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Drug Development

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Unleashing the power of generative AI in drug discovery.

Drug discovery today
Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de...

[The revolution of AI in drug development].

Medecine sciences : M/S
Artificial intelligence and machine learning enable the construction of predictive models, which are currently used to assist in decision-making throughout the process of drug discovery and development. These computational models can be used to repre...

Leveraging Modeling and Simulation to Enhance the Efficiency of Bioequivalence Approaches for Generic Drugs: Highlights from the 2023 Generic Drug Science and Research Initiatives Public Workshop.

The AAPS journal
The 2023 Generic Drug Science and Research Initiative Public Workshop organized by the U.S. Food and Drug Administration (FDA) discussed the research needs to improve and enhance bioequivalence (BE) approaches for generic drug development. FDA takes ...

Towards explainable interaction prediction: Embedding biological hierarchies into hyperbolic interaction space.

PloS one
Given the prolonged timelines and high costs associated with traditional approaches, accelerating drug development is crucial. Computational methods, particularly drug-target interaction prediction, have emerged as efficient tools, yet the explainabi...

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine.

Expert opinion on drug metabolism & toxicology
INTRODUCTION: Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hamp...

Deep-NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data.

CPT: pharmacometrics & systems pharmacology
Noncompartmental analysis (NCA) is a model-independent approach for assessing pharmacokinetics (PKs). Although the existing NCA algorithms are very well-established and widely utilized, they suffer from low accuracies in the setting of sparse PK samp...

Drug target prediction through deep learning functional representation of gene signatures.

Nature communications
Many machine learning applications in bioinformatics currently rely on matching gene identities when analyzing input gene signatures and fail to take advantage of preexisting knowledge about gene functions. To further enable comparative analysis of O...

Transforming drug development with synthetic biology and AI.

Trends in biotechnology
The COVID-19 pandemic has thrust RNA as a platform for drug development into the spotlight. However, identifying promising drug candidates is challenging. With advances in synthetic biology and artificial intelligence (AI) models, we can overcome thi...