2021
DOI: 10.1371/journal.pone.0246126
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Abstract: Computational methods have been widely used in drug design. The recent developments in machine learning techniques and the ever-growing chemical and biological databases are fertile ground for discoveries in this area. In this study, we evaluated the performance of Deep Learning models in comparison to Random Forest, and Support Vector Regression for predicting the biological activity (pIC50) of ALK-5 inhibitors as candidates to treat cancer. The generalization power of the models was assessed by internal and … Show more

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Cited by 11 publications
(3 citation statements)
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“…The RL framework uses the value of the property predicted to generate a reward or loss to drive the Generator to output molecules with the desired property. Deep neural networks have been demonstrated to be capable of performing this task [ 37 ]. In our system, the Predictor is a character-level Convolutional Neural Network (CNN).…”
Section: Methodsmentioning
confidence: 99%
“…The RL framework uses the value of the property predicted to generate a reward or loss to drive the Generator to output molecules with the desired property. Deep neural networks have been demonstrated to be capable of performing this task [ 37 ]. In our system, the Predictor is a character-level Convolutional Neural Network (CNN).…”
Section: Methodsmentioning
confidence: 99%
“…Machine learning (ML) based regression techniques are becoming wide spread in many areas of data analysis in the chemical 11,12 and pharmaceutical sector [13][14][15][16] ; they have recently been employed in drug development [17][18][19] , diagnostic 20 , treatment algorithm optimisation 21 , drug repurposing 2,22 and material discovery 23,24 ; however such applications are still quite limited despite being very promising 25,26 . Another application of ML technologies in drug discovery is during compound screening or hit/lead generation and optimization enabling a virtual screening platform that offers a quicker and cheaper alternative to classic testing of large compounds libraries 27,28 ; virtual screening can be generally classified in ligand-based or structure-based 28 .…”
Section: Feasibility and Application Of Machine Learning Enabled Fast...mentioning
confidence: 99%
“…Machine learning (ML) based regression techniques are becoming wide spread in many areas of data analysis in the chemical 11,12 and pharmaceutical sector [13][14][15][16] and have recently been employed in drug development [17][18][19] , diagnostic 20 , treatment algorithm optimisation 21 , drug repurposing 2,22 and material discovery 23,24 ; however such applications are still quite limited despite been very promising 25,26 . Figure 1 depicts how ML could be deployed to accelerate the biomaterial development process.…”
Section: Introductionmentioning
confidence: 99%