Finding Constellations in Chemical Space Through Core Analysis

Herein we introduce the constellation plots as a general approach that merges different and complementary molecular representations to enhance the information contained in a visual representation and analysis of chemical space. The method is based on a combination of a sub-structure based representation and classification of compounds with a "classical" coordinate-based representation of chemical space. A distinctive outcome of the method is that organizing the compounds in analog series leads to the formation of groups of molecules, aka "constellations" in chemical space. The novel approach is general and can be used to rapidly identify, for instance, insightful and "bright" Structure-Activity Relationships (StARs) in chemical space that are easy to interpret. This kind of analysis is expected to be especially useful for lead identification in large datasets of unannotated molecules, such as those obtained through high-throughput screening. We demonstrate the application of the method using two datasets of focused inhibitors designed against DNMTs and AKT1.

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.

Conclusion:

A new scaffold definition is introduced and a computational methodology developed to systematically identify such scaffolds, yielding a large freely available scaffold knowledge base.

In medicinal chemistry and drug design, so-called scaffolds are used to represent core structures of bioactive compounds. Over the past 20 years, a formal scaffold definition has predominantly been applied that considers molecules to consist of ring structures, which represent the scaffold, and chemical groups attached to rings. Herein, we introduce a new scaffold concept, which takes compound series and chemical reaction information into account.

We introduce a new scaffold concept for medicinal chemistry. The figure illustrates how an ‘analog series-based scaffold’ is obtained from a series of structural analogs.

Assessing Scaffold Diversity of Kinase Inhibitors Using Alternative Scaffold Concepts and Estimating the Scaffold Hopping Potential for Different Kinases

Publicly available kinase inhibitors provide a large source of information for structure-activity relationship analysis and kinase drug design. In this study, publicly available inhibitors of the human kinome were collected and analog series formed by kinase inhibitors systematically identified. Then, alternative scaffold concepts were applied to assess diversity and promiscuity of kinase inhibitors. Over the past two years, the number of publicly available kinase inhibitors with high-confidence activity data more than doubled, but coverage of the human kinome only slightly increased. Approximately 70% of current kinase inhibitors belonged to analog series. However, the detectable degree of promiscuity among these kinase inhibitors remained low. Approximately 76% of all inhibitors were only annotated with a single kinase, compared to ~70% two years ago. For many kinases, the assessment of scaffold diversity among their inhibitors and the distribution of differently defined scaffolds over analog series made it possible to assess scaffold hopping potential. Our analysis revealed that the consideration of conventional compound-based scaffolds most likely leads to an overestimation of scaffold hopping frequency, at least for compounds forming analog series.

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.

‘Scaffolds’ represent core structures of small molecules. A series of structural analogs contains the same scaffold (core) and different functional groups attached to the core. Scaffolds are often used to organize active compounds into different structural families. They are also used to aid in compound design in medicinal chemistry. Scaffolds can be extracted computationally from single molecules or, following a recently introduced method, from analog series. Herein, an extension of the series-based scaffold methodology is presented to further increase the number of informative scaffolds that can be obtained and represent as many analog series and compounds as possible.

The SAR Matrix Method and an Artificially Intelligent Variant for the Identification and Structural Organization of Analog Series, SAR Analysis, and Compound Design

The SAR Matrix (SARM) approach was originally conceived for the systematic identification of analog series, their structural organization, and graphical structure-activity relationship (SAR) analysis. For structurally related series, SARMs also produce virtual candidate compounds. Hence, SARM represents a unique computational approach establishing a direct link between SAR visualization and compound design. The SARM data structure is reminiscent of R-group tables and hence easily accessible from a chemical perspective, although the underlying algorithmic basis is complex. The SARM concept has been extended in different ways to further increase its analytical and design capacity. While the efforts were largely driven from a research perspective, they have also increased the utility for practical applications. Among others, extensions include approaches for SARM-based compound activity prediction, the generation of a large SARM database for analog searching, and the design of a deep learning architecture for advanced analog design taking chemical space information for target families into account. Herein, the SARM approach and its extensions are discussed within their scientific context.

Collection of analog series-based scaffolds from public compound sources

Aim:

Providing a large and freely available in silico collection of analog series-based (ASB) scaffolds for computational design and medicinal chemistry applications.

Methodology:

Using a recently introduced computational method, ASB scaffolds with single and multiple substitution sites were systematically isolated from publicly available active compounds.

