BACKGROUND: Systems approaches to studying drug-side-effect (drug-SE) associations are emerging as an active research area for both drug target discovery and drug repositioning. However, a comprehensive drug-SE association knowledge base does not exi...
BACKGROUND: Ontology-based enrichment analysis aids in the interpretation and understanding of large-scale biological data. Ontologies are hierarchies of biologically relevant groupings. Using ontology annotations, which link ontology classes to biol...
High-throughput compound screening is time and resource consuming, and considerable effort is invested into screening compound libraries, profiling, and selecting the most promising candidates for further testing. Active-learning methods assist the s...
The amount of biomedical literature has been increasing rapidly during the last decade. Text mining techniques can harness this large-scale data, shed light onto complex drug mechanisms, and extract relation information that can support computational...
Bioorganic & medicinal chemistry letters
Nov 7, 2014
The great majority of molecular modeling tasks require the construction of a model that is then used to evaluate new compounds. Although various types of these models exist, at some stage, they all use knowledge about the activity of a given group of...
MOTIVATION: Identifying drug-target interactions (DTIs) is a crucial step in drug repurposing and drug discovery. The significant increase in demand and the expensive nature for experimentally identifying DTIs necessitate computational tools for auto...
The accurate categorization of compounds within the anatomical therapeutic chemical (ATC) system is fundamental for drug development and fundamental research. Although this area has garnered significant research focus for over a decade, the majority ...
Identifying interactions between drugs and targets is crucial for drug discovery and development. Nevertheless, the determination of drug-target binding affinities (DTAs) through traditional experimental methods is a time-consuming process. Conventio...
Drug Delivery Systems (DDS) have been developed to address the challenges associated with traditional drug delivery methods. These DDS aim to improve drug administration, enhance patient compliance, reduce side effects, and optimize target therapy. T...
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2025
Machine learning (ML) has increasingly been applied to predict properties of drugs. Particularly, metabolism can be predicted with ML methods, which can be exploited during drug discovery and development. The prediction of metabolism is a crucial bot...