Deep learning methods for drug response prediction in cancer: predominant and emerging trends
Cancer claims millions of lives yearly worldwide. While many therapies have been made
available in recent years, by in large cancer remains unsolved. Exploiting computational …
available in recent years, by in large cancer remains unsolved. Exploiting computational …
Caster: Predicting drug interactions with chemical substructure representation
Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality.
Identifying potential DDIs during the drug design process is critical for patients and society …
Identifying potential DDIs during the drug design process is critical for patients and society …
DeepTTA: a transformer-based model for predicting cancer drug response
Identifying new lead molecules to treat cancer requires more than a decade of dedicated
effort. Before selected drug candidates are used in the clinic, their anti-cancer activity is …
effort. Before selected drug candidates are used in the clinic, their anti-cancer activity is …
META-DDIE: predicting drug–drug interaction events with few-shot learning
Drug–drug interactions (DDIs) are one of the major concerns in pharmaceutical research,
and a number of computational methods have been developed to predict whether two drugs …
and a number of computational methods have been developed to predict whether two drugs …
Modality-DTA: multimodality fusion strategy for drug–target affinity prediction
Prediction of the drug–target affinity (DTA) plays an important role in drug discovery. Existing
deep learning methods for DTA prediction typically leverage a single modality, namely …
deep learning methods for DTA prediction typically leverage a single modality, namely …
Effects of data quality and quantity on deep learning for protein-ligand binding affinity prediction
FJ Fan, Y Shi - Bioorganic & Medicinal Chemistry, 2022 - Elsevier
Prediction of protein-ligand binding affinities is crucial for computational drug discovery. A
number of deep learning approaches have been developed in recent years to improve the …
number of deep learning approaches have been developed in recent years to improve the …
MDTips: a multimodal-data-based drug–target interaction prediction system fusing knowledge, gene expression profile, and structural data
Motivation Screening new drug–target interactions (DTIs) by traditional experimental
methods is costly and time-consuming. Recent advances in knowledge graphs, chemical …
methods is costly and time-consuming. Recent advances in knowledge graphs, chemical …
Hygnn: Drug-drug interaction prediction via hypergraph neural network
KM Saifuddin, B Bumgardner, F Tanvir… - 2023 IEEE 39th …, 2023 - ieeexplore.ieee.org
Drug-Drug Interactions (DDIs) may hamper the functionalities of drugs, and in the worst
scenario, they may lead to adverse drug reactions (ADRs). Predicting all DDIs is a …
scenario, they may lead to adverse drug reactions (ADRs). Predicting all DDIs is a …
Target-aware molecular graph generation
Generating molecules with desired biological activities has attracted growing attention in
drug discovery. Previous molecular generation models are designed as chemocentric …
drug discovery. Previous molecular generation models are designed as chemocentric …
Integration of computational docking into anti-cancer drug response prediction models
Simple Summary Anti-cancer drug response prediction models aim to reduce the time
necessary for developing a treatment for patients affected by this complex disease. Their …
necessary for developing a treatment for patients affected by this complex disease. Their …