A Survey of Quantitative Descriptions of Molecular Structure

Guha, Rajarshi; Willighagen, Egon

doi:10.2174/156802612804910278

Cited by 53 publications

(17 citation statements)

References 78 publications

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“…These descriptors can provide information on the degree branching of the structure as well as molecular size and shape 25 . Although topological descriptors are extensively used in predictive modelling, they are usually hard to interpret 26 . Topological descriptors may have provided information on how well a molecule fits in the binding site and along with atom counts the interactions with the binding residues.…”

Section: Resultsmentioning

confidence: 99%

Random forest classification for predicting lifespan-extending chemical compounds

Kapsiani

Howlin

2021

Sci Rep

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Ageing is a major risk factor for many conditions including cancer, cardiovascular and neurodegenerative diseases. Pharmaceutical interventions that slow down ageing and delay the onset of age-related diseases are a growing research area. The aim of this study was to build a machine learning model based on the data of the DrugAge database to predict whether a chemical compound will extend the lifespan of Caenorhabditis elegans. Five predictive models were built using the random forest algorithm with molecular fingerprints and/or molecular descriptors as features. The best performing classifier, built using molecular descriptors, achieved an area under the curve score (AUC) of 0.815 for classifying the compounds in the test set. The features of the model were ranked using the Gini importance measure of the random forest algorithm. The top 30 features included descriptors related to atom and bond counts, topological and partial charge properties. The model was applied to predict the class of compounds in an external database, consisting of 1738 small-molecules. The chemical compounds of the screening database with a predictive probability of ≥ 0.80 for increasing the lifespan of Caenorhabditis elegans were broadly separated into (1) flavonoids, (2) fatty acids and conjugates, and (3) organooxygen compounds.

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Section: Resultsmentioning

confidence: 99%

Random forest classification for predicting lifespan-extending chemical compounds

Kapsiani

Howlin

2021

Sci Rep

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“…These descriptors can provide information on the degree branching of the structure as well as molecular size and shape [32]. Although topological descriptors are extensively used in predictive modelling, they are usually hard to interpret [33]. Topological descriptors may have provided information on how well a molecule ts in the binding site and along with atom counts the interactions with the binding residues.…”

Section: Feature Importancementioning

confidence: 99%

Random forest classification for predicting lifespan-extending chemical compounds

Kapsiani

Howlin

2020

Preprint

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Ageing is a major risk factor for many conditions including cancer, cardiovascular and neurodegenerative diseases. Pharmaceutical interventions that slow down ageing and delay the onset of age-related diseases are a growing research area. The aim of this study was to build a machine learning model based on the data of the DrugAge database to predict whether a chemical compound will extend the lifespan of the worm species Caenorhabditis elegans. Five predictive models were built using the random forest algorithm with molecular fingerprints and/or molecular descriptors as features. Feature selection was achieved using variation and mutual information-based methods. The best performing classifier, built using molecular descriptors, achieved an area under the curve (AUC) score of 0.815 for classifying the compounds in the test set. The features of the model were ranked using the Gini importance measure of the random forest algorithm. The top 30 most important features included descriptors related to atom and bond counts, topological and partial charge properties. The model was applied to predict the class of compounds in an external database, consisting of 1,738 small-molecules. The chemical compounds of the screening database with a predictive probability of ≥ 0.80 for increasing the lifespan of Caenorhabditis elegans were broadly separated into (i) flavonoids, (ii) fatty acids and conjugates, and (iii) organooxygen compounds.

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“…The structures were retrieved in SDF format and were changed to PDB and MOL2 format using Discovery Studio Visualizer 4.0 software. The structure and complete chemical properties, torsional energy, van der Waals potential energy, electrostatic energy, weight, log P, total polar surface area (TPSA), donor atoms and acceptor atoms of the ligands were listed ( Supplementary Table 4S) by the help of MOE Module [38].…”

Section: Preparation Of Ligandmentioning

confidence: 99%

In-SilicoAnalysis of Secondary Metabolites that Modulates Enzymes of Cholesterol Target

Marahatha

Basnet

Bhattarai

et al. 2020

Preprint

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Hypercholesterolemia has posed a serious threat of heart diseases and stroke worldwide. Xanthine oxidase (XO), the rate-limiting enzyme in uric acid biosynthesis, is regarded as the root of reactive oxygen species (ROS) that generates atherosclerosis and cholesterol crystals. β-Hydroxy β-methylglutaryl-coenzyme A reductase (HMGR) is a rate-limiting enzyme in cholesterol biosynthesis. Although some commercially available enzyme inhibiting drugs have effectively reduced the cholesterol level, most of them have failed to meet the requirements of being apt drug candidates. Here, we have carried out an in-silico analysis of secondary metabolites that have already shown good inhibitory activity against XO and HMGR. Out of 118 secondary metabolites reviewed, sixteen molecules inhibiting XO and HMGR were taken based on IC50 values reported in vitro assays. Further, receptor-based virtual screening was carried out against secondary metabolites using GOLD Protein-Ligand Docking Software, combined with subsequent post-docking, to study the binding affinities of ligands to the enzymes. In-Silico ADMET analysis was carried out to study their pharmacokinetic properties, followed by toxicity prediction through ProTox-II. The molecular docking of amentoflavone (1) (GOLD score 70.54), and ganomycin I (9) (GOLD score 59.61) evinced that the drug has effectively bind at the competitive site of XO and HMGR, respectively. Besides, 6-paradol (3) and selgin (4) could be potential drug candidates to inhibit XO. Likewise, n-octadecanyl-O-α-D-glucopyranosyl(6’→1”)-O-α-D-glucopyranoside (10) could be potential drug candidates to maintain serum cholesterol. In-silico ADMET analysis showed that the sixteen metabolites were optimal within the categorical range in comparison to commercially available XO and HMGR inhibitors, respectively. Toxicity analysis through Protox-II revealed that 6-gingerol (2), ganoleucoin K (11), and ganoleucoin Z (12) are toxic for human use. This computational analysis supports earlier experimental evidence towards the inhibition of XO and HMGR by natural products. Further study is necessary to explore the clinical efficacy of these secondary molecules, which might be alternatives for the treatment of hypercholesterolemia.Graphical abstract

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A Survey of Quantitative Descriptions of Molecular Structure

Cited by 53 publications

References 78 publications

Random forest classification for predicting lifespan-extending chemical compounds

Random forest classification for predicting lifespan-extending chemical compounds

Random forest classification for predicting lifespan-extending chemical compounds

In-SilicoAnalysis of Secondary Metabolites that Modulates Enzymes of Cholesterol Target

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