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Drug Industry

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Strategic partnerships for AI-driven drug discovery: The role of relational dynamics.

Drug discovery today
Artificial intelligence-driven drug discovery (AIDD) companies hold significant promise for transforming pharmaceutical development, yet little is known about how they manage partnerships with established pharmaceutical firms. To address this researc...

How can language models assist with pharmaceuticals manufacturing deviations and investigations?

International journal of pharmaceutics
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...

Artificial intelligence-driven pharmaceutical industry: A paradigm shift in drug discovery, formulation development, manufacturing, quality control, and post-market surveillance.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
The advent of artificial intelligence (AI) has catalyzed a profound transformation in the pharmaceutical industry, ushering in a paradigm shift across various domains, including drug discovery, formulation development, manufacturing, quality control,...

Role of Artificial Intelligence in Drug Discovery to Revolutionize the Pharmaceutical Industry: Resources, Methods and Applications.

Recent patents on biotechnology
Traditional drug discovery methods such as wet-lab testing, validations, and synthetic techniques are time-consuming and expensive. Artificial Intelligence (AI) approaches have progressed to the point where they can have a significant impact on the d...

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...

Innovative Approaches in Regulatory Affairs: Leveraging Artificial Intelligence and Machine Learning for Efficient Compliance and Decision-Making.

The AAPS journal
Artificial Intelligence (AI) and AI-driven technologies are transforming industries across the board, with the pharmaceutical sector emerging as a frontrunner beneficiary. This article explores the growing impact of AI and Machine Learning (ML) withi...

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...

AI-Driven Analysis of Drug Marketing Efficiency: Unveiling FDA Approval to Market Release Dynamics.

The AAPS journal
This paper explores a novel approach using generative AI to enhance drug marketing strategies in the US pharmaceutical sector. By leveraging an official dataset sourced from the US government, the AI generates Python code to analyze the time interval...

Recommendations for Artificial Intelligence Application in Continued Process Verification: A Journey Toward the Challenges and Benefits of AI in the Biopharmaceutical Industry.

PDA journal of pharmaceutical science and technology
This review paper explores the transformative impact of Artificial Intelligence (AI) on Continued Process Verification (CPV) in the biopharmaceutical industry. Originating from the CPV of the Future project, the study investigates the challenges and ...

The Role of Artificial Intelligence in Drug Discovery and Pharmaceutical Development: A Paradigm Shift in the History of Pharmaceutical Industries.

AAPS PharmSciTech
In today's world, with an increasing patient population, the need for medications is increasing rapidly. However, the current practice of drug development is time-consuming and requires a lot of investment by the pharmaceutical industries. Currently,...