AIMC Topic: Drug Discovery

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miDruglikeness: Subdivisional Drug-Likeness Prediction Models Using Active Ensemble Learning Strategies.

Biomolecules Dec 23, 2022
The drug development pipeline involves several stages including in vitro assays, in vivo assays, and clinical trials. For candidate selection, it is important to consider that a compound will successfully pass through these stages. Using graph neural...
Drug Discovery Benchmarking Drug Development Neural Networks, Computer Machine Learning
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Improved compound-protein interaction site and binding affinity prediction using self-supervised protein embeddings.

BMC bioinformatics Dec 16, 2022
BACKGROUND: Compound-protein interaction site and binding affinity predictions are crucial for drug discovery and drug design. In recent years, many deep learning-based methods have been proposed for predications related to compound-protein interacti...
Drug Discovery Proteins Neural Networks, Computer Binding Sites Amino Acid Sequence
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RMSCNN: A Random Multi-Scale Convolutional Neural Network for Marine Microbial Bacteriocins Identification.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2022
The abuse of traditional antibiotics has led to an increase in the resistance of bacteria and viruses. Similar to the function of antibacterial peptides, bacteriocins are more common as a kind of peptides produced by bacteria that have bactericidal o...
Peptides Bacteria Drug Discovery Aquatic Organisms Neural Networks, Computer Sequence Analysis, DNA Bacteriocins Anti-Bacterial Agents
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A Novel Method for Inferring Chemical Compounds With Prescribed Topological Substructures Based on Integer Programming.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2022
Drug discovery is one of the major goals of computational biology and bioinformatics. A novel framework has recently been proposed for the design of chemical graphs using both artificial neural networks (ANNs) and mixed integer linear programming (MI...
Programming, Linear Algorithms Neural Networks, Computer Computational Biology Drug Discovery
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Structural Analysis and Prediction of Hematotoxicity Using Deep Learning Approaches.

Journal of chemical information and modeling Dec 6, 2022
Hematotoxicity has been becoming a serious but overlooked toxicity in drug discovery. However, only a few models have been reported for the prediction of hematotoxicity. In this study, we constructed a high-quality dataset comprising 759 hematotoxic...
Machine Learning Deep Learning Computer Simulation Drug Discovery Algorithms
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Exposing the Limitations of Molecular Machine Learning with Activity Cliffs.

Journal of chemical information and modeling Dec 1, 2022
Machine learning has become a crucial tool in drug discovery and chemistry at large, , to predict molecular properties, such as bioactivity, with high accuracy. However, activity cliffs─pairs of molecules that are highly similar in their structure bu...
Algorithms Structure-Activity Relationship Machine Learning Models, Molecular Drug Discovery
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Exploration of Chemical Space Guided by PixelCNN for Fragment-Based De Novo Drug Discovery.

Journal of chemical information and modeling Dec 1, 2022
We report a novel framework for achieving fragment-based molecular design using pixel convolutional neural network (PixelCNN) combined with the simplified molecular input line entry system (SMILES) as molecular representation. While a widely used rec...
Drug Discovery Molecular Structure Drug Design Neural Networks, Computer
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Antibiotic discovery in the artificial intelligence era.

Annals of the New York Academy of Sciences Nov 29, 2022
As the global burden of antibiotic resistance continues to grow, creative approaches to antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly progressing computational revolution-artificial intelligence-offers an...
Anti-Bacterial Agents Artificial Intelligence Humans Drug Resistance, Microbial Drug Discovery Machine Learning
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Few-Shot Learning for Low-Data Drug Discovery.

Journal of chemical information and modeling Nov 21, 2022
The discovery of new hits through ligand-based virtual screening in drug discovery is essentially a low-data problem, as data acquisition is both difficult and expensive. The requirement for large amounts of training data hinders the application of c...
Machine Learning Drug Discovery Neural Networks, Computer Ligands
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Deep graph level anomaly detection with contrastive learning.

Scientific reports Nov 18, 2022
Graph level anomaly detection (GLAD) aims to spot anomalous graphs that structure pattern and feature information are different from most normal graphs in a graph set, which is rarely studied by other researchers but has significant application value...
Proteins Neural Networks, Computer Drug Discovery
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