Exploration of the Chemical Space of DNA‐encoded Libraries

Pikalyova, Regina; Zabolotna, Yuliana; Volochnyuk, Dmitriy M.; Horvath, Dragos; Gilles, Marcous; Varnek, Alexandre

doi:10.1002/minf.202100289

Cited by 19 publications

(36 citation statements)

References 29 publications

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“…The ChEMBL dataset (version 28) was used here as a reference library. It was downloaded and standardized in our previous work 10 according to the approach implemented on the Virtual Screening Web Server of the Laboratory of Chemoinformatics at the University of Strasbourg, using the ChemAxon Standardizer 11 . This procedure included dearomatization and final aromatization (heterocycles like pyridone are not aromatized), dealkalization, conversion to canonical SMILES, removal of salts and mixtures, neutralization of all species, except nitrogen(IV), generation of the major tautomer according to ChemAxon.…”

Section: Data 1 Chemblmentioning

confidence: 99%

“…1M representative subsets for all 2497 DELs were generated in our previous work 10 with the help of the eDesigner tool 12 . This was done using commercially available BBs from eMolecules and Enamine that satisfy the Ro2 13 and eDesigner built-in DNA-compatibility filters.…”

Section: Dna-encoded Librariesmentioning

confidence: 99%

“…Both coverage and weighted coverage scores were used in our previous work for the analysis and comparison of virtual DEL collections to the ChEMBL chemical space 10 .…”

Section: Defining Similarity Relationships Between Libraries In the Clsmentioning

confidence: 99%

“…In the present article, these vectors were used to encode previously generated 2.5K DELs 10 . The ability of each of the vectors to accurately represent and identify DELs closest to the reference library was evaluated and compared to previous results obtained using responsibility patterns (RPs) 10 . Based on the values from each of the introduced library vectors (nCRV, lCRV, and pCRV)…”

Section: Introductionmentioning

confidence: 99%

“…In our previous study 10 , representative sets of each of the 2,5K DELs were generated using random sampling of BBs in eDesigner 12 tool, and not the full libraries. Such a set or sublibrary by design should be very similar to the entire DEL and cover virtually all of its chemical space.…”

mentioning

confidence: 99%

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The chemical library space and its application to DNA-Encoded Libraries

Pikalyova

Zabolotna

Horvath

et al. 2023

Preprint

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The development of DNA-Encoded Library (DEL) technology introduced new challenges for the analysis of chemical libraries. Unlike classical HTS libraries, the DEL composition cannot be modified once synthesized and, therefore, it must be considered as a stand-alone chemoinformatic object represented both as a collection of independent molecules, and yet an individual entity. For the analysis of such collections, the concept of the Chemical Library Space (CLS) where entire libraries become objects is indispensable. In this article, we introduce, analyze and compare four vectorial library representations obtained using Generative Topographic Mapping (GTM), formally defining CLS. Such representations allow effective comparison of libraries, with the ability to tune and chemically interpret the similarity relationships. In particular, property-tuned CLS encodings enable to simultaneously compare libraries with respect to both property and chemotype distribution. We apply the various CLS encodings for the selection problem of DELs that optimally match a reference collection (here ChEMBL28), showing how the choice of the CLS descriptors may help to fine-tune the matching (overlap) criteria. The proposed CLS encodings are a new efficient way for polyvalent analysis of the space of thousands of chemical libraries.

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Section: Data 1 Chemblmentioning

confidence: 99%

Section: Dna-encoded Librariesmentioning

confidence: 99%

“…Both coverage and weighted coverage scores were used in our previous work for the analysis and comparison of virtual DEL collections to the ChEMBL chemical space 10 .…”

Section: Defining Similarity Relationships Between Libraries In the Clsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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The chemical library space and its application to DNA-Encoded Libraries

Pikalyova

Zabolotna

Horvath

et al. 2023

Preprint

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PredictingS. aureusantimicrobial resistance with interpretable genomic space maps

Pikalyova,

Orlov,

Horvath

et al. 2024

Molecular Informatics

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Increasing antimicrobial resistance (AMR) represents a global healthcare threat. To decrease the spread of AMR and associated mortality, methods for rapid selection of optimal antibiotic treatment are urgently needed. Machine learning (ML) models based on genomic data to predict resistant phenotypes can serve as a fast screening tool prior to phenotypic testing. Nonetheless, many existing ML methods lack interpretability. Therefore, we present a methodology for visualization of sequence space and AMR prediction based on the non‐linear dimensionality reduction method – generative topographic mapping (GTM). This approach, applied to AMR data of >5000 S. aureus isolates retrieved from the PATRIC database, yielded GTM models with reasonable accuracy for all drugs (balanced accuracy values ≥0.75). The Generative Topographic Maps (GTMs) represent data in the form of illustrative maps of the genomic space and allow for antibiotic‐wise comparison of resistant phenotypes. The maps were also found to be useful for the analysis of genetic determinants responsible for drug resistance. Overall, the GTM‐based methodology is a useful tool for both the illustrative exploration of the genomic sequence space and AMR prediction.

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The freedom space – a new set of commercially available molecules for hit discovery

Protopopov,

Tararina,

Bonachera

et al. 2024

Molecular Informatics

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The advent of high‐performance virtual screening techniques nowadays allows drug designers to explore ultra‐large sets of candidate compounds in search of molecules predicted to have desired properties. However, the success of such an endeavor heavily relies on the pertinence (drug‐likeness and, foremost, chemical feasibility) of these candidates, or otherwise, virtual screening will return valueless “hits”, by the garbage in/garbage out principle. The huge popularity of the judiciously enumerated Enamine REAL Space is clear proof of the strength of this Big Data trend in drug discovery. Here we describe a new dataset of make‐on‐demand compounds called the Freedom space. It follows the principles of Enamine REAL Space and contains highly feasible molecules (synthesis success rate over 75 percent). However, the scaffold and chemography analysis revealed significant differences to both the REAL and biologically annotated compounds from the ChEMBL database. The Freedom Space is a significant extension of the REAL Space and can be utilized for a more comprehensive exploration of the synthetically feasible chemical space in hit finding and hit‐to‐lead campaigns.

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Exploration of the Chemical Space of DNA‐encoded Libraries

Cited by 19 publications

References 29 publications

The chemical library space and its application to DNA-Encoded Libraries

The chemical library space and its application to DNA-Encoded Libraries

PredictingS. aureusantimicrobial resistance with interpretable genomic space maps

The freedom space – a new set of commercially available molecules for hit discovery

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