In Silico Prediction of Hemolytic Toxicity on the Human Erythrocytes for Small Molecules by Machine-Learning and Genetic Algorithm

Even before the perception or interaction with pathogens, plants rely on constitutively guardian molecules, often specific to tissue or stage, with further expression after contact with the pathogen. These guardians include small molecules as antimicrobial peptides (AMPs), generally cysteine-rich, functioning to prevent pathogen establishment. Some of these AMPs are shared among eukaryotes (eg, defensins and cyclotides), others are plant specific (eg, snakins), while some are specific to certain plant families (such as heveins). When compared with other organisms, plants tend to present a higher amount of AMP isoforms due to gene duplications or polyploidy, an occurrence possibly also associated with the sessile habit of plants, which prevents them from evading biotic and environmental stresses. Therefore, plants arise as a rich resource for new AMPs. As these molecules are difficult to retrieve from databases using simple sequence alignments, a description of their characteristics and in silico (bioinformatics) approaches used to retrieve them is provided, considering resources and databases available. The possibilities and applications based on tools versus database approaches are considerable and have been so far underestimated.

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In Silico Prediction of Metabolic Epoxidation for Drug‐like Molecules via Machine Learning Methods

Molecular Informatics ◽

10.1002/minf.201900178 ◽

2020 ◽

Vol 39 (8) ◽

pp. 1900178

Author(s):

Jiajing Hu ◽

Yingchun Cai ◽

Weihua Li ◽

Guixia Liu ◽

Yun Tang

Keyword(s):

Machine Learning ◽

In Silico ◽

In Silico Prediction ◽

Learning Methods ◽

Machine Learning Methods

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In silico prediction of brain and CSF permeation of small molecules using PLS regression models

European Journal of Medicinal Chemistry ◽

10.1016/j.ejmech.2007.11.011 ◽

2008 ◽

Vol 43 (8) ◽

pp. 1581-1592 ◽

Cited By ~ 29

Author(s):

Stefanie Bendels ◽

Manfred Kansy ◽

Björn Wagner ◽

Jörg Huwyler

Keyword(s):

Small Molecules ◽

In Silico ◽

Regression Models ◽

Pls Regression ◽

In Silico Prediction

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Physicochemical and Structural Parameters Contributing to the Antibacterial Activity and Efflux Susceptibility of Small Molecule Inhibitors of Escherichia coli

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.01925-20 ◽

2021 ◽

Author(s):

Sara S. El Zahed ◽

Shawn French ◽

Maya A. Farha ◽

Garima Kumar ◽

Eric D. Brown

Keyword(s):

Machine Learning ◽

Escherichia Coli ◽

Antibacterial Activity ◽

Small Molecules ◽

Small Molecule ◽

In Silico ◽

Molecular Descriptors ◽

Structural Parameters ◽

Side Chain ◽

Gram Negative

Discovering new Gram-negative antibiotics has been a challenge for decades. This has been largely attributed to a limited understanding of the molecular descriptors governing Gram-negative permeation and efflux evasion. Herein, we address the contribution of efflux using a novel approach that applies multivariate analysis, machine learning, and structure-based clustering to some 4,500 actives from a small molecule screen in efflux-compromised Escherichia coli. We employed principal-component analysis and trained two decision tree-based machine learning models to investigate descriptors contributing to the antibacterial activity and efflux susceptibility of these actives. This approach revealed that the Gram-negative activity of hydrophobic and planar small molecules with low molecular stability is limited to efflux-compromised E. coli. Further, molecules with reduced branching and compactness showed increased susceptibility to efflux. Given these distinct properties that govern efflux, we developed the first machine learning model, called Susceptibility to Efflux Random Forest (SERF), as a tool to analyze the molecular descriptors of small molecules and predict those that could be susceptible to efflux pumps in silico. Here, SERF demonstrated high accuracy in identifying such molecules. Further, we clustered all 4,500 actives based on their core structures and identified distinct clusters highlighting side chain moieties that cause marked changes in efflux susceptibility. In all, our work reveals a role for physicochemical and structural parameters in governing efflux, presents a machine learning tool for rapid in silico analysis of efflux susceptibility, and provides a proof of principle for the potential of exploiting side chain modification to design novel antimicrobials evading efflux pumps.

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In Silico Prediction of CYP2C8 Inhibition with Machine-Learning Methods

Chemical Research in Toxicology ◽

10.1021/acs.chemrestox.1c00078 ◽

2021 ◽

Author(s):

Xiaoxiao Zhang ◽

Piaopiao Zhao ◽

Zhiyuan Wang ◽

Xuan Xu ◽

Guixia Liu ◽

...

Keyword(s):

Machine Learning ◽

In Silico ◽

In Silico Prediction ◽

Learning Methods ◽

Machine Learning Methods

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In silico prediction of drug-induced developmental toxicity by using machine learning approaches

Molecular Diversity ◽

10.1007/s11030-019-09991-y ◽

2019 ◽

Vol 24 (4) ◽

pp. 1281-1290

Author(s):

Hui Zhang ◽

Jun Mao ◽

Hua-Zhao Qi ◽

Lan Ding

Keyword(s):

Machine Learning ◽

In Silico ◽

Developmental Toxicity ◽

Learning Approaches ◽

In Silico Prediction ◽

Drug Induced

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In silico prediction of hERG potassium channel blockage by chemical category approaches

Toxicology Research ◽

10.1039/c5tx00294j ◽

2016 ◽

Vol 5 (2) ◽

pp. 570-582 ◽

Cited By ~ 16

Author(s):

Chen Zhang ◽

Yuan Zhou ◽

Shikai Gu ◽

Zengrui Wu ◽

Wenjie Wu ◽

...

Keyword(s):

Machine Learning ◽

Potassium Channel ◽

In Silico ◽

Molecular Descriptors ◽

In Silico Prediction ◽

Learning Methods ◽

Machine Learning Methods ◽

Channel Blockage

A series of models of hERG blockage were built using five machine learning methods based on 13 molecular descriptors, five types of fingerprints and molecular descriptors combining fingerprints at four blockage thresholds.

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