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

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 dete...

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Bibliographic Details
Published in:Computation 2021-10, Vol.9 (10), p.103
Main Authors: Lowdon, Joseph W., Ishikura, Hikaru, Kvernenes, Malene K., Caldara, Manlio, Cleij, Thomas J., van Grinsven, Bart, Eersels, Kasper, Diliën, Hanne
Format: Article
Language:English
Subjects:
Affinity
Algorithms
artificial intelligence
Caffeine
Data analysis
Datasets
Deep learning
Imprinted polymers
Machine learning
molecularly imprinted polymers
neural networking
Neural networks
Polymers
Selective binding
Selectivity
simulation
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Summary: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.
ISSN:2079-3197
2079-3197
DOI:10.3390/computation9100103