AIMC Topic: Drug Discovery

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De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Jan 10, 2024
One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable activity. Traditional drug development typically begins with target selection, but the correlation between targets and disease remains to b...
Machine Learning Proteins Drug Discovery
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Artificial intelligence pushes the boundaries of behavioral analysis in drug discovery: a revolution from the deep.

Expert opinion on drug discovery Jan 8, 2024
Neural Networks, Computer Humans Drug Discovery Artificial Intelligence
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Generative artificial intelligence empowers digital twins in drug discovery and clinical trials.

Expert opinion on drug discovery Jan 8, 2024
INTRODUCTION: The concept of Digital Twins (DTs) translated to drug development and clinical trials describes virtual representations of systems of various complexities, ranging from individual cells to entire humans, and enables in silico simulation...
Artificial Intelligence Computer Simulation Humans Drug Development Drug Discovery Biomedical Research Clinical Trials as Topic
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HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions.

Journal of chemical information and modeling Jan 7, 2024
A central problem in drug discovery is to identify the interactions between drug-like compounds and protein targets. Over the past few decades, various quantitative structure-activity relationship (QSAR) and proteo-chemometric (PCM) approaches have b...
Proteins Drug Development Machine Learning Drug Discovery Neural Networks, Computer
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PatentNetML: A Novel Framework for Predicting Key Compounds in Patents Using Network Science and Machine Learning.

Journal of medicinal chemistry Jan 5, 2024
Patents play a crucial role in drug research and development, providing early access to unpublished data and offering unique insights. Identifying key compounds in patents is essential to finding novel lead compounds. This study collected a comprehen...
Drug Industry Drug Discovery Algorithms Machine Learning
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Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.

Biomolecules Jan 5, 2024
Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the va...
Binding Sites Ligands Quantitative Structure-Activity Relationship Drug Discovery Machine Learning
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T6496 targeting EGFR mediated by T790M or C797S mutant: machine learning, virtual screening and bioactivity evaluation study.

Journal of biomolecular structure & dynamics Jan 4, 2024
Acquired resistance to EGFR is a major impediment in lung cancer treatment, highlighting the urgent need to discover novel compounds to overcome EGFR drug resistance. In this study, we utilized in silico methods and bioactivity evaluation for drug di...
Cell Line, Tumor Protein Binding Antineoplastic Agents Drug Screening Assays, Antitumor Mutation Apoptosis Cell Proliferation Machine Learning Drug Discovery Molecular Docking Simulation Humans ErbB Receptors Protein Kinase Inhibitors Drug Resistance, Neoplasm
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Deep learning algorithms applied to computational chemistry.

Molecular diversity Dec 27, 2023
Recently, there has been a significant increase in the use of deep learning techniques in the molecular sciences, which have shown high performance on datasets and the ability to generalize across data. However, no model has achieved perfect performa...
Computational Chemistry Algorithms Deep Learning Drug Discovery
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Adaptive Transfer of Graph Neural Networks for Few-Shot Molecular Property Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Dec 25, 2023
Few-Shot Molecular Property Prediction (FSMPP) is an improtant task on drug discovery, which aims to learn transferable knowledge from base property prediction tasks with sufficient data for predicting novel properties with few labeled molecules. Its...
Drug Discovery Neural Networks, Computer
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A Deep Learning Approach to the Prediction of Drug Side-Effects on Molecular Graphs.

IEEE/ACM transactions on computational biology and bioinformatics Dec 25, 2023
Predicting drug side effects before they occur is a critical task for keeping the number of drug-related hospitalizations low and for improving drug discovery processes. Automatic predictors of side-effects generally are not able to process the struc...
Neural Networks, Computer Benchmarking Drug-Related Side Effects and Adverse Reactions Deep Learning Humans Drug Discovery
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