AIMC Topic: Drug Development

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In Silico Clinical Trials: Is It Possible?

Methods in molecular biology (Clifton, N.J.)
Modeling and simulation (M&S), including in silico (clinical) trials, helps accelerate drug research and development and reduce costs and have coined the term "model-informed drug development (MIDD)." Data-driven, inferential approaches are now becom...

AI-Driven Enhancements in Drug Screening and Optimization.

Methods in molecular biology (Clifton, N.J.)
The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, qua...

Recent Deep Learning Applications to Structure-Based Drug Design.

Methods in molecular biology (Clifton, N.J.)
Identification and optimization of small molecules that bind to and modulate protein function is a crucial step in the early stages of drug development. For decades, this process has benefitted greatly from the use of computational models that can pr...

Attention is all you need: utilizing attention in AI-enabled drug discovery.

Briefings in bioinformatics
Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the pri...

NG-DTA: Drug-target affinity prediction with n-gram molecular graphs.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Drug-target affinity (DTA) prediction is crucial to speed up drug development. The advance in deep learning allows accurate DTA prediction. However, most deep learning methods treat protein as a 1D string which is not informative to models compared t...

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...