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Article ; Online: Predicting S. aureus antimicrobial resistance with interpretable genomic space maps.

Pikalyova, Karina / Orlov, Alexey / Horvath, Dragos / Marcou, Gilles / Varnek, Alexandre

2024 , Page(s) e202300263

Abstract: 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 ... ...

Abstract	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.
Language	English
Publishing date	2024-02-22
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2537668-8
ISSN	1868-1751 ; 1868-1743
ISSN (online)	1868-1751
ISSN	1868-1743
DOI	10.1002/minf.202300263
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Meta-GTM: Visualization and Analysis of the Chemical Library Space.

Pikalyova, Regina / Zabolotna, Yuliana / Horvath, Dragos / Marcou, Gilles / Varnek, Alexandre

Journal of chemical information and modeling

2023 Volume 63, Issue 17, Page(s) 5571–5582

Abstract: In chemical library analysis, it may be useful to describe libraries as individual items rather than collections of compounds. This is particularly true for ultra-large noncherry-pickable compound mixtures, such as DNA-encoded libraries (DELs). In this ... ...

Abstract	In chemical library analysis, it may be useful to describe libraries as individual items rather than collections of compounds. This is particularly true for ultra-large noncherry-pickable compound mixtures, such as DNA-encoded libraries (DELs). In this sense, the chemical library space (CLS) is useful for the management of a portfolio of libraries, just like chemical space (CS) helps manage a portfolio of molecules. Several possible CLSs were previously defined using vectorial library representations obtained from generative topographic mapping (GTM). Given the steadily growing number of DEL designs, the CLS becomes "crowded" and requires analysis tools beyond pairwise library comparison. Therefore, herein, we investigate the cartography of CLS on meta-(μ)GTMs─"meta" to remind that these are maps of the CLS, itself based on responsibility vectors issued by regular CS GTMs. 2,5 K DELs and ChEMBL (reference) were projected on the μGTM, producing landscapes of library-specific properties. These describe both interlibrary similarity and intrinsic library characteristics in the same view, herewith facilitating the selection of the best project-specific libraries.
MeSH term(s)	Gene Library ; Small Molecule Libraries
Chemical Substances	Small Molecule Libraries
Language	English
Publishing date	2023-08-21
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	190019-5
ISSN	1549-960X ; 0095-2338
ISSN (online)	1549-960X
ISSN	0095-2338
DOI	10.1021/acs.jcim.3c00719
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Article ; Online: Chemical Library Space: Definition and DNA-Encoded Library Comparison Study Case.

Pikalyova, Regina / Zabolotna, Yuliana / Horvath, Dragos / Marcou, Gilles / Varnek, Alexandre

Journal of chemical information and modeling

2023 Volume 63, Issue 13, Page(s) 4042–4055

Abstract: The development of DNA-encoded library (DEL) technology introduced new challenges for the analysis of chemical libraries. It is often useful to consider a chemical library as a stand-alone chemoinformatic object─represented both as a collection of ... ...

Abstract	The development of DNA-encoded library (DEL) technology introduced new challenges for the analysis of chemical libraries. It is often useful to consider a chemical library as a stand-alone chemoinformatic object─represented both as a collection of independent molecules, and yet an individual entity─in particular, when they are inseparable mixtures, like DELs. Herein, we introduce the concept of chemical library space (CLS), in which resident items are individual chemical libraries. We define and compare four vectorial library representations obtained using generative topographic mapping. These allow for an 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 distributions. 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. Hence, the proposed CLS may represent a new efficient way for polyvalent analysis of thousands of chemical libraries. Selection of an easily accessible compound collection for drug discovery, as a substitute for a difficult to produce reference library, can be tuned for either primary or target-focused screening, also considering property distributions of compounds. Alternatively, selection of libraries covering novel regions of the chemical space with respect to a reference compound subspace may serve for library portfolio enrichment.
MeSH term(s)	Small Molecule Libraries/chemistry ; DNA/chemistry ; Gene Library ; Drug Discovery/methods
Chemical Substances	Small Molecule Libraries ; DNA (9007-49-2)
Language	English
Publishing date	2023-06-27
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	190019-5
ISSN	1549-960X ; 0095-2338
ISSN (online)	1549-960X
ISSN	0095-2338
DOI	10.1021/acs.jcim.3c00520
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Article ; Online: French dispatch: GTM-based analysis of the Chimiothèque Nationale Chemical Space.

