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Title: Virtual Screening of Molecules via Neural Fingerprint-based Deep Learning Technique
Abstract A machine learning-based drug screening technique has been developed and optimized using convolutional neural network-derived fingerprints. The optimization of weights in the neural network-based fingerprinting technique was compared with fixed Morgan fingerprints in regard to binary classification on drug-target binding affinity. The assessment was carried out using six different target proteins using randomly chosen small molecules from the ZINC15 database for training. This new architecture proved to be more efficient in screening molecules that less favorably bind to specific targets and retaining molecules that favorably bind to it. Scientific contribution We have developed a new neural fingerprint-based screening model that has a significant ability to capture hits. Despite using a smaller dataset, this model is capable of mapping chemical space similar to other contemporary algorithms designed for molecular screening. The novelty of the present algorithm lies in the speed with which the models are trained and tuned before testing its predictive capabilities and hence is a significant step forward in the field of machine learning-embedded computational drug discovery.  more » « less
Award ID(s):
2150191
PAR ID:
10544566
Author(s) / Creator(s):
;
Publisher / Repository:
Research Square
Date Published:
Format(s):
Medium: X
Institution:
Research Square
Sponsoring Org:
National Science Foundation
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