Attention is all you need: utilizing attention in AI-enabled drug discovery
Recently, attention mechanism and derived models have gained significant traction in drug
development due to their outstanding performance and interpretability in handling complex …
development due to their outstanding performance and interpretability in handling complex …
Recent advances and challenges in protein structure prediction
Artificial intelligence has made significant advances in the field of protein structure prediction
in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated …
in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated …
Design of a modified transformer architecture based on relative position coding
Natural language processing (NLP) based on deep learning provides a positive
performance for generative dialogue system, and the transformer model is a new boost in …
performance for generative dialogue system, and the transformer model is a new boost in …
Designing antimicrobial peptides using deep learning and molecular dynamic simulations
Q Cao, C Ge, X Wang, PJ Harvey… - Briefings in …, 2023 - academic.oup.com
With the emergence of multidrug-resistant bacteria, antimicrobial peptides (AMPs) offer
promising options for replacing traditional antibiotics to treat bacterial infections, but …
promising options for replacing traditional antibiotics to treat bacterial infections, but …
CCL-DTI: contributing the contrastive loss in drug–target interaction prediction
Abstract Background The Drug–Target Interaction (DTI) prediction uses a drug molecule and
a protein sequence as inputs to predict the binding affinity value. In recent years, deep …
a protein sequence as inputs to predict the binding affinity value. In recent years, deep …
Deciphering ligand–receptor-mediated intercellular communication based on ensemble deep learning and the joint scoring strategy from single-cell transcriptomic …
Background: Cell–cell communication in a tumor microenvironment is vital to tumorigenesis,
tumor progression and therapy. Intercellular communication inference helps understand …
tumor progression and therapy. Intercellular communication inference helps understand …
BINDTI: a bi-directional intention network for drug-target interaction identification based on attention mechanisms
L Peng, X Liu, L Yang, L Liu, Z Bai… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
The identification of drug-target interactions (DTIs) is an essential step in drug discovery. In
vitro experimental methods are expensive, laborious, and time-consuming. Deep learning …
vitro experimental methods are expensive, laborious, and time-consuming. Deep learning …
GPCNDTA: prediction of drug-target binding affinity through cross-attention networks augmented with graph features and pharmacophores
Drug-target affinity prediction is a challenging task in drug discovery. The latest
computational models have limitations in mining edge information in molecule graphs …
computational models have limitations in mining edge information in molecule graphs …
[HTML][HTML] A review of deep learning methods for ligand based drug virtual screening
Drug discovery is costly and time consuming, and modern drug discovery endeavors are
progressively reliant on computational methodologies, aiming to mitigate temporal and …
progressively reliant on computational methodologies, aiming to mitigate temporal and …
Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting
The identification of druggable proteins (DPs) is significant for the development of new
drugs, personalized medicine, understanding of disease mechanisms, drug repurposing …
drugs, personalized medicine, understanding of disease mechanisms, drug repurposing …