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Drug Development

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Integrating Model-Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation.

Clinical and translational science
The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate in...

Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives.

Current drug targets
The applications of artificial intelligence (AI) in pharmaceutical sectors have advanced drug discovery and development methods. AI has been applied in virtual drug design, molecule synthesis, advanced research, various screening methods, and decisio...

Machine Learning in Early Prediction of Metabolism of Drugs.

Methods in molecular biology (Clifton, N.J.)
Machine learning (ML) has increasingly been applied to predict properties of drugs. Particularly, metabolism can be predicted with ML methods, which can be exploited during drug discovery and development. The prediction of metabolism is a crucial bot...

Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development.

Clinical and translational science
Despite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models for drug development, effectively interpreting their predictions remains a challenge, which limits their impact on clinical decisions. We address this ...

Increasing acceptance of AI-generated digital twins through clinical trial applications.

Clinical and translational science
Today's approach to medicine requires extensive trial and error to determine the proper treatment path for each patient. While many fields have benefited from technological breakthroughs in computer science, such as artificial intelligence (AI), the ...

Accelerating Drug Development Using Spatial Multi-omics.

Cancer discovery
Spatial biology approaches enabled by innovations in imaging biomarker platforms and artificial intelligence-enabled data integration and analysis provide an assessment of patient and disease heterogeneity at ever-increasing resolution. The utility o...

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications.

Drug development research
The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and exp...

ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization.

Briefings in bioinformatics
Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 5...

Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics.

Current pharmaceutical design
Over the period of the preceding decade, artificial intelligence (AI) has proved an outstanding performance in entire dimensions of science including pharmaceutical sciences. AI uses the concept of machine learning (ML), deep learning (DL), and neura...