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. 2023 Mar 16;14(4):466-472.
doi: 10.1021/acsmedchemlett.3c00021. eCollection 2023 Apr 13.

Relevance of the Trillion-Sized Chemical Space "eXplore" as a Source for Drug Discovery

Alexander Neumann  1 , Lester Marrison  2 , Raphael Klein  1
Affiliations

Relevance of the Trillion-Sized Chemical Space "eXplore" as a Source for Drug Discovery

Alexander Neumann et al. ACS Med Chem Lett. .

Abstract

Within the past two decades, virtual combinatorial compound collections, so-called chemical spaces, became an important molecule source for pharmaceutical research all over the world. The emergence of compound vendor chemical spaces with rapidly growing numbers of molecules raises questions about their application suitability and the quality of the content. Here, we examine the composition of the recently published and, so far, biggest chemical space, "eXplore", which comprises approximately 2.8 trillion virtual product molecules. The utility of eXplore to retrieve interesting chemistry around approved drugs and common Bemis Murcko scaffolds has been assessed with several methods (FTrees, SpaceLight, SpaceMACS). Further, the overlap between several vendor chemical spaces and a physicochemical property distribution analysis has been performed. Despite the straightforward chemical reactions underlying its setup, eXplore is demonstrated to provide relevant and, most importantly, easily accessible molecules for drug discovery campaigns.

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Conflict of interest statement

The authors declare the following competing financial interest(s): A.N. and R.K. are employees of BioSolveIT, a company offering paid software to screen the eXplore space. L.M. is an employee of eMolecules that offers building blocks and screening compounds described in this work.

Figures

Figure 1
Figure 1
(A) FTrees, (B) SpaceLight (fCSFP3), and (C) SpaceMACS similarity search results for close analogs of 2793 FDA-approved drugs within the eXplore space. The FTrees search was performed with different intervals, given the nature of the fuzziness of the algorithm that allows soft matching of molecular features, and is therefore represented with a grain pattern.
Figure 2
Figure 2
Closest analogs to celecoxib retrieved by FTrees, SpaceMACS, and SpaceLight. For each molecule, the corresponding similarity scores of the respective other algorithms were calculated.
Figure 3
Figure 3
Examples of eXplore’s highest ranked search results for drugs without an identical match. The similarity score of each method is provided below the structure.
Figure 4
Figure 4
Pie chart of the pairwise MCS similarity between 2.4 million Bemis Murcko scaffolds and the most similar compounds from eXplore.
Figure 5
Figure 5
Overlap of Bemis Murcko scaffolds found with a MCS similarity score of 1.
Figure 6
Figure 6
Physicochemical property distributions of eXplore.
See this image and copyright information in PMC

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