Global Assessment of Substituents on the Basis of Analogue Series

Palladium-Catalyzed PIDA-Mediated δ-C(sp3 )-H Acetoxylation of Amino Acid Derivatives: Overriding Competitive Intramolecular Amination

The selective δ-C(sp3 )-H acetoxylation of N-(SO2 Py)-protected amino acid derivatives has been accomplished by using palladium-catalysis and PhI(OAc)2 (PIDA) as both terminal oxidant and acetoxy source. The distinct structural and electronic features of the SO2 Py compared to more traditional carbonyl-based directing groups is essential to override the otherwise more favourable competitive intramolecular C-H amination. The δ-site selectivity predominates over traditionally more favorable 5-membered cyclopalladation at competitive γ-CH2 . Experimental and DFT mechanistic studies provide important insights about the mechanism and the underlying factors controlling the chemo- and regioselectivity.

Introducing a Chemically Intuitive Core-Substituent Fingerprint Designed to Explore Structural Requirements for Effective Similarity Searching and Machine Learning

Fingerprint (FP) representations of chemical structure continue to be one of the most widely used types of molecular descriptors in chemoinformatics and computational medicinal chemistry. One often distinguishes between two- and three-dimensional (2D and 3D) FPs depending on whether they are derived from molecular graphs or conformations, respectively. Primary application areas for FPs include similarity searching and compound classification via machine learning, especially for hit identification. For these applications, 2D FPs are particularly popular, given their robustness and for the most part comparable (or better) performance to 3D FPs. While a variety of FP prototypes has been designed and evaluated during earlier times of chemoinformatics research, new developments have been rare over the past decade. At least in part, this has been due to the situation that topological (atom environment) FPs derived from molecular graphs have evolved as a gold standard in the field. We were interested in exploring the question of whether the amount of structural information captured by state-of-the-art 2D FPs is indeed required for effective similarity searching and compound classification or whether accounting for fewer structural features might be sufficient. Therefore, pursuing a "structural minimalist" approach, we designed and implemented a new 2D FP based upon ring and substituent fragments obtained by systematically decomposing large numbers of compounds from medicinal chemistry. The resulting FP termed core-substituent FP (CSFP) captures much smaller numbers of structural features than state-of-the-art 2D FPs. However, CSFP achieves high performance in similarity searching and machine learning, demonstrating that less structural information is required for establishing molecular similarity relationships than is often believed. Given its high performance and chemical tangibility, CSFP is also relevant for practical applications in medicinal chemistry.

Systematic mapping of R-group space enables the generation of an R-group replacement system for medicinal chemistry

Selection of R-groups (substituents, functional groups) is of critical importance for the generation of analogues during hit-to-lead and lead optimization. In the practice of medicinal chemistry, R-group selection is mostly driven by chemical experience and intuition taking synthetic criteria into account. However, systematic analyses of substituents are currently rare. In this work, we have computationally isolated R-groups from more than 17,000 analog series comprising ∼315,000 bioactive compounds. From more than 50,000 unique substituents, frequently used R-groups were identified. For these R-groups, preferred replacements over more than 60,000 individual substitution sites were identified with the aid of a network data structure. These data provided the basis for the generation of a searchable R-group replacement system for medicinal chemistry containing replacement hierarchies for frequently used R-groups, which is made freely available as the central component of our study.

Automatic Identification of Analogue Series from Large Compound Data Sets: Methods and Applications

Analogue series play a key role in drug discovery. They arise naturally in lead optimization efforts where analogues are explored based on one or a few core structures. However, it is much harder to accurately identify and extract pairs or series of analogue molecules in large compound databases with no predefined core structures. This methodological review outlines the most common and recent methodological developments to automatically identify analogue series in large libraries. Initial approaches focused on using predefined rules to extract scaffold structures, such as the popular Bemis-Murcko scaffold. Later on, the matched molecular pair concept led to efficient algorithms to identify similar compounds sharing a common core structure by exploring many putative scaffolds for each compound. Further developments of these ideas yielded, on the one hand, approaches for hierarchical scaffold decomposition and, on the other hand, algorithms for the extraction of analogue series based on single-site modifications (so-called matched molecular series) by exploring potential scaffold structures based on systematic molecule fragmentation. Eventually, further development of these approaches resulted in methods for extracting analogue series defined by a single core structure with several substitution sites that allow convenient representations, such as R-group tables. These methods enable the efficient analysis of large data sets with hundreds of thousands or even millions of compounds and have spawned many related methodological developments.

R-group replacement database for medicinal chemistry

Aim:

Generation of an R-group replacement system for compound optimization in medicinal chemistry.

Materials & methods:

From bioactive compounds, analogue series (ASs) were systematically extracted and from these ASs, all R-groups were isolated and further analyzed.

Exemplary results & data:

From more than 17,000 ASs, more than 50,000 unique R-groups were isolated. For the 500 most frequently used R-groups, preferred replacements were identified and organized in hierarchies. All original data and an R-group replacement database are made available in an open access deposition.

Limitations & next steps:

The searchable database has no limitations and can easily be modified using the source data we provide. The next step will be applying this R-group resource in practical medicinal chemistry projects as decision support.

To optimize the biological activity of small molecules in medicinal chemistry, series of analogues are generated by introducing substituents (R-groups) at different positions. The choice of R-groups largely depends on the experience of individual chemists. We have computationally isolated a large number of R-groups from currently available analogue series. Frequently used R-groups and their preferred replacements were identified and organized in a searchable database for medicinal chemists to aid in R-group selection.