AI-DrugNet: A network-based deep learning model for drug repurposing and combination therapy in neurological disorders

Pan, Xingxin; Yun, Jimmy; Akdemir, Zeynep H. Coban; Jiang, Xiaoqian; Sahni, Nidhi; Wu, Erxi; Huang, Jason H.; Yi, S. Stephen

doi:10.1016/j.csbj.2023.02.004

Cited by 12 publications

(2 citation statements)

References 80 publications

(87 reference statements)

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“…84,252,253 Graph convolutional neural network was utilized by AI-DrugNet to identify drug−target associations. 254 CoV-KGE identified drug candidates using a variety of graph-based feature extraction techniques (RotatE, TransE, ComplEx, DistMult). 249 deepDR identified candidates with scores using a collective VAE and a random-walk-based strategy for network fusion.…”

Section: Network-based Drug Design (Nbdd)mentioning

confidence: 99%

“…Subsequently, unsupervised dimensionality reduction methods, such as multimodal autoencoder (AE), variational autoencoder (VAE), and GNN, were implemented to extract characteristics of drugs, diseases, and their associations from the KG. , These characteristics of the heterogeneous KG were utilized to prepare the data set for training, validation, and testing purposes, as well as ML and DL model construction. Currently, DL strategies are most widely used to process graph-structured data because of their capacity to manage complex network data. ,, Graph convolutional neural network was utilized by AI-DrugNet to identify drug–target associations . CoV-KGE identified drug candidates using a variety of graph-based feature extraction techniques (RotatE, TransE, ComplEx, DistMult) .…”

Section: Rational Drug Design Technologiesmentioning

confidence: 99%

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Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Nandi,

Bhaduri,

Das

et al. 2024

Mol. Pharmaceutics

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Understanding protein sequence and structure is essential for understanding protein−protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure−activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein−protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

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