AIMC Topic: Drug Development
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Generative AI: driving productivity and scientific breakthroughs in pharmaceutical R&D.
Drug discovery today
Dec 13, 2024
The rapid advancement of generative artificial intelligence (AI) is reshaping pharmaceutical research and development (R&D), offering opportunities across drug discovery and development. Generative AI (GenAI) enhances productivity by enabling virtual...
FormulationBCS: A Machine Learning Platform Based on Diverse Molecular Representations for Biopharmaceutical Classification System (BCS) Class Prediction.
Molecular pharmaceutics
Dec 8, 2024
The Biopharmaceutics Classification System (BCS) has facilitated biowaivers and played a significant role in enhancing drug regulation and development efficiency. However, the productivity of measuring the key discriminative properties of BCS, solubi...
Accelerating virtual patient generation with a Bayesian optimization and machine learning surrogate model.
CPT: pharmacometrics & systems pharmacology
Dec 4, 2024
The pharmaceutical industry has increasingly adopted model-informed drug discovery and development (MID3) to enhance productivity in drug discovery and development. Quantitative systems pharmacology (QSP), which integrates drug action mechanisms and ...
The Role of Artificial Intelligence and Machine Learning in Accelerating the Discovery and Development of Nanomedicine.
Pharmaceutical research
Dec 2, 2024
The unique potential of nanomedicine to address challenging health issues is rapidly advancing the field, leading to the generation of more effective products. However, these complex systems often pose several challenges with respect to their design ...
Strategic partnerships for AI-driven drug discovery: The role of relational dynamics.
Drug discovery today
Nov 14, 2024
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...
Prediction of Multi-Pharmacokinetics Property in Multi-Species: Bayesian Neural Network Stacking Model with Uncertainty.
Molecular pharmaceutics
Nov 7, 2024
Pharmacokinetic (PK) properties of a drug are vital attributes influencing its therapeutic effectiveness, playing an important role in the drug development process. Focusing on the difficult task of predicting PK parameters, we compiled an extensive ...
Machine learning driven bioequivalence risk assessment at an early stage of generic drug development.
European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V
Oct 26, 2024
BACKGROUND: Bioequivalence risk assessment as an extension of quality risk management lacks examples of quantitative approaches to risk assessment at an early stage of generic drug development. The aim of our study was to develop a model-based approa...
Unlocking therapeutic frontiers: harnessing artificial intelligence in drug discovery for neurodegenerative diseases.
Drug discovery today
Oct 18, 2024
Neurodegenerative diseases (NDs) pose serious healthcare challenges with limited therapeutic treatments and high social burdens. The integration of artificial intelligence (AI) into drug discovery has emerged as a promising approach to address these ...
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
Oct 16, 2024
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,...
Data-centric challenges with the application and adoption of artificial intelligence for drug discovery.
Expert opinion on drug discovery
Sep 24, 2024
INTRODUCTION: Artificial intelligence (AI) is exhibiting tremendous potential to reduce the massive costs and long timescales of drug discovery. There are however important challenges currently limiting the impact and scope of AI models.
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