Oleneva, Polina / Zabolotna, Yuliana / Horvath, Dragos / Marcou, Gilles / Bonachera, Fanny / Varnek, Alexandre

Molecular informatics

2023 Volume 42, Issue 4, Page(s) e2200208

Abstract: In order to analyze the Chimiothèque Nationale (CN) - The French National Compound Library - in the context of screening and biologically relevant compounds, the library was compared with ZINC in-stock collection and ChEMBL. This includes the study of ... ...

Abstract	In order to analyze the Chimiothèque Nationale (CN) - The French National Compound Library - in the context of screening and biologically relevant compounds, the library was compared with ZINC in-stock collection and ChEMBL. This includes the study of chemical space coverage, physicochemical properties and Bemis-Murcko (BM) scaffold populations. More than 5 K CN-unique scaffolds (relative to ZINC and ChEMBL collections) were identified. Generative Topographic Maps (GTMs) accommodating those libraries were generated and used to compare the compound populations. Hierarchical GTM («zooming») was applied to generate an ensemble of maps at various resolution levels, from global overview to precise mapping of individual structures. The respective maps were added to the ChemSpace Atlas website. The analysis of synthetic accessibility in the context of combinatorial chemistry showed that only 29,7 % of CN compounds can be fully synthesized using commercially available building blocks.
MeSH term(s)	Databases, Chemical
Language	English
Publishing date	2023-02-06
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2537668-8
ISSN	1868-1751 ; 1868-1743
ISSN (online)	1868-1751
ISSN	1868-1743
DOI	10.1002/minf.202200208
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Article ; Online: Computational Approaches to the Rational Design of Tubulin-Targeting Agents.

Pérez-Peña, Helena / Abel, Anne-Catherine / Shevelev, Maxim / Prota, Andrea E / Pieraccini, Stefano / Horvath, Dragos

Biomolecules

2023 Volume 13, Issue 2

Abstract: Microtubules are highly dynamic polymers of α,β-tubulin dimers which play an essential role in numerous cellular processes such as cell proliferation and intracellular transport, making them an attractive target for cancer and neurodegeneration research. ...

Abstract	Microtubules are highly dynamic polymers of α,β-tubulin dimers which play an essential role in numerous cellular processes such as cell proliferation and intracellular transport, making them an attractive target for cancer and neurodegeneration research. To date, a large number of known tubulin binders were derived from natural products, while only one was developed by rational structure-based drug design. Several of these tubulin binders show promising in vitro profiles while presenting unacceptable off-target effects when tested in patients. Therefore, there is a continuing demand for the discovery of safer and more efficient tubulin-targeting agents. Since tubulin structural data is readily available, the employment of computer-aided design techniques can be a key element to focus on the relevant chemical space and guide the design process. Due to the high diversity and quantity of structural data available, we compiled here a guide to the accessible tubulin-ligand structures. Furthermore, we review different ligand and structure-based methods recently used for the successful selection and design of new tubulin-targeting agents.
MeSH term(s)	Humans ; Tubulin ; Ligands ; Antineoplastic Agents/chemistry ; Microtubules ; Neoplasms/drug therapy
Chemical Substances	Tubulin ; Ligands ; Antineoplastic Agents
Language	English
Publishing date	2023-02-02
Publishing country	Switzerland
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	2701262-1
ISSN	2218-273X ; 2218-273X
ISSN (online)	2218-273X
ISSN	2218-273X
DOI	10.3390/biom13020285
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Article ; Online: GENERA: A Combined Genetic/Deep-Learning Algorithm for Multiobjective Target-Oriented De Novo Design.

Lamanna, Giuseppe / Delre, Pietro / Marcou, Gilles / Saviano, Michele / Varnek, Alexandre / Horvath, Dragos / Mangiatordi, Giuseppe Felice

Journal of chemical information and modeling

2023 Volume 63, Issue 16, Page(s) 5107–5119

Abstract: This study introduces a new de novo design algorithm ... ...

