Abstract
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.
Highlights
We have further extended the assessment of R-groups on the basis of analogue series (ASs) [8] by generalizing the approach and identified all R-groups that currently occur in ASs
To generalize the approach and identify all possible R-groups, beyond those defined by retrosynthetic rules, we introduced an algorithmic compound–core relationship (CCR) variant in which retrosynthetic fragmentation was replaced by random fragmentation of single exocyclic bonds in compounds
We analyzed the frequency with which individual R-groups occurred across all replacements in the global network and found that the top three R-groups in Figure 2 including the H atom, methyl group and phenyl ring were for the most part involved in replacements of other frequently used R-groups
Summary
We analyzed the frequency with which individual R-groups occurred across all replacements in the global network and found that the top three R-groups in Figure 2 including the H atom, methyl group and phenyl ring were for the most part involved in replacements of other frequently used R-groups. Compared with the global or final network, this small local network has a dramatically reduced edge density (and is used for representation purposes only) It provides a view of replacements among most frequent R-groups. Data We provide a searchable repository containing the 500 most frequently used R-groups from the final network and their 5 × 2 first and second layer replacement hierarchies. For an R-group of interest, preferred replacements across bioactive compounds can be
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