Machine Learning

Abstract Oral dosage form development still relies heavily on empirical trial-and-error, while the high prevalence of poorly soluble drug candidates...

Abstract The rapid development of artificial intelligence (AI) and the increasing demand for the modernization of Chinese herbal medicine (CHM)...

Abstract Pharmaceutical formulations comprise active pharmaceutical ingredients (APIs) and excipients, the latter of which can significantly influence drug stability. Compatibility...

Abstract Background Fast-disintegrating tablets (FDTs) are widely used oral dosage forms in which disintegration time is a critical quality attribute...

Abstract Background/Objectives: Tablet development requires simultaneous optimization of multiple quality attributes under limited experimental budgets, yet formulation–property relationships are highly nonlinear...

Abstract Machine learning (ML) is expected to accelerate the developments of three-dimensional (3D) printed medicines. Despite ML’s potential, the need...

Abstract The presented work investigates critical aspects of rational formulation design through machine learning (ML) methodology to identify essential patterns...

Abstract Nose-to-brain drug delivery via nasal sprays is severely limited by low olfactory deposition. In this study, we integrate machine...

Abstract Creating high-quality datasets for training machine learning models in specialized domains like pharmaceutical research is often constrained by the...

Abstract Since Pfizer developed the mRNA vaccine for COVID-19 by leveraging artificial intelligence (AI) for designing the vaccine, integrating AI...