Abstract	This study introduces a new de novo design algorithm called
MeSH term(s)	Humans ; Deep Learning ; COVID-19 ; Algorithms ; Drug Design
Language	English
Publishing date	2023-08-09
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	190019-5
ISSN	1549-960X ; 0095-2338
ISSN (online)	1549-960X
ISSN	0095-2338
DOI	10.1021/acs.jcim.3c00963
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Article ; Online: Exploration of the Chemical Space of DNA-encoded Libraries.

Pikalyova, Regina / Zabolotna, Yuliana / Volochnyuk, Dmitriy M / Horvath, Dragos / Marcou, Gilles / Varnek, Alexandre

Molecular informatics

2022 Volume 41, Issue 6, Page(s) e2100289

Abstract: DNA-Encoded Library (DEL) technology has emerged as an alternative method for bioactive molecules discovery in medicinal chemistry. It enables the simple synthesis and screening of compound libraries of enormous size. Even though it gains more and more ... ...

Abstract	DNA-Encoded Library (DEL) technology has emerged as an alternative method for bioactive molecules discovery in medicinal chemistry. It enables the simple synthesis and screening of compound libraries of enormous size. Even though it gains more and more popularity each day, there are almost no reports of chemoinformatics analysis of DEL chemical space. Therefore, in this project, we aimed to generate and analyze the ultra-large chemical space of DEL. Around 2500 DELs were designed using commercially available building blocks resulting in 2,5B DEL compounds that were compared to biologically relevant compounds from ChEMBL using Generative Topographic Mapping. This allowed to choose several optimal DELs covering the chemical space of ChEMBL to the highest extent and thus containing the maximum possible percentage of biologically relevant chemotypes. Different combinations of DELs were also analyzed to identify a set of mutually complementary libraries allowing to attain even higher coverage of ChEMBL than it is possible with one single DEL.
MeSH term(s)	Cheminformatics ; Chemistry, Pharmaceutical ; DNA/chemistry ; Drug Discovery/methods ; Small Molecule Libraries/chemistry
Chemical Substances	Small Molecule Libraries ; DNA (9007-49-2)
Language	English
Publishing date	2022-01-28
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2537668-8
ISSN	1868-1751 ; 1868-1743
ISSN (online)	1868-1751
ISSN	1868-1743
DOI	10.1002/minf.202100289
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Article ; Online: Trustworthiness, the Key to Grid-Based Map-Driven Predictive Model Enhancement and Applicability Domain Control.

Horvath, Dragos / Marcou, Gilles / Varnek, Alexandre

Journal of chemical information and modeling

2020 Volume 60, Issue 12, Page(s) 6020–6032

Abstract: In chemography, grid-based maps sample molecular descriptor space by injecting a set of nodes, and then linking them to some regular 2D grid representing the map. They include self-organizing maps (SOMs) and generative topographic maps (GTMs). Grid-based ...

Abstract	In chemography, grid-based maps sample molecular descriptor space by injecting a set of nodes, and then linking them to some regular 2D grid representing the map. They include self-organizing maps (SOMs) and generative topographic maps (GTMs). Grid-based maps are predictive because any compound thereupon projected can "inherit" the properties of its residence node(s)-node properties themselves "inherited" from node-neighboring training set compounds. This Article proposes a formalism to define the trustworthiness of these nodes as "providers" of structure-activity information captured from training compounds. An empirical four-parameter node trustworthiness (NT) function of density (sparsely populated nodes are less trustworthy) and coherence (nodes with training set residents of divergent properties are less trustworthy) is proposed. Based upon it, a trustworthiness score
MeSH term(s)	Algorithms
Language	English
Publishing date	2020-11-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	190019-5
ISSN	1549-960X ; 0095-2338
ISSN (online)	1549-960X
ISSN	0095-2338
DOI	10.1021/acs.jcim.0c00998
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Article ; Online: Generative topographic mapping in drug design.

