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TranScreen: Transfer Learning on Graph-Based Anti-Cancer Virtual Screening Model

1
Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA
2
Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL 32816, USA
*
Author to whom correspondence should be addressed.
Equal Contributions.
Big Data Cogn. Comput. 2020, 4(3), 16; https://doi.org/10.3390/bdcc4030016
Received: 19 May 2020 / Revised: 17 June 2020 / Accepted: 23 June 2020 / Published: 29 June 2020
Deep learning’s automatic feature extraction has proven its superior performance over traditional fingerprint-based features in the implementation of virtual screening models. However, these models face multiple challenges in the field of early drug discovery, such as over-training and generalization to unseen data, due to the inherently unbalanced and small datasets. In this work, the TranScreen pipeline is proposed, which utilizes transfer learning and a collection of weight initializations to overcome these challenges. An amount of 182 graph convolutional neural networks are trained on molecular source datasets and the learned knowledge is transferred to the target task for fine-tuning. The target task of p53-based bioactivity prediction, an important factor for anti-cancer discovery, is chosen to showcase the capability of the pipeline. Having trained a collection of source models, three different approaches are implemented to compare and rank them for a given task before fine-tuning. The results show improvement in performance of the model in multiple cases, with the best model increasing the area under receiver operating curve ROC-AUC from 0.75 to 0.91 and the recall from 0.25 to 1. This improvement is vital for practical virtual screening via lowering the false negatives and demonstrates the potential of transfer learning. The code and pre-trained models are made accessible online. View Full-Text
Keywords: cancer; drug discovery; machine learning; transfer learning; virtual screening cancer; drug discovery; machine learning; transfer learning; virtual screening
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MDPI and ACS Style

Salem, M.; Khormali, A.; Arshadi, A.K.; Webb, J.; Yuan, J.-S. TranScreen: Transfer Learning on Graph-Based Anti-Cancer Virtual Screening Model. Big Data Cogn. Comput. 2020, 4, 16. https://doi.org/10.3390/bdcc4030016

AMA Style

Salem M, Khormali A, Arshadi AK, Webb J, Yuan J-S. TranScreen: Transfer Learning on Graph-Based Anti-Cancer Virtual Screening Model. Big Data and Cognitive Computing. 2020; 4(3):16. https://doi.org/10.3390/bdcc4030016

Chicago/Turabian Style

Salem, Milad; Khormali, Aminollah; Arshadi, Arash K.; Webb, Julia; Yuan, Jiann-Shiun. 2020. "TranScreen: Transfer Learning on Graph-Based Anti-Cancer Virtual Screening Model" Big Data Cogn. Comput. 4, no. 3: 16. https://doi.org/10.3390/bdcc4030016

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