AIMC Topic: Drug Discovery

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Improvement of multi-task learning by data enrichment: application for drug discovery.

Journal of computer-aided molecular design Mar 21, 2023
Multi-task learning in deep neural networks has become a topic of growing importance in many research fields, including drug discovery. However, applying multi-task learning poses new challenges in improving prediction performance. This study investi...
Neural Networks, Computer Drug Discovery
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AlphaFold, allosteric, and orthosteric drug discovery: Ways forward.

Drug discovery today Mar 11, 2023
Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrate...
Artificial Intelligence Drug Design Drug Discovery Proteins Allosteric Site Allosteric Regulation
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QuantumTox: Utilizing quantum chemistry with ensemble learning for molecular toxicity prediction.

Computers in biology and medicine Mar 9, 2023
Molecular toxicity prediction plays an important role in drug discovery, which is directly related to human health and drug fate. Accurately determining the toxicity of molecules can help weed out low-quality molecules in the early stage of drug disc...
Humans Machine Learning Algorithms Drug Discovery
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Applications and prospects of cryo-EM in drug discovery.

Military Medical Research Mar 6, 2023
Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time- and effort-consuming. Structural biology has been demonstrated as a powerful tool to...
Humans Artificial Intelligence Quality of Life Drug Discovery Cryoelectron Microscopy Proteolysis Targeting Chimera
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BCM-DTI: A fragment-oriented method for drug-target interaction prediction using deep learning.

Computational biology and chemistry Mar 5, 2023
The identification of drug-target interaction (DTI) is significant in drug discovery and development, which is usually of high cost in time and money due to large amount of molecule and protein space. The application of deep learning in predicting DT...
Drug Delivery Systems Drug Discovery Deep Learning Drug Development Proteins
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An approach combining deep learning and molecule docking for drug discovery of cathepsin L.

Expert opinion on drug discovery Feb 27, 2023
OBJECTIVES: Cathepsin L (CTSL) is a promising therapeutic target for metabolic disorders and COVID-19. However, there are still no clinically available CTSL inhibitors. Our objective is to develop an approach for the discovery of potential reversible...
Humans COVID-19 Drug Discovery Deep Learning Zinc Cathepsin L
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Structure-based drug design with geometric deep learning.

Current opinion in structural biology Feb 24, 2023
Structure-based drug design uses three-dimensional geometric information of macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric deep learning, an emerging concept of neural-network-based machine learning, has be...
Neural Networks, Computer Machine Learning Drug Design Drug Discovery Deep Learning Ligands
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Strategy toward Kinase-Selective Drug Discovery.

Journal of chemical theory and computation Feb 23, 2023
Kinase drug selectivity is the ground challenge in cancer research. Due to the structurally similar kinase drug pockets, off-target inhibitor toxicity has been a major cause for clinical trial failures. The pockets are similar but not identical. Here...
Humans Artificial Intelligence Drug Discovery Computational Biology Protein Kinase Inhibitors
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Machine learning approaches to predict drug efficacy and toxicity in oncology.

Cell reports methods Feb 21, 2023
In recent years, there has been a surge of interest in using machine learning algorithms (MLAs) in oncology, particularly for biomedical applications such as drug discovery, drug repurposing, diagnostics, clinical trial design, and pharmaceutical pro...
Artificial Intelligence Machine Learning Algorithms Drug Discovery Medical Oncology
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Federated learning for molecular discovery.

Current opinion in structural biology Feb 16, 2023
Federated Learning enables machine learning across multiple sources of data and alleviates the risk of leaking private information between partners thereby encouraging knowledge sharing and collaborative modelling. Hence, Federated Learning opens the...
Machine Learning Drug Discovery
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