AIMC Topic: Drug Development

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MOViDA: multiomics visible drug activity prediction with a biologically informed neural network model.

Bioinformatics (Oxford, England)
MOTIVATION: The process of drug development is inherently complex, marked by extended intervals from the inception of a pharmaceutical agent to its eventual launch in the market. Additionally, each phase in this process is associated with a significa...

[Advances in machine learning for predicting protein functions].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Proteins play a variety of functional roles in cellular activities and are indispensable for life. Understanding the functions of proteins is crucial in many fields such as medicine and drug development. In addition, the application of enzymes in gre...

MFR-DTA: a multi-functional and robust model for predicting drug-target binding affinity and region.

Bioinformatics (Oxford, England)
MOTIVATION: Recently, deep learning has become the mainstream methodology for drug-target binding affinity prediction. However, two deficiencies of the existing methods restrict their practical applications. On the one hand, most existing methods ign...

Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling.

Toxicological sciences : an official journal of the Society of Toxicology
Physiologically based pharmacokinetic (PBPK) models are useful tools in drug development and risk assessment of environmental chemicals. PBPK model development requires the collection of species-specific physiological, and chemical-specific absorptio...

Artificial Intelligence in Drug Formulation and Development: Applications and Future Prospects.

Current drug metabolism
Artificial Intelligence (AI) has emerged as a powerful tool in various domains, and the field of drug formulation and development is no exception. This review article aims to provide an overview of the applications of AI in drug formulation and devel...

Revolutionizing Pharmaceutical Industry: The Radical Impact of Artificial Intelligence and Machine Learning.

Current pharmaceutical design
This article explores the significant impact of artificial intelligence (AI) and machine learning (ML) on the pharmaceutical industry, which has transformed the drug development process. AI and ML technologies provide powerful tools for analysis, dec...

State-of-the-art Application of Artificial Intelligence to Transporter-centered Functional and Pharmaceutical Research.

Current drug metabolism
Protein transporters not only have essential functions in regulating the transport of endogenous substrates and remote communication between organs and organisms, but they also play a vital role in drug absorption, distribution, and excretion and are...

Development of a GCN-based model to predict in vitro phototoxicity from the chemical structure and HOMO-LUMO gap.

The Journal of toxicological sciences
The interaction between sunlight and drugs can lead to phototoxicity in patients who have received such drugs. Phototoxicity assessment is a regulatory requirement globally and one of the main toxicity screening steps in the early stages of drug disc...

A Comparative Analytical Review on Machine Learning Methods in Drugtarget Interactions Prediction.

Current computer-aided drug design
BACKGROUND: Predicting drug-target interactions (DTIs) is an important topic of study in the field of drug discovery and development. Since DTI prediction in vitro studies is very expensive and time-consuming, computational techniques for predict...

Natural History and Real-World Data in Rare Diseases: Applications, Limitations, and Future Perspectives.

Journal of clinical pharmacology
Rare diseases represent a highly heterogeneous group of disorders with high phenotypic and genotypic diversity within individual conditions. Due to the small numbers of people affected, there are unique challenges in understanding rare diseases and d...