Dagmar Stumpfe

Exploring activity cliffs in medicinal chemistry.

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Molecular Similarity in Medicinal Chemistry

Similarity is a subjective and multifaceted concept, regardless of whether compounds or any other objects are considered. Despite its intrinsically subjective nature, attempts to quantify the similarity of compounds have a long history in chemical informatics and drug discovery. Many computational methods employ similarity measures to identify new compounds for pharmaceutical research. However, chemoinformaticians and medicinal chemists typically perceive similarity in different ways. Similarity methods and numerical readouts of similarity calculations are probably among the most misunderstood computational approaches in medicinal chemistry. Herein, we evaluate different similarity concepts, highlight key aspects of molecular similarity analysis, and address some potential misunderstandings. In addition, a number of practical aspects concerning similarity calculations are discussed.

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.

Recent Progress in Understanding Activity Cliffs and Their Utility in Medicinal Chemistry

The activity cliff concept is of high relevance for medicinal chemistry. Recent studies are discussed that have further refined our understanding of activity cliffs and suggested different ways of exploiting activity cliff information. These include alternative approaches to define and classify activity cliffs in two and three dimensions, data mining investigations to systematically detect all possible activity cliffs, the introduction of computational methods to predict activity cliffs, and studies designed to explore activity cliff progression in medicinal chemistry. The discussion of these studies is complemented with new findings revealing the frequency of activity cliff formation when different molecular representations are used and the distribution of activity cliffs across different targets. Taken together, the results have a number of implications for the practice of medicinal chemistry.

Scaffold hopping using two-dimensional fingerprints: true potential, black magic, or a hopeless endeavor? Guidelines for virtual screening.

The scaffold hopping potential of popular 2D fingerprints has been thoroughly investigated. We have found that these types of fingerprints have at least limited scaffold hopping ability including early enrichment of small numbers of active scaffolds at high database ranks. However, it has not been possible to derive Tanimoto coefficient value ranges for individual fingerprints that are generally preferred for scaffold hopping. For selected fingerprints, similarity threshold values have been identified that yield small database selection sets having a high probability to contain a few active scaffolds. Furthermore, essentially all tested fingerprints have shown the ability to enrich scaffold hops in approximately 1% of a screening database. For the test cases reported herein, selecting 0.5-1% of the screening database yields approximately 25% of the available scaffolds. On the basis of our findings, practical guidelines for virtual screening using different types of 2D fingerprints have been formulated.

Similarity searching: Similarity searching

Similarity searching is one of the traditional and most widely applied approaches in chemical and pharmaceutical research to select compounds with desired properties from databases. The computational efficiency of many (but not all) similarity search techniques has further increased their popularity as compound databases began to rapidly grow in size. Different methods have been developed for small molecule similarity searching. However, foundations and intrinsic limitations of similarity searching are often not well understood, although a number of similarity methods are rather simplistic. Regardless of methodological details, all similarity search approaches depend on how molecular similarity is evaluated and quantified. In its essence, molecular similarity is a subjective concept and much dependent on how we represent and view molecular structures. Moreover, trying to understand the relationship between molecular similarity, however assessed, and structure‐dependent properties including, first and foremost, biological activity continues to be a challenging problem. Consequently, although similarity searching usually provides a quantitative readout and a ranking of compounds relative to chosen reference molecules, predicting structure–activity relationships on the basis of calculated similarity values often involves subjective criteria and chemical intuition. Thus, similarity searching is still far from being a routine application in database mining. In this review, we first discuss important principles underlying similarity searching, describe its tasks, and introduce major categories of search methods. Then, we focus on molecular fingerprints, the design and application of which can be regarded as a paradigm for the similarity search field.

