AIMC Topic: Pharmaceutical Preparations

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MutualDTA: An Interpretable Drug-Target Affinity Prediction Model Leveraging Pretrained Models and Mutual Attention.

Journal of chemical information and modeling Jan 29, 2025
Efficient and accurate drug-target affinity (DTA) prediction can significantly accelerate the drug development process. Recently, deep learning models have been widely applied to DTA prediction and have achieved notable success. However, existing met...
Humans Binding Sites Drug Discovery Proteins Deep Learning Pharmaceutical Preparations Protein Binding
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MSMDL-DDI: Multi-Layer Soft Mask Dual-View Learning for Drug-Drug Interactions.

Computational biology and chemistry Jan 23, 2025
Drug-drug interactions (DDIs) occur when multiple medications are co-administered, potentially leading to adverse effects and compromising patient safety. However, existing DDI prediction methods often overlook the intricate interactions among chemic...
Pharmaceutical Preparations Drug Interactions Neural Networks, Computer Machine Learning Humans
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MMPD-DTA: Integrating Multi-Modal Deep Learning with Pocket-Drug Graphs for Drug-Target Binding Affinity Prediction.

Journal of chemical information and modeling Jan 20, 2025
Predicting drug-target binding affinity (DTA) is a crucial task in drug discovery research. Recent studies have demonstrated that pocket features and interactions between targets and drugs significantly improve the understanding of DTA. However, chal...
Binding Sites Proteins Pharmaceutical Preparations Protein Binding Humans Drug Discovery Deep Learning
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Active learning and Gaussian processes for the development of dissolution models: An AI-based data-efficient approach.

Journal of controlled release : official journal of the Controlled Release Society Jan 13, 2025
In vitro dissolution testing plays a key role in controlling the quality and optimizing the formulation of solid dosage pharmaceutical products. Data-driven dissolution models can improve the efficiency of testing: their predictions can act as surrog...
Artificial Intelligence Solubility Drug Liberation Machine Learning Pharmaceutical Preparations Normal Distribution
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Progress of machine learning in the application of small molecule druggability prediction.

European journal of medicinal chemistry Jan 10, 2025
Machine learning (ML) has become an important tool for predicting the pharmaceutical properties of small molecules. Recent advancements in ML algorithms enable the rapid and accurate evaluation of solubility, activity, toxicity, pharmacokinetics, and...
Small Molecule Libraries Molecular Structure Pharmaceutical Preparations Humans Machine Learning Drug Discovery
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Machine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs.

Scientific reports Jan 4, 2025
We have adopted the classification Read-Across Structure-Activity Relationship (c-RASAR) approach in the present study for machine-learning (ML)-based model development from a recently reported curated dataset of nephrotoxicity potential of orally ac...
Quantitative Structure-Activity Relationship Kidney Machine Learning Pharmaceutical Preparations Kidney Diseases Algorithms Humans Administration, Oral
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Drug molecular representations for drug response predictions: a comprehensive investigation via machine learning methods.

Scientific reports Jan 2, 2025
The integration of drug molecular representations into predictive models for Drug Response Prediction (DRP) is a standard procedure in pharmaceutical research and development. However, the comparative effectiveness of combining these representations ...
Machine Learning Precision Medicine Humans Drug Repositioning Pharmaceutical Preparations Drug Discovery Deep Learning
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Interpretable Deep-Learning p Prediction for Small Molecule Drugs via Atomic Sensitivity Analysis.

Journal of chemical information and modeling Dec 30, 2024
Machine learning (ML) models now play a crucial role in predicting properties essential to drug development, such as a drug's logscale acid-dissociation constant (p). Despite recent architectural advances, these models often generalize poorly to nove...
Small Molecule Libraries Neural Networks, Computer Deep Learning Pharmaceutical Preparations
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Predicting patients' sentiments about medications using artificial intelligence techniques.

Scientific reports Dec 30, 2024
The increasing development of technology has led to the increase of digital data in various fields, such as medication-related texts. Sentiment Analysis (SA) in medication is essential to give clinicians insights into patients' feedback about the tre...
Natural Language Processing Pharmaceutical Preparations Algorithms Deep Learning Humans Artificial Intelligence Machine Learning
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How can language models assist with pharmaceuticals manufacturing deviations and investigations?

International journal of pharmaceutics Dec 18, 2024
Large Language Models (LLM) such as the Generative-Pretrained-Transformer (GPT) and Large-Language-Model-Meta-AI (LLaMA) have attracted much attention. There is strong evidence that these models perform remarkably well in various natural language pro...
Drug Industry Natural Language Processing Artificial Intelligence Models, Theoretical Pharmaceutical Preparations
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