiling was carried out on 128 target-based data sets comprising more than 60,000 compounds with high-confidence activity annotations. A numerical measure of SAR discontinuity was applied to assess SAR information on a per compound basis. For ~70% of all test compounds, changes in SAR characteristics were detected when different fingerprints were used as molecular representations. Moreover, the SAR phenotype of ~30% of the compounds changed, and distinct fingerprint-dependent local SAR environments were detected. The fingerprints we compared were found to generate SAR models that were essentially not comparable. Atom environment and pharmacophore fingerprints produced the largest differences in compound-associated SAR information. Taken together, the results of our systematic analysis reveal larger fingerprint-dependent changes in compound-associated SAR information than would have been anticipated.

Compound pathway model to capture SAR progression: comparison of activity cliff-dependent and -independent pathways.

A compound pathway model is introduced to monitor SAR progression in compound data sets. Pathways are formed by sequences of structurally analogous compounds with stepwise increasing potency that ultimately yield highly potent compounds. Hence, the model was designed to mimic compound optimization efforts. Different pathway categories were defined. Pathways originating from any active compound in a data set were systematically identified including compounds forming activity cliffs. The relative frequency of activity cliff-dependent and -independent pathways was determined and compared. In 23 of 39 different compound data sets that qualified for our analysis, significant differences in the relative frequency of activity cliff-dependent and -independent pathways were observed. In 17 of these 23 data sets, activity cliff-dependent pathways occurred with higher relative frequency than cliff-independent pathways. In addition, pathways originating from the majority of activity cliff compounds displayed desired SAR progression, reflecting SAR information gain associated with activity cliffs.

Frequency of occurrence and potency range distribution of activity cliffs in bioactive compounds.

We have systematically identified activity cliffs in bioactive compounds for which high-confidence potency data were available. Different molecular representations were utilized and similarity and potency difference criteria for activity cliffs were clearly defined. Cliff formation was studied across the global potency range observed for qualifying bioactive compounds. Depending on the specific representation of activity cliffs, between ∼22% and 34% of all active compounds meeting the data selection criteria were involved in the formation of at least one activity cliff spanning a potency difference of at least two orders of magnitude. However, these cliffs involved only between ∼4.7% and ∼5.7% of all compound pairs meeting the similarity criteria. Hence, in light of these findings, the formation of well-defined activity cliffs is a relatively rare event. Moreover, the potency range distribution of activity cliffs was analyzed in detail, revealing that most activity cliffs were formed between compounds with micro- and nanomolar potency, consistent with the global distribution of potency data. On the basis of our analysis, we propose a general definition of activity cliffs for data mining.

MMP-Cliffs: systematic identification of activity cliffs on the basis of matched molecular pairs.

Activity cliffs are generally defined as pairs of structurally similar compounds having large differences in potency. The analysis of activity cliffs is of general interest because structure-activity relationship (SAR) determinants can often be deduced from them. Critical questions for the study of activity cliffs include how similar compounds should be to qualify as cliff partners, how similarity should be assessed, and how large potency differences between participating compounds should be. Thus far, activity cliffs have mostly been defined on the basis of calculated Tanimoto similarity values using structural descriptors, especially 2D fingerprints. As any theoretical assessment of molecular similarity, this approach has its limitations. For example, calculated Tanimoto similarities might often be difficult to reconcile and interpret from a chemical perspective, a point of critique frequently raised in medicinal chemistry. Herein, we have explored activity cliffs by considering well-defined substructure replacements instead of calculated similarity values. For this purpose, the matched molecular pair (MMP) formalism has been applied. MMPs were systematically derived from public domain compounds, and activity cliffs were extracted from them, termed MMP-cliffs. The frequency of cliff formation was determined for compounds active against different targets, MMP-cliffs were analyzed in detail, and re-evaluated on the basis of Tanimoto similarity. In many instances, chemically intuitive activity cliffs were only detected on the basis of MMPs, but not Tanimoto similarity.

A homogeneous fluorescence resonance energy transfer system for monitoring the activation of a protein switch in real time.