Lessons Learned from Molecular Scaffold Analysis

The scaffold concept is one of the most frequently applied concepts in medicinal chemistry and virtual screening. The term scaffold is used to describe molecular core structures that are utilized in drug design or detected in virtual screening and, in addition, building blocks for synthetic efforts. For a series of analogs, a scaffold might be derived by determining their maximum common substructure, but there are many other ways to define scaffolds (vide infra). Unfortunately, in chemoinformatics, the scaffold concept is often applied in a rather subjective manner, without adhering to clear, formal, and consistent definitions. For scaffold hopping, i.e., the identification of different scaffolds with similar activity that represents the “holy grail” of virtual screening, the frequent lack of formal consistency presents a substantial problem and makes it often impossible to compare different studies and methods. In fact, the absence of generally accepted evaluation standards for benchmarking and the inconsistency in assessing scaffold hopping analyses currently are major roadblocks for the further development of the virtual screening field. To further complicate matters, the terms scaffolds, substructures, and fragments are often used to refer to similar or the same structures. Substructures and fragments are rather general designations and are applied to describe small or large structural moieties, scaffolds, parts of scaffolds, or R-groups. Moreover, many different substructures are utilized in drug design applications and different molecular fragmentation schemes have been introduced. 8 These fragmentation methods include knowledge-based approaches such as the generation of fragment dictionaries to flag reactive and toxic compounds or predict ADME properties as well as systematic fragmentation schemes that are based on synthetic or retrosynthetic criteria. Such fragmentation and fragment organization approaches have also provided a basis for the design of fragment libraries in the context of fragment-based drug discovery. 11 Also, even random fragmentation approaches have been introduced to generate structural signatures of compounds with certain biological activities. In addition to knowledge-based and synthetically oriented fragmentation methods, fragments can also be systematically derived on the basis of a defined molecular hierarchy and such approaches have become particularly relevant for scaffold generation and analysis. Regardless of how scaffolds are ultimately rationalized, general aims of scaffold analysis include the assessment of structural diversity of small molecules, the generation of structural classes and structural organization schemes, and the evaluation of biological activities or other molecular properties that are associated with different structural motifs. In this Perspective, we largely, but not exclusively, focus on studies that have analyzed scaffold distributions in selected compound data sets (such as drugs or screening libraries) or in currently available bioactive compounds. A number of these investigations have explored different types of relationships between scaffolds and the biological activities of compounds they represent.

Ligand Prediction from Protein Sequence and Small Molecule Information Using Support Vector Machines and Fingerprint Descriptors

Support vector machine (SVM) database search strategies are presented that aim at the identification of small molecule ligands for targets for which no ligand information is currently available. In pharmaceutical research and chemical biology, this situation is faced, for example, when studying orphan targets or newly identified members of protein families. To investigate methods for de novo ligand identification in the absence of known three-dimensional target structures or active molecules, we have focused on combining sequence and ligand information for closely and distantly related proteins. To provide a basis for these investigations, a set of 11 protease targets from different families was assembled together with more than 2000 inhibitors directed against individual proteases. We have compared SVM approaches that combine protein sequence and ligand information in different ways and utilize 2D fingerprints as ligand descriptors. These methodologies were applied to search for inhibitors of individual proteases not taken into account during learning. A target sequence-ligand kernel and, in particular, a linear combination of multiple target-directed SVMs consistently identified inhibitors with high accuracy including test cases where homology-based similarity searching using data fusion and conventional SVM ranking nearly or completely failed. The SVM linear combination and target-ligand kernel methods described herein are intuitive and straightforward to adopt for ligand prediction against other targets.

Virtual compound screening in drug discovery

Virtual screening (VS) methods are applied in both academia and drug discovery, and can be divided into ligand- and target structure-based approaches. The VS field is still evolving and is characterized by scientific heterogeneity. The value of virtual compound screening for drug discovery is often debated, in particular, given the large investments made in experimental high-throughput screening technologies. The current state-of-the-art in the VS field is discussed. Despite its limitations, VS applications have often succeeded in identifying novel hits including first-in-class active compounds and novel chemotypes. VS has its place in pharmaceutical research, but there is still much room for further improvements including method evaluation and drug discovery applications. The potential of VS is currently underutilized because its complementarity to high-throughput screening is not sufficiently exploited. Building close interfaces between computational and experimental screening would further streamline the hit identification process.

Computational Exploration of Molecular Scaffolds in Medicinal Chemistry.

The scaffold concept is widely applied in medicinal chemistry. Scaffolds are mostly used to represent core structures of bioactive compounds. Although the scaffold concept has limitations and is often viewed differently from a chemical and computational perspective, it has provided a basis for systematic investigations of molecular cores and building blocks, going far beyond the consideration of individual compound series. Over the past 2 decades, alternative scaffold definitions and organization schemes have been introduced and scaffolds have been studied in a variety of ways and increasingly on a large scale. Major applications of the scaffold concept include the generation of molecular hierarchies, structural classification, association of scaffolds with biological activities, and activity prediction. This contribution discusses computational approaches for scaffold generation and analysis, with emphasis on recent developments impacting medicinal chemistry. A variety of scaffold-based studies are discussed, and a perspective on scaffold methods is provided.