Horvath, Dragos / Marcou, Gilles / Varnek, Alexandre

Drug discovery today. Technologies

2020 Volume 32-33, Page(s) 99–107

Abstract: This is a review article of Generative Topographic Mapping (GTM) - a non-linear dimensionality reduction technique producing generative 2D maps of high-dimensional vector spaces - and its specific applications in Drug Design (chemical space cartography, ... ...

Abstract	This is a review article of Generative Topographic Mapping (GTM) - a non-linear dimensionality reduction technique producing generative 2D maps of high-dimensional vector spaces - and its specific applications in Drug Design (chemical space cartography, compound library design and analysis, virtual screening, pharmacological profiling, de novo drug design, conformational space & docking interaction cartography, etc.) Written by chemoinformaticians for potential users among medicinal chemists and biologists, the article purposely avoids all underlying mathematics. First, the GTM concept is intuitively explained, based on the strong analogies with the rather popular Self-Organizing Maps (SOMs), which are well established library analysis tools. GTM is basically a fuzzy-logics-based generalization of SOMs. The second part of the review, some of published GTM applications in drug design are briefly revisited.
MeSH term(s)	Drug Design ; Drug Discovery ; Humans ; Models, Molecular ; Molecular Conformation ; Peptide Mapping
Language	English
Publishing date	2020-06-30
Publishing country	England
Document type	Journal Article
ISSN	1740-6749
ISSN (online)	1740-6749
DOI	10.1016/j.ddtec.2020.06.003
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Article ; Online: "Big Data" Fast Chemoinformatics Model to Predict Generalized Born Radius and Solvent Accessibility as a Function of Geometry.

Horvath, Dragos / Marcou, Gilles / Varnek, Alexandre

Journal of chemical information and modeling

2020 Volume 60, Issue 6, Page(s) 2951–2965

Abstract: The Generalized Born (GB) solvent model is offering the best accuracy/computing effort ratio yet requires drastic simplifications to estimate of the Effective Born Radii (EBR) in bypassing a too expensive volume integration step. EBRs are a measure of ... ...

Abstract	The Generalized Born (GB) solvent model is offering the best accuracy/computing effort ratio yet requires drastic simplifications to estimate of the Effective Born Radii (EBR) in bypassing a too expensive volume integration step. EBRs are a measure of the degree of burial of an atom and not very sensitive to small changes of geometry: in molecular dynamics, the costly EBR update procedure is not mandatory at every step. This work however aims at implementing a GB model into the Sampler for Multiple Protein-Ligand Entities (S4MPLE) evolutionary algorithm with mandatory EBR updates at each step triggering arbitrarily large geometric changes. Therefore, a quantitative structure-property relationship has been developed in order to express the EBRs as a linear function of both the topological neighborhood and geometric occupancy of the space around atoms. A training set of 810 molecular systems, starting from fragment-like to drug-like compounds, proteins, host-guest systems, and ligand-protein complexes, has been compiled. For each species, S4MPLE generated several hundreds of random conformers. For each atom in each geometry of each species, its "standard" EBR was calculated by numeric integration and associated to topological and geometric descriptors of the atom neighborhood. This training set (EBR, atom descriptors) involving >5 M entries was subjected to a boot-strapping multilinear regression process with descriptor selection. In parallel, the strategy was repurposed to also learn atomic solvent-accessible areas (SA) based on the same descriptors. Resulting linear equations were challenged to predict EBR and SA values for a similarly compiled external set of >2000 new molecular systems. Solvation energies calculated with estimated EBR and SA match "standard" energies within the typical error of a force-field-based approach (a few kilocalories per mole). Given the extreme diversity of molecular systems covered by the model, this simple EBR/SA estimator covers a vast applicability domain.
MeSH term(s)	Cheminformatics ; Proteins ; Radius ; Solvents ; Thermodynamics
Chemical Substances	Proteins ; Solvents
Language	English
Publishing date	2020-05-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	190019-5
ISSN	1549-960X ; 0095-2338
ISSN (online)	1549-960X
ISSN	0095-2338
DOI	10.1021/acs.jcim.9b01172
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