AIMC Topic: Drug Discovery

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Application of Machine Learning Technology in the Prediction of ADME- Related Pharmacokinetic Parameters.

Current medicinal chemistry
BACKGROUND: As an important determinant in drug discovery, the accurate analysis and acquisition of pharmacokinetic parameters are very important for the clinical application of drugs. At present, the research and development of new drugs mainly obta...

A review of biomedical datasets relating to drug discovery: a knowledge graph perspective.

Briefings in bioinformatics
Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graph...

FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction.

Briefings in bioinformatics
Accurate prediction of molecular properties, such as physicochemical and bioactive properties, as well as ADME/T (absorption, distribution, metabolism, excretion and toxicity) properties, remains a fundamental challenge for molecular design, especial...

MGPLI: exploring multigranular representations for protein-ligand interaction prediction.

Bioinformatics (Oxford, England)
MOTIVATION: The capability to predict the potential drug binding affinity against a protein target has always been a fundamental challenge in silico drug discovery. The traditional experiments in vitro and in vivo are costly and time-consuming which ...

Combined docking and machine learning identify key molecular determinants of ligand pharmacological activity on β2 adrenoceptor.

Pharmacology research & perspectives
G protein-coupled receptors (GPCRs) are valuable therapeutic targets for many diseases. A central question of GPCR drug discovery is to understand what determines the agonism or antagonism of ligands that bind them. Ligands exert their action via the...

A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia.

Briefings in bioinformatics
Nowadays, the complexity of disease mechanisms and the inadequacy of single-target therapies in restoring the biological system have inevitably instigated the strategy of multi-target therapeutics with the analysis of each target individually. Howeve...

ComABAN: refining molecular representation with the graph attention mechanism to accelerate drug discovery.

Briefings in bioinformatics
An unsolved challenge in developing molecular representation is determining an optimal method to characterize the molecular structure. Comprehension of intramolecular interactions is paramount toward achieving this goal. In this study, ComABAN, a new...

IIFDTI: predicting drug-target interactions through interactive and independent features based on attention mechanism.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying drug-target interactions is a crucial step for drug discovery and design. Traditional biochemical experiments are credible to accurately validate drug-target interactions. However, they are also extremely laborious, time-consu...