Cheminformatics in Natural Product‐based Drug Discovery

Ya, Chen; Kirchmair, Johannes

doi:10.1002/minf.202000171

Cited by 89 publications

(72 citation statements)

References 165 publications

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“…The increasing use of NP in modern drug discovery has promoted the application of chemoinformatic methods for natural product-based drug discovery. One such contribution is the generation and development of compound databases [6][7][8]. The development of compound databases of NP and synthetic analogs has been recently reviewed [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…One such contribution is the generation and development of compound databases [6][7][8]. The development of compound databases of NP and synthetic analogs has been recently reviewed [8,9]. A recent notable example is the COlleCtion of Open NatUral producTs (COCONUT), a compendium of 50 open-access databases collecting more than 400,000 compounds.…”

Section: Introductionmentioning

confidence: 99%

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Fragment Library of Natural Products and Compound Databases for Drug Discovery

2020

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Natural products and semi-synthetic compounds continue to be a significant source of drug candidates for a broad range of diseases, including coronavirus disease 2019 (COVID-19), which is causing the current pandemic. Besides being attractive sources of bioactive compounds for further development or optimization, natural products are excellent substrates of unique substructures for fragment-based drug discovery. To this end, fragment libraries should be incorporated into automated drug design pipelines. However, public fragment libraries based on extensive collections of natural products are still limited. Herein, we report the generation and analysis of a fragment library of natural products derived from a database with more than 400,000 compounds. We also report fragment libraries of a large food chemical database and other compound datasets of interest in drug discovery, including compound libraries relevant for COVID-19 drug discovery. The fragment libraries were characterized in terms of content and diversity.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fragment Library of Natural Products and Compound Databases for Drug Discovery

2020

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“…Later, we will also investigate the utility of hybrid feature vectors containing interrelation profiles concatenated with, for example, QAFFP biological fingerprints [63,64] or with other features of interest. We plan to use interrelation profiling in various cheminformatics applications, such as in biological activity classification or potency prediction, focused chemical library construction, diversity data selection or ensemble modeling using RFT together with domain-specific models for, e.g., natural product likeness assessment [65][66][67]. Given that Correlation between ZRFT, SYBA and SAScore.…”

Section: Resultsmentioning

confidence: 99%

Profiling and analysis of chemical compounds using pointwise mutual information

2021

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Pointwise mutual information (PMI) is a measure of association used in information theory. In this paper, PMI is used to characterize several publicly available databases (DrugBank, ChEMBL, PubChem and ZINC) in terms of association strength between compound structural features resulting in database PMI interrelation profiles. As structural features, substructure fragments obtained by coding individual compounds as MACCS, PubChemKey and ECFP fingerprints are used. The analysis of publicly available databases reveals, in accord with other studies, unusual properties of DrugBank compounds which further confirms the validity of PMI profiling approach. Z-standardized relative feature tightness (ZRFT), a PMI-derived measure that quantifies how well the given compound’s feature combinations fit these in a particular compound set, is applied for the analysis of compound synthetic accessibility (SA), as well as for the classification of compounds as easy (ES) and hard (HS) to synthesize. ZRFT value distributions are compared with these of SYBA and SAScore. The analysis of ZRFT values of structurally complex compounds in the SAVI database reveals oligopeptide structures that are mispredicted by SAScore as HS, while correctly predicted by ZRFT and SYBA as ES. Compared to SAScore, SYBA and random forest, ZRFT predictions are less accurate, though by a narrow margin (AccZRFT = 94.5%, AccSYBA = 98.8%, AccSAScore = 99.0%, AccRF = 97.3%). However, ZRFT ability to distinguish between ES and HS compounds is surprisingly high considering that while SYBA, SAScore and random forest are dedicated SA models, ZRFT is a generic measurement that merely quantifies the strength of interrelations between structural feature pairs. The results presented in the current work indicate that structural feature co-occurrence, quantified by PMI or ZRFT, contains a significant amount of information relevant to physico-chemical properties of organic compounds.

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“…The understanding of the interaction between an NP and a target is a challenging task and is often a necessary step for the design of chemical compounds with enhanced properties [ 1 ]. This step requires a precise knowledge of the structure of the NP (and of its target) at the atomic level, a concern that converges with the one of chemotaxonomy.…”

Section: Introductionmentioning

confidence: 99%

The Three Pillars of Natural Product Dereplication. Alkaloids from the Bulbs of Urceolina peruviana (C. Presl) J.F. Macbr. as a Preliminary Test Case

et al. 2021

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The role and importance of the identification of natural products are discussed in the perspective of the study of secondary metabolites. The rapid identification of already reported compounds, or structural dereplication, is recognized as a key element in natural product chemistry. The biological taxonomy of metabolite producing organisms, the knowledge of metabolite molecular structures, and the availability of metabolite spectroscopic signatures are considered as the three pillars of structural dereplication. The role and the construction of databases is illustrated by references to the KNApSAcK, UNPD, CSEARCH, and COCONUT databases, and by the importance of calculated taxonomic and spectroscopic data as substitutes for missing or lost original ones. Two NMR-based tools, the PNMRNP database that derives from UNPD, and KnapsackSearch, a database generator that provides taxonomically focused libraries of compounds, are proposed to the community of natural product chemists. The study of the alkaloids from Urceolina peruviana, a plant from the Andes used in traditional medicine for antibacterial and anticancer actions, has given the opportunity to test different approaches to dereplication, favoring the use of publicly available data sources.

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Cheminformatics in Natural Product‐based Drug Discovery

Cited by 89 publications

References 165 publications

Fragment Library of Natural Products and Compound Databases for Drug Discovery

Fragment Library of Natural Products and Compound Databases for Drug Discovery

Profiling and analysis of chemical compounds using pointwise mutual information

The Three Pillars of Natural Product Dereplication. Alkaloids from the Bulbs of Urceolina peruviana (C. Presl) J.F. Macbr. as a Preliminary Test Case

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