Progress in visual representations of chemical space

Osolodkin, Dmitry I.; Radchenko, Eugene V.; Orlov, Alexey A.; Voronkov, Andrey E; Palyulin, V. A.; Зефиров, Н. С.

doi:10.1517/17460441.2015.1060216

Cited by 77 publications

(65 citation statements)

References 104 publications

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“…This reflects a well-known conundrum: while chemical space is straightforward to envision, it is difficult to visualize. Although a variety of computational approaches have been introduced for visualization of multi-dimensional chemical space [8], they often represent chemical space in rather abstract formats, which do not cater to the chemist's and the understanding of which is often limited to experts in chemical informatics.…”

Section: Introductionmentioning

confidence: 99%

Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures

Zhang

Vogt

Maggiora

et al. 2015

J Comput Aided Mol Des

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Chemical space networks (CSNs) have recently been introduced as an alternative to other coordinate-free and coordinate-based chemical space representations. In CSNs, nodes represent compounds and edges pairwise similarity relationships. In addition, nodes are annotated with compound property information such as biological activity. CSNs have been applied to view biologically relevant chemical space in comparison to random chemical space samples and found to display well-resolved topologies at low edge density levels. The way in which molecular similarity relationships are assessed is an important determinant of CSN topology. Previous CSN versions were based on numerical similarity functions or the assessment of substructure-based similarity. Herein, we report a new CSN design that is based upon combined numerical and substructure similarity evaluation. This has been facilitated by calculating numerical similarity values on the basis of maximum common substructures (MCSs) of compounds, leading to the introduction of MCS-based CSNs (MCS-CSNs). This CSN design combines advantages of continuous numerical similarity functions with a robust and chemically intuitive substructure-based assessment. Compared to earlier version of CSNs, MCS-CSNs are characterized by a further improved organization of local compound communities as exemplified by the delineation of drug-like subspaces in regions of biologically relevant chemical space.

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Section: Introductionmentioning

confidence: 99%

Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures

Zhang

Vogt

Maggiora

et al. 2015

J Comput Aided Mol Des

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“…Two approaches were used to cluster and visualize the molecules in the data sets based on their molecular properties and structural features: Principal Component Analysis (PCA) (Jolliffe, 2002) and Generative Topographic Mapping (GTM) (Osolodkin et al, 2015). PCA is a technique often used to emphasize variation and find patterns in a data set.…”

Section: Methodsmentioning

confidence: 99%

Scaffold Diversity of Fungal Metabolites

González-Medina

Owen²,

El‐Elimat

et al. 2017

Front. Pharmacol.

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Many drug discovery projects rely on commercial compounds to discover active leads. However, current commercial libraries, with mostly synthetic compounds, access a small fraction of the possible chemical diversity. Natural products, in contrast, possess a vast structural diversity and have proven to be an outstanding source of new drugs. Several chemoinformatic analyses of natural products have demonstrated their diversity and structural complexity. However, to our knowledge, the scaffold content and structural diversity of fungal secondary metabolites have never been studied. Herein, the scaffold diversity of 223 fungal metabolites was measured and compared to the diversity of approved drugs and commercial libraries for HTS containing natural, synthetic, and semi-synthetic compounds. In addition, the global diversity of the fungal isolates was assessed and compared to other reference data sets using Consensus Diversity Plots, a chemoinformatic tool recently developed. It was concluded that fungal secondary metabolites are cyclic systems with few ramifications and more diverse than the commercial libraries with natural products and semi-synthetic compounds. The fungal metabolites data set was one of the most structurally diverse, containing a large proportion of different and unique scaffolds not found in the other compound data sets including ChEMBL. Therefore, fungal metabolites offer a rich source of molecules suited for identifying diverse candidates for drug discovery.

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“…MD simulations were performed with GROMACS 4.5.256 using Gromos96 45a3 parameters set57. The conotoxins’ structures were solvated inside a (3.5–5 nm)3 box; a SPC water model58 was used; the number of water molecules was 1600–3600. Counterions (Na + or Cl − ) were added to maintain electroneutrality (0–4 ions).…”

Section: Methodsmentioning

confidence: 99%

“…Although long proclaimed, the rational design of biological molecules (e.g., drug design) is still more art and fortune than well-established technology. This is especially true when, rather than talking about small molecules (where drug design strategies have achieved considerable success1234567), we examine bioactive peptides, which are versatile bioregulators and target many receptors and ion channels in the organism89101112, most significantly in the nervous system. Because protein–ligand binding critically depends on the spatial distribution of a number of physical properties over the interacting surfaces, a detailed characterization of the latter is indispensable in order to understand the interaction mechanisms.…”

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confidence: 99%

High-Affinity α-Conotoxin PnIA Analogs Designed on the Basis of the Protein Surface Topography Method

Kasheverov

Chugunov

Kudryavtsev

et al. 2016

Sci Rep

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Despite some success for small molecules, elucidating structure–function relationships for biologically active peptides — the ligands for various targets in the organism — remains a great challenge and calls for the development of novel approaches. Some of us recently proposed the Protein Surface Topography (PST) approach, which benefits from a simplified representation of biomolecules’ surface as projection maps, which enables the exposure of the structure–function dependencies. Here, we use PST to uncover the “activity pattern” in α-conotoxins — neuroactive peptides that effectively target nicotinic acetylcholine receptors (nAChRs). PST was applied in order to design several variants of the α-conotoxin PnIA, which were synthesized and thoroughly studied. Among the best was PnIA[R9, L10], which exhibits nanomolar affinity for the α7 nAChR, selectivity and a slow wash-out from this target. Importantly, these mutations could hardly be delineated by “standard” structure-based drug design. The proposed combination of PST with a set of experiments proved very efficient for the rational construction of new bioactive molecules.

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Progress in visual representations of chemical space

Cited by 77 publications

References 104 publications

Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures

Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures

Scaffold Diversity of Fungal Metabolites

High-Affinity α-Conotoxin PnIA Analogs Designed on the Basis of the Protein Surface Topography Method

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