A homogeneous fluorescence resonance energy transfer (FRET) system for the real-time monitoring of exchange factor-catalyzed activation of a ras-like small GTPase is described. The underlying design is based on supramolecular template effects exerted by protein-protein interactions between the GTPase adenosine diphosphate ribosylation factor (ARF) and its effector protein GGA3. The GTPase is activated when bound to guanosine triphosphate (GTP) and switched off in its guanosine diphosphate (GDP)-bound state. Both states are accompanied by severe conformational changes that are recognized by GGA3, which only binds the GTPase "on" state. GDP-to-GTP exchange, i.e., GTPase activation, is catalyzed by the guanine nucleotide exchange factor cytohesin-2. When GGA3 and the GTPase ARF1 are labeled with thoroughly selected FRET probes, with simultaneous recording of the fluorescence of an internal tryptophan residue in ARF1, the conformational changes during the activation of the GTPase can be monitored in real time. We applied the FRET system to a multiplex format that allows the simultaneous identification and distinction of small-molecule inhibitors that interfere with the cytohesin-catalyzed ARF1 activation and/or with the interaction between activated ARF1-GTP and GGA3. By screening a library of potential cytohesin inhibitors, predicted by in silico modeling, we identified new inhibitors for the cytohesin-catalyzed GDP/GTP exchange on ARF1 and verified their increased potency in a cell proliferation assay.

Assessing the confidence level of public domain compound activity data and the impact of alternative potency measurements on SAR analysis.

Publicly available compound activity data have been analyzed to distinguish between compounds for which single or multiple potency measurements were available and gain insight into data confidence levels. Different potency measurements with defined end points and alternative ways to represent multiple potency values for active compounds have been evaluated in the context of SAR analysis. Approximately 78% of all compounds with multiple potency measurements were found to represent high-confidence data, which corresponded to ∼10% of all activity data. The use of different types of potency measurements and alternative representations of multiple potency values changed the SAR information content of compound data sets and resulted in different activity cliff distributions. Thus, the types of activity measurements that were available and how they were used substantially impacted SAR analysis. Compounds with multiple K(i) measurements provided the most reliable basis for SAR exploration.

Molecular mechanism-based network-like similarity graphs reveal relationships between different types of receptor ligands and structural changes that determine agonistic, inverse-agonistic, and antagonistic effects.

Receptor ligands might act as agonists, partial agonists, inverse agonists, or antagonists and it is often difficult to understand structural modifications that alter the mechanism of action. In order to compare ligands that are active against a given receptor but have different mechanisms of action, we have designed molecular networks that mirror similarity relationships and incorporate both mechanism of action information and mechanism-specific SAR features. These network representations make it possible to systematically evaluate relationships between different types of receptor ligands and identify communities of structurally very similar ligands with different mechanisms. From a series of such ligands, structural modifications can often be deduced that lead to "mechanism hops".

Development of a method to consistently quantify the structural distance between scaffolds and to assess scaffold hopping potential.

We introduce a method to determine a structural distance between any pair of molecular scaffolds. The development of this approach was motivated by the need to accurately evaluate scaffold hopping studies in virtual screening and medicinal chemistry and assess the degree of difficulty involved in facilitating a transition from one structure to another. In order to consistently derive structural distances, scaffolds of different composition and topology are subjected to molecular editing procedures that abstract from original scaffolds in a defined manner until compositional and topological equivalence can be established. Pairs of corresponding scaffold representations are transformed into one-dimensional atom sequences that are aligned using approaches adapted from biological sequence comparison. From best scoring atom sequence alignments, interscaffold distances are derived. The algorithm is evaluated at different levels including the analysis of a series of model scaffolds with defined chemical changes, a scaffold library, and scaffolds from reference compounds and hits of successful virtual screening applications. It is demonstrated that chemically intuitive scaffold distances are obtained for pairs of scaffolds with varying composition and topology. Distance threshold values for close and remote structural relationships between scaffolds are also determined. The methodology is made publicly available in order to provide a basis for a consistent assessment of scaffold hopping ability and to aid in the evaluation and comparison of virtual screening methods.