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. 2020 Oct 15;23(11):101681.
doi: 10.1016/j.isci.2020.101681. eCollection 2020 Nov 20.

Generating Multibillion Chemical Space of Readily Accessible Screening Compounds

Oleksandr O Grygorenko  1   2 , Dmytro S Radchenko  1   2 , Igor Dziuba  3 , Alexander Chuprina  4 , Kateryna E Gubina  2 , Yurii S Moroz  2   3
Affiliations

Generating Multibillion Chemical Space of Readily Accessible Screening Compounds

Oleksandr O Grygorenko et al. iScience. .

Erratum in

Abstract

An approach to the generation of ultra-large chemical libraries of readily accessible ("REAL") compounds is described. The strategy is based on the use of two- or three-step three-component reaction sequences and available starting materials with pre-validated chemical reactivity. After the preliminary parallel experiments, the methods with at least ∼80% synthesis success rate (such as acylation - deprotection - acylation of monoprotected diamines or amide formation - click reaction with functionalized azides) can be selected and used to generate the target chemical space. It is shown that by using only on the two aforementioned reaction sequences, a nearly 29-billion compound library is easily obtained. According to the predicted physico-chemical descriptor values, the generated chemical space contains large fractions of both drug-like and "beyond rule-of-five" members, whereas the strictest lead-likeness criteria (the so-called Churcher's rules) are met by the lesser part, which still exceeds 22 million.

Keywords: Chemical Compound; Cheminformatics; Computational Chemistry by Subject.

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Conflict of interest statement

The authors declare no competing interests.

Figures

None
Graphical abstract
Figure 1
Figure 1
A General Principle of the REAL Database Generation Using One-Step Two-Component Reactions
Figure 2
Figure 2
An Approach to the Generation of Ultra-large Chemical Space Described in this Work
Scheme 1
Scheme 1
Parallel Reaction Sequences Studied in This Work. See also Tables S1 and S2, Figures S1 and S2.
Figure 3
Figure 3
Examples of Reagents 1–3 Showing Excellent and Poor Efficiency in the Methods Studied (Relative Configurations are Shown)
Figure 4
Figure 4
Examples of Synthons Generated from Reagents 1, 2, 4, 5, 8, and 10 (in the Corresponding SMILES Representations, Uncommon [“Dummy”] Atoms are Used Instead of the Colored Asterisks [∗] to Denote Different Types of the Variation Points)
Scheme 2
Scheme 2
Virtual Coupling of the Synthons Shown in Figure 4 (the Variation Points [∗] Are Connected according to Their Types)
Figure 5
Figure 5
The Workflow of the Multibillion Chemical Space Generation
Figure 6
Figure 6
Distribution of Physico-Chemical Descriptors Predicted for the Generated Chemical Space and Approved Drugs See also Table S3.
Figure 7
Figure 7
Relationship between the Size of the Generated Databases and the Size of the Synthon Subsets Obtained by random selections from the initial synthon set for Method A; average from three independent selections; see also Table S4.
Figure 8
Figure 8
Properties of the Generated Chemical Space as a Function of the Molecular Weight Cut-offs Applied to the Initial Synthon Sets for Method A (A and B) (A) The size of the generated databases. (B) Distribution of physico-chemical descriptors (MW and sLogP) for the generated chemical space. See also Table S5.
See this image and copyright information in PMC

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