Boosting compound-protein interaction prediction by deep learning

By Kai Tian, Mingyu Shao, Shuigeng Zhou, Jihong Guan. 2015

164 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

ProDCoNN: Protein design using a convolutional neural network (2019, methodology): “In 2014, Zhou and co-workers developed SPIN (sequence profiles by integrated neural network) based on fragment-derived sequence profiles and structurederived energy profiles.(54,55,57) Both local and nonlocal features were designed and served as input for a two hidden layer neural network, which con”
DeepDTA: deep drug–target binding affinity prediction (2018, background): “A few studies employing Deep Neural Networks (DNN) have already been proposed for DTI binary class prediction using different input models for proteins and drugs [10,26,49] as well as the ones that employ stacked auto-encoders [52] and deep-belief networks [53].”
Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, methodology): “Performance comparison of CPI is shown in Fig.”
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences (2018, methodology): “[23], domain binary vectors were employed to represent the existence of domains used to describe proteins.”
A review on machine learning approaches and trends in drug discovery (2021, methodology): “PubChem Fingerprints are 881-bit long structural keys, which PubChem uses to perform similarity search [44,46].”
NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions (2018, background): “, compound-protein interaction prediction, drug discovery with one-shot learning) (Wang and Zeng, 2013; Wan and Zeng, 2016; Hamanaka et al., 2017; Tian et al., 2016; Altae-Tran et al., 2017; Xu et al., 2017).”
Artificial intelligence in drug discovery and development (2020, unknown): “Various AI methods have been useful in the accurate prediction of ligand–protein interactions, ensuring better therapeutic efﬁcacy [55,59].”