AIMC Topic: Drug Discovery

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A guide to artificial intelligence for cancer researchers.

Nature reviews. Cancer May 16, 2024
Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to a readily accessible tool for cancer researchers. AI-based tools can boost research productivity in daily workflows, but can also extract hidden informati...
Biomedical Research Neoplasms Artificial Intelligence Software Humans Natural Language Processing Drug Discovery Research Personnel Image Processing, Computer-Assisted
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Guided Docking as a Data Generation Approach Facilitates Structure-Based Machine Learning on Kinases.

Journal of chemical information and modeling May 15, 2024
Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While including 3D structural information is considered beneficial, structural models are hindered by the availability of protein-ligand ...
Protein Binding Protein Kinases Protein Kinase Inhibitors Ligands Phosphotransferases Protein Conformation Molecular Docking Simulation Drug Discovery Neural Networks, Computer Machine Learning
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Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank.

Journal of chemical information and modeling May 13, 2024
Personalized cancer treatment requires a thorough understanding of complex interactions between drugs and cancer cell lines in varying genetic and molecular contexts. To address this, high-throughput screening has been used to generate large-scale dr...
Drug Discovery Antineoplastic Agents Cell Line, Tumor Humans Neural Networks, Computer
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Quantifying the Hardness of Bioactivity Prediction Tasks for Transfer Learning.

Journal of chemical information and modeling May 13, 2024
Today, machine learning methods are widely employed in drug discovery. However, the chronic lack of data continues to hamper their further development, validation, and application. Several modern strategies aim to mitigate the challenges associated w...
Drug Discovery Machine Learning Humans
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Lessons learnt from machine learning in early stages of drug discovery.

Expert opinion on drug discovery May 10, 2024
Machine Learning Drug Discovery Humans Drug Development
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Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules.

Expert opinion on drug discovery May 10, 2024
INTRODUCTION: Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structur...
Pharmacokinetics Drug Discovery Drug Development Machine Learning Pharmaceutical Preparations Humans Animals Models, Biological
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Emerging opportunities of using large language models for translation between drug molecules and indications.

Scientific reports May 10, 2024
A drug molecule is a substance that changes an organism's mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for treating a particular medical condition. While the Large Language Model (L...
Artificial Intelligence Drug Discovery Pharmaceutical Preparations Humans
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MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery.

Nature computational science May 10, 2024
Large language models have greatly enhanced our ability to understand biology and chemistry, yet robust methods for structure-based drug discovery, quantum chemistry and structural biology are still sparse. Precise biomolecule-ligand interaction data...
Molecular Dynamics Simulation Quantum Theory Drug Discovery Proteins Ligands Machine Learning
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Prediction of Drug-Target Affinity Using Attention Neural Network.

International journal of molecular sciences May 8, 2024
Studying drug-target interactions (DTIs) is the foundational and crucial phase in drug discovery. Biochemical experiments, while being the most reliable method for determining drug-target affinity (DTA), are time-consuming and costly, making it chall...
Drug Discovery Deep Learning Pharmaceutical Preparations Neural Networks, Computer Humans Algorithms
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GraphEGFR: Multi-task and transfer learning based on molecular graph attention mechanism and fingerprints improving inhibitor bioactivity prediction for EGFR family proteins on data scarcity.

Journal of computational chemistry May 7, 2024
The proteins within the human epidermal growth factor receptor (EGFR) family, members of the tyrosine kinase receptor family, play a pivotal role in the molecular mechanisms driving the development of various tumors. Tyrosine kinase inhibitors, key c...
Neural Networks, Computer Machine Learning Humans ErbB Receptors Protein Kinase Inhibitors Drug Discovery Deep Learning
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