Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications

Jiao, Zeren; Hu, Pingfan; Xu, Hongfei; Wang, Qingsheng

doi:10.1021/acs.chas.0c00075

Cited by 125 publications

(56 citation statements)

References 158 publications

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“…The laboratory in vitro research necessitates chemicals and other techniques, which is a time-consuming and tedious process [ 65 ]. Therefore, we accessed the experiment-free prediction method for assessing the inhibitory behavior of our hit compounds.…”

Section: Discussionmentioning

confidence: 99%

Computational Investigation Identified Potential Chemical Scaffolds for Heparanase as Anticancer Therapeutics

Parate

Kumar

Danishuddin

et al. 2021

IJMS

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Heparanase (Hpse) is an endo-β-D-glucuronidase capable of cleaving heparan sulfate side chains. Its upregulated expression is implicated in tumor growth, metastasis and angiogenesis, thus making it an attractive target in cancer therapeutics. Currently, a few small molecule inhibitors have been reported to inhibit Hpse, with promising oral administration and pharmacokinetic (PK) properties. In the present study, a ligand-based pharmacophore model was generated from a dataset of well-known active small molecule Hpse inhibitors which were observed to display favorable PK properties. The compounds from the InterBioScreen database of natural (69,034) and synthetic (195,469) molecules were first filtered for their drug-likeness and the pharmacophore model was used to screen the drug-like database. The compounds acquired from screening were subjected to molecular docking with Heparanase, where two molecules used in pharmacophore generation were used as reference. From the docking analysis, 33 compounds displayed higher docking scores than the reference and favorable interactions with the catalytic residues. Complex interactions were further evaluated by molecular dynamics simulations to assess their stability over a period of 50 ns. Furthermore, the binding free energies of the 33 compounds revealed 2 natural and 2 synthetic compounds, with better binding affinities than reference molecules, and were, therefore, deemed as hits. The hit compounds presented from this in silico investigation could act as potent Heparanase inhibitors and further serve as lead scaffolds to develop compounds targeting Heparanase upregulation in cancer.

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Section: Discussionmentioning

confidence: 99%

Computational Investigation Identified Potential Chemical Scaffolds for Heparanase as Anticancer Therapeutics

Parate

Kumar

Danishuddin

et al. 2021

IJMS

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“…number of observations in a sample, number of samples, allocation of the observations) using the developed model [32]. Also, novel techniques such as machine learning or artificial intelligence may be used [33][34][35]. On the other hand, it would be of interest to incorporate stochastic modeling, in case of extending the analysis to a greater number of independent variables, in order to have a robust system that allows generating predictions of the response in front of partiality in the independent variables [36].…”

Section: Further Perspectivementioning

confidence: 99%

Testing the Fit of Regression Models Estimated with Extremely Small Samples: Application in Pharmaceutical Stability Studies

Mihalovits

Kemény

2022

Period. Polytech. Chem. Eng.

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Pharmaceutical stability studies are conducted to estimate the shelf life, i.e. the period during which the drug product maintains its identity and stability. In the evaluation of process, regression curve is fitted on the data obtained during the study and the shelf life is determined using the fitted curve. The evaluation process suggested by ICH considers only the case of the true relationship between the measured attribute and time being linear. However, no method is suggested for the practitioner to decide if the linear model is appropriate for their dataset. This is a major problem, as a falsely selected model may distort the estimated shelf life to a great extent, resulting in unreliable quality control. The difficulty of model misspecification detection in stability studies is that very few observations are available. The conventional methods applied for model verification might not be appropriate or efficient due to the small sample size. In this paper, this problem is addressed and some developed methods are proposed to detect model misspecification. The methods can be applied for any process where the regression estimation is performed on independent small samples. Besides stability studies, frequently performed construction of single calibration curves for an analytical measurement is another case where the methods may be applied. It is shown that our methods are statistically appropriate and some of them have high efficiency in the detection of model misspecification when applied in simulated situations which resemble pre-approval and post-approval stability studies.

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“…A wide range of NN architectures has been documented, including Directed Acyclic Graphs (DAG), Spatial Graph convolution, Multitask Regressors, and image classification models, to name just a few [24][25][26][27]. Each has a unique manner in which it processes data, though all require molecular structures and other variables to be preprocessed prior to analysis.…”

Section: Introductionmentioning

confidence: 99%

Identifying Potential Machine Learning Algorithms for the Simulation of Binding Affinities to Molecularly Imprinted Polymers

et al. 2021

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Molecularly imprinted polymers (MIPs) are synthetic receptors engineered towards the selective binding of a target molecule; however, the manner in which MIPs interact with other molecules is of great importance. Being able to rapidly analyze the binding of potential molecular interferences and determine the selectivity of a MIP can be a long tedious task, being time- and resource-intensive. Identifying computational models capable of reliably predicting and reporting the binding of molecular species is therefore of immense value in both a research and commercial setting. This research therefore sets focus on comparing the use of machine learning algorithms (multitask regressor, graph convolution, weave model, DAG model, and inception) to predict the binding of various molecular species to a MIP designed towards 2-methoxphenidine. To this end, each algorithm was “trained” with an experimental dataset, teaching the algorithms the structures and binding affinities of various molecular species at varying concentrations. A validation experiment was then conducted for each algorithm, comparing experimental values to predicted values and facilitating the assessment of each approach by a direct comparison of the metrics. The research culminates in the construction of binding isotherms for each species, directly comparing experimental vs. predicted values and identifying the approach that best emulates the real-world data.

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Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications

Cited by 125 publications

References 158 publications

Computational Investigation Identified Potential Chemical Scaffolds for Heparanase as Anticancer Therapeutics

Computational Investigation Identified Potential Chemical Scaffolds for Heparanase as Anticancer Therapeutics

Testing the Fit of Regression Models Estimated with Extremely Small Samples: Application in Pharmaceutical Stability Studies

Identifying Potential Machine Learning Algorithms for the Simulation of Binding Affinities to Molecularly Imprinted Polymers

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