To mitigate the impact of false-negative associations caused by negative sampling of DPPs, the proposed SENT-DTI method is inspired by the advantage of negative training (NT) strategy on identification of false-negative samples and design a novel NT strategy that adaptively learns the probability distribution of known DPP features by incorporating a unified high-confidence false-negative association filtering mechanism into a negative loss objective function.
IHLO-DTI, a novel prediction model based on an improved hypergraph neural network and Laplacian matrix optimization, can effectively capture high-order many-to-many interactions between drugs and targets, improving prediction accuracy and robustness.
Guolongwei Dai, Tao Luo, Dan-Dan Li et al.· ACS Synthetic Biology· 0 citations
This review systematically summarizes the latest developments in heterogeneous graph neural networks, protein language models, and generative artificial intelligence, pointing out the problems currently being addressed in research such as data sparsity and cold start as well as the manifestations of general machine lea...
Qi-Zhong Yang· ITM Web of Conferences· 0 citations
This paper proposes TextDTI, a multimodal framework that simultaneously exploits sequential and structural representations and enhances feature alignment through adversarial learning and contrastive loss, resulting in robust and high-performance DTI prediction.
Jia-Qi Deng, Senyu Tang, Ji-Jun Tang et al.· Journal of Chemical Informat...· 0 citations
SGTL-DDA is proposed, a novel graph transformer framework designed to incorporate structural information and domain-specific knowledge from heterogeneous biological information networks (HBINs) that successfully identifies both known therapeutics and novel repositioning candidates, supported by molecular docking result...
Bowei Zhao, Hui Zhao, Yu-An Huang et al.· IEEE transactions on computa...· 0 citations
A multi-modal deep learning framework to predict drug-target affinity by integrating sequence semantics with graph structural information and design a new symmetric dual cross-attention fusion mechanism for drugs and targets.
Wei Lan, Tian Huang, Guohang He et al.· IEEE journal of biomedical a...· 0 citations
By integrating disease-specific context into molecular generation, DrugGen-2 advances AI-assisted drug discovery, offering a powerful tool for de novo design and drug repurposing that accounts for the complex interplay between diseases and molecular targets.
Ali Motahharynia, Mohammadreza Ghaffarzadeh-Esfahani, Mahsa Sheikholeslami et al.· 0 citations
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