Data:

A total of 23,791 unique ASB scaffolds are made available in an organized and machine-readable form as an open access deposition. For each ASB scaffold, the number of analogs it represents is also provided.

Next steps:

The freely available collection of ASB scaffolds will be further analyzed to explore different types of scaffold relationships. The ASB scaffold collection will periodically be updated.

The scaffold concept is popular in medicinal chemistry, drug design and chemical informatics. Scaffolds represent core structures of small molecules. They can be defined and computationally generated in different ways. Scaffolds extracted from individual compounds are distinguished from scaffolds representing series of closely related compounds, so-called analogs. Herein, a large collection of analog series-based scaffolds is reported. These scaffolds are made freely available to aid in medicinal chemistry and drug design applications.

Tubulin Inhibitors: A Chemoinformatic Analysis Using Cell-Based Data

Inhibiting the tubulin-microtubules (Tub-Mts) system is a classic and rational approach for treating different types of cancers. A large amount of data on inhibitors in the clinic supports Tub-Mts as a validated target. However, most of the inhibitors reported thus far have been developed around common chemical scaffolds covering a narrow region of the chemical space with limited innovation. This manuscript aims to discuss the first activity landscape and scaffold content analysis of an assembled and curated cell-based database of 851 Tub-Mts inhibitors with reported activity against five cancer cell lines and the Tub-Mts system. The structure-bioactivity relationships of the Tub-Mts system inhibitors were further explored using constellations plots. This recently developed methodology enables the rapid but quantitative assessment of analog series enriched with active compounds. The constellations plots identified promising analog series with high average biological activity that could be the starting points of new and more potent Tub-Mts inhibitors.

From SAR Diagnostics to Compound Design: Development Chronology of the Compound Optimization Monitor (COMO) Method

In medicinal chemistry, compound optimization largely depends on chemical knowledge, experience, and intuition, and progress in hit-to-lead and lead optimization projects is difficult to estimate. Accordingly, approaches are sought after that aid in assessing the odds of success with an optimization project and making decisions whether to continue or discontinue work on an analog series at a given stage. However, currently there are only very few approaches available that are capable of providing decision support. We introduce a computational methodology designed to combine the assessment of chemical saturation of analog series and structure-activity relationship (SAR) progression. The current endpoint of these development efforts, the compound optimization monitor (COMO), further extends lead optimization diagnostics to compound design and activity prediction. Hence, COMO plays dual role in supporting lead optimization campaigns.

Chemoinformatic Characterization of Synthetic Screening Libraries Focused on Epigenetic Targets

The importance of epigenetic drug and probe discovery is on the rise. This is not only paramount to identify and develop therapeutic treatments associated with epigenetic processes but also to understand the underlying epigenetic mechanisms involved in biological processes. To this end, chemical vendors have been developing synthetic compound libraries focused on epigenetic targets to increase the probabilities of identifying promising starting points for drug or probe candidates. However, the chemical contents of these data sets, the distribution of their physicochemical properties, and diversity remain unknown. To fill this gap and make this information available to the scientific community, we report a comprehensive analysis of eleven libraries focused on epigenetic targets containing more than 50,000 compounds. We used well-validated chemoinformatics approaches to characterize these sets, including novel methods such as automated detection of analog series and visual representations of the chemical space based on Constellation Plots and Chemical Library Networks. This work will guide the efforts of experimental groups working on high-throughput and medium-throughput screening of epigenetic-focused libraries. The outcome of this work can also be used as a reference to design and describe novel focused epigenetic libraries.

AnalogExplorer2 - Stereochemistry sensitive graphical analysis of large analog series

AnalogExplorer is a computational methodology for the extraction and organization of series of structural analogs from compound data sets and their graphical analysis. The method is suitable for the analysis of large analog series originating from lead optimization programs. Herein we report AnalogExplorer2 designed to explicitly take stereochemical information during graphical analysis into account and describe a freely available deposition of the original AnalogExplorer program, AnalogExplorer2, and exemplary compound sets to illustrate their use.

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.

In drug discovery, compounds are tested in high-throughput screening assays for desired biological activities. Unfortunately, some test compounds that are not specifically active give false-positive (artificial) activity signals in assays, which are difficult to recognize. However, other compounds can bind specifically to multiple targets and hence give true activity signals in multiple assays. To help distinguish between artifacts and true activities, we have systematically identified series of analogs (i.e., structurally closely related compounds) that are active with unusually high frequency across biological assays. Herein, we describe these data and make them freely available for follow-up analysis.