AIMC Topic: Drug Discovery
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HimGNN: a novel hierarchical molecular graph representation learning framework for property prediction.
Briefings in bioinformatics
Sep 20, 2023
Accurate prediction of molecular properties is an important topic in drug discovery. Recent works have developed various representation schemes for molecular structures to capture different chemical information in molecules. The atom and motif can be...
CMMS-GCL: cross-modality metabolic stability prediction with graph contrastive learning.
Bioinformatics (Oxford, England)
Aug 1, 2023
MOTIVATION: Metabolic stability plays a crucial role in the early stages of drug discovery and development. Accurately modeling and predicting molecular metabolic stability has great potential for the efficient screening of drug candidates as well as...
iGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network.
Bioinformatics (Oxford, England)
Aug 1, 2023
MOTIVATION: The task of predicting drug-target interactions (DTIs) plays a significant role in facilitating the development of novel drug discovery. Compared with laboratory-based approaches, computational methods proposed for DTI prediction are pref...
Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction.
Briefings in bioinformatics
Jul 20, 2023
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect...
Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery.
Briefings in bioinformatics
Jul 20, 2023
Computational analysis of RNA sequences constitutes a crucial step in the field of RNA biology. As in other domains of the life sciences, the incorporation of artificial intelligence and machine learning techniques into RNA sequence analysis has gain...
A Review on Artificial Intelligence Approaches and Rational Approaches in Drug Discovery.
Current pharmaceutical design
Jun 6, 2023
Artificial intelligence (AI) speeds up the drug development process and reduces its time, as well as the cost which is of enormous importance in outbreaks such as COVID-19. It uses a set of machine learning algorithms that collects the available data...
GraphscoreDTA: optimized graph neural network for protein-ligand binding affinity prediction.
Bioinformatics (Oxford, England)
Jun 1, 2023
MOTIVATION: Computational approaches for identifying the protein-ligand binding affinity can greatly facilitate drug discovery and development. At present, many deep learning-based models are proposed to predict the protein-ligand binding affinity an...
A Review on Deep Learning-driven Drug Discovery: Strategies, Tools and Applications.
Current pharmaceutical design
May 19, 2023
It takes an average of 10-15 years to uncover and develop a new drug, and the process is incredibly time-consuming, expensive, difficult, and ineffective. In recent years the dramatic changes in the field of artificial intelligence (AI) have helped t...
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