An ai‐based prediction model for drug‐drug interactions in osteoporosis and Paget's diseases from smiles
The skeleton is one of the most important organs in the human body in assisting our motion
and activities; however, bone density attenuates gradually as we age. Among common bone …
and activities; however, bone density attenuates gradually as we age. Among common bone …
Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties
Abstract Objective Drug–drug interactions (DDIs) are an important consideration in both
drug development and clinical application, especially for co-administered medications …
drug development and clinical application, especially for co-administered medications …
DDI-CPI, a server that predicts drug–drug interactions through implementing the chemical–protein interactome
Drug–drug interactions (DDIs) may cause serious side-effects that draw great attention from
both academia and industry. Since some DDIs are mediated by unexpected drug–human …
both academia and industry. Since some DDIs are mediated by unexpected drug–human …
BioDKG–DDI: predicting drug–drug interactions based on drug knowledge graph fusing biochemical information
The way of co-administration of drugs is a sensible strategy for treating complex diseases
efficiently. Because of existing massive unknown interactions among drugs, predicting …
efficiently. Because of existing massive unknown interactions among drugs, predicting …
Computational prediction of drug target interactions using chemical, biological, and network features
DS Cao, LX Zhang, GS Tan, Z Xiang… - Molecular …, 2014 - Wiley Online Library
Drug target interactions (DTIs) are central to current drug discovery processes. Efforts have
been devoted to the development of methodology for predicting DTIs and drug target …
been devoted to the development of methodology for predicting DTIs and drug target …
A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network
Background The key to modern drug discovery is to find, identify and prepare drug
molecular targets. However, due to the influence of throughput, precision and cost …
molecular targets. However, due to the influence of throughput, precision and cost …
Deepcci: End-to-end deep learning for chemical-chemical interaction prediction
Chemical-chemical interaction (CCI) plays a key role in predicting candidate drugs, toxicity,
therapeutic effects, and biological functions. In various types of chemical analyses …
therapeutic effects, and biological functions. In various types of chemical analyses …
Drug–drug interaction prediction: databases, web servers and computational models
In clinical treatment, two or more drugs (ie drug combination) are simultaneously or
successively used for therapy with the purpose of primarily enhancing the therapeutic …
successively used for therapy with the purpose of primarily enhancing the therapeutic …
Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees
Identifying potential protein-ligand interactions is central to the field of drug discovery as it
facilitates the identification of potential novel drug leads, contributes to advancement from …
facilitates the identification of potential novel drug leads, contributes to advancement from …
DDI-PULearn: a positive-unlabeled learning method for large-scale prediction of drug-drug interactions
Abstract Background Drug-drug interactions (DDIs) are a major concern in patients'
medication. It's unfeasible to identify all potential DDIs using experimental methods which …
medication. It's unfeasible to identify all potential DDIs using experimental methods which …