Deep Learning for Drug-Induced Liver Injury

By Youjun Xu, Ziwei Dai, Fangjin Chen, Shuaishi Gao, Jianfeng Pei, Luhua Lai. Journal of Chemical Information and Modeling, 2015

324 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/db0f059b80acd2e9e494f42b49c8e6066294866a

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Citations in the corpus

Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models (2017, methodology): “These human-expert features are then used by machine learning algorithms for QSAR and QSPR modeling, which in recent years have also included deep neural networks.(9-23) However, we observed that even in the most recent chemistry literature, deep learning in chemistry still falls under this traditio”
Recent advances and applications of deep learning methods in materials science (2021, methodology): “DL for molecular materials have been used to predict technologically important properties such as aqueous solubility [135] and toxicity [136].”
Deep Learning and Medical Diagnosis: A Review of Literature (2018, unknown): “[28] UGRNN Various medical data sets Drug-induced liver injury (DILI) prediction.”
Development and evaluation of a deep learning model for protein–ligand binding affinity prediction (2017, methodology): “…of deep learning methods have also been shown for computer-aided drug de-sign (CADD) (Lusci et al. , 2013; Dahl et al. , 2014; Ma et al. , 2015; Xu et al. , 2015; Duvenaud et al. , 2015; Kearnes et al. , 2016; Jastrz¸ebski et al. , 2016; Segler et al. , 2017; Olivecrona et al. , 2017;…”
The rise of deep learning in drug discovery. (2018, methodology): “[47] applied the same method to model drug-induced liver injury (DILI).”
Computational Protein Design with Deep Learning Neural Networks (2018, methodology): “…Particularly in computational biology/chemistry, it has been used in protein-ligand scoring, 40-42 protein-protein interaction prediction, 43 protein secondary structure prediction, 44-49 protein contact map prediction, 50-52 and compound toxicity 53,54 and liver injury prediction, 55 among others.”
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, methodology): “This dataset is aggregated from U.S. FDA’s National Center for Toxicological Research and is collected from Xu et al. (2015).”