A machine learning-driven approach for prioritizing food contact chemicals of carcinogenic concern based on complementary in silico methods

2022 ◽  
pp. 112802
Author(s):  
Chia-Chi Wang ◽  
Yu-Chih Liang ◽  
Shan-Shan Wang ◽  
Pinpin Lin ◽  
Chun-Wei Tung
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
Food Contact ◽  
Contact Chemicals ◽  
In Silico Methods

scholarly journals Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2505
Author(s):  
Raheem Remtulla ◽  
Sanjoy Kumar Das ◽  
Leonard A. Levin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
In Silico ◽  
Disulfide Bonds ◽  
Oral Absorption ◽  
In Vivo Studies ◽  
Drug Candidates ◽  
In Silico Methods

Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated in the downstream effects of axonal injury. A difficulty in using standard in silico methods for studying these drugs is that most computational tools are not designed for borane-containing compounds. Using in silico and machine learning methodologies, the absorption-distribution properties of these unique compounds were assessed. Features examined with in silico methods included cellular permeability, octanol-water partition coefficient, blood-brain barrier permeability, oral absorption and serum protein binding. The resultant neural networks demonstrated an appropriate level of accuracy and were comparable to existing in silico methodologies. Specifically, they were able to reliably predict pharmacokinetic features of known boron-containing compounds. These methods predicted that phosphine-borane compounds and their metabolites meet the necessary pharmacokinetic features for orally active drug candidates. This study showed that the combination of standard in silico predictive and machine learning models with neural networks is effective in predicting pharmacokinetic features of novel boron-containing compounds as neuroprotective drugs.

Computational methods on food contact chemicals: Big data and in silico screening on nuclear receptors family

Chemosphere ◽  
2021 ◽  
pp. 133422
Author(s):  
Pietro Cozzini ◽  
Francesca Cavaliere ◽  
Giulia Spaggiari ◽  
Gianluca Morelli ◽  
Marco Riani
Keyword(s):  
Big Data ◽  
Nuclear Receptors ◽  
Computational Methods ◽  
In Silico ◽  
In Silico Screening ◽  
Food Contact ◽  
Contact Chemicals

Machine learning and in silico methods

2021 ◽  
pp. 375-390
Author(s):  
Ching-Long Lin ◽  
Eric A. Hoffman ◽  
Stavros Kassinos
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
In Silico Methods

In Silico Machine Learning Methods in Drug Development

2014 ◽  
Vol 14 (16) ◽  
pp. 1913-1922 ◽  
Author(s):  
Dimitar Dobchev ◽  
Girinath Pillai ◽  
Mati Karelson
Keyword(s):  
Machine Learning ◽  
Drug Development ◽  
In Silico ◽  
Learning Methods ◽  
Machine Learning Methods

Elucidation of Phosphodiesterase-1 Inhibitory Effect of Some Selected Natural Polyphenolics Using In Vitro and In Silico Methods

2016 ◽  
Vol 17 (4) ◽  
pp. 412-417 ◽  
Author(s):  
Abdur Rauf ◽  
Ilkay Erdogan Orhan ◽  
Abdulselam Ertas ◽  
Hamdi Temel ◽  
Taibi Ben Hadda ◽  
...  
Keyword(s):  
In Silico ◽  
Inhibitory Effect ◽  
In Silico Methods

Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes

2019 ◽  
Vol 19 (5) ◽  
pp. 319-336 ◽  
Author(s):  
Alexander V. Dmitriev ◽  
Alexey A. Lagunin ◽  
Dmitry А. Karasev ◽  
Anastasia V. Rudik ◽  
Pavel V. Pogodin ◽  
...  
Keyword(s):  
In Silico ◽  
Computational Models ◽  
Field Analysis ◽  
Pharmaceutical Chemistry ◽  
Sources Of Information ◽  
Metabolizing Enzymes ◽  
Physiologically Based Pharmacokinetic ◽  
Pharmacophore Models ◽  
Drug Metabolising Enzymes ◽  
In Silico Methods

Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.

scholarly journals Energetics of LOHC: Structure-Property Relationships from Network of Thermochemical Experiments and in Silico Methods

Hydrogen ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 101-121
Author(s):  
Sergey P. Verevkin ◽  
Vladimir N. Emel’yanenko ◽  
Riko Siewert ◽  
Aleksey A. Pimerzin
Keyword(s):  
In Silico ◽  
Quantum Chemical ◽  
Structure Property ◽  
Chemical Methods ◽  
Key Technology ◽  
Structure Property Relationships ◽  
Quantum Chemical Methods ◽  
Dehydrogenation Reactions ◽  
Storage Technologies ◽  
In Silico Methods

The storage of hydrogen is the key technology for a sustainable future. We developed an in silico procedure, which is based on the combination of experimental and quantum-chemical methods. This method was used to evaluate energetic parameters for hydrogenation/dehydrogenation reactions of various pyrazine derivatives as a seminal liquid organic hydrogen carriers (LOHC), that are involved in the hydrogen storage technologies. With this in silico tool, the tempo of the reliable search for suitable LOHC candidates will accelerate dramatically, leading to the design and development of efficient materials for various niche applications.

scholarly journals Glucocorticoid resistance conferring mutation in the C-terminus of GR alters the receptor conformational dynamics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anna Kaziales ◽  
Florian Rührnößl ◽  
Klaus Richter
Keyword(s):  
Glucocorticoid Receptor ◽  
Steroid Hormones ◽  
In Silico ◽  
Conformational Dynamics ◽  
Glucocorticoid Resistance ◽  
Physiological Processes ◽  
C Terminus ◽  
Hsp90 Chaperone ◽  
In Silico Methods

AbstractThe glucocorticoid receptor is a key regulator of essential physiological processes, which under the control of the Hsp90 chaperone machinery, binds to steroid hormones and steroid-like molecules and in a rather complicated and elusive response, regulates a set of glucocorticoid responsive genes. We here examine a human glucocorticoid receptor variant, harboring a point mutation in the last C-terminal residues, L773P, that was associated to Primary Generalized Glucocorticoid Resistance, a condition originating from decreased affinity to hormone, impairing one or multiple aspects of GR action. Using in vitro and in silico methods, we assign the conformational consequences of this mutation to particular GR elements and report on the altered receptor properties regarding its binding to dexamethasone, a NCOA-2 coactivator-derived peptide, DNA, and importantly, its interaction with the chaperone machinery of Hsp90.

In silico prediction of chemical respiratory toxicity via machine learning

2021 ◽  
Vol 18 ◽  
pp. 100155
Author(s):  
Zhiyuan Wang ◽  
Piaopiao Zhao ◽  
Xiaoxiao Zhang ◽  
Xuan Xu ◽  
Weihua Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
In Silico Prediction ◽  
Respiratory Toxicity
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