Table 3.

List of commonly explored AI models in pharmaceutical product development.

AI/Machine Learning Models	Description/Usage	References
Genetic Algorithms	Genetic algorithms are optimization techniques inspired by the principles of natural selection and genetics. They can be applied to optimize formulation compositions, drug release profiles, and process parameters to achieve desired dosage form characteristics.	[95]
Artificial Neural Networks (ANNs)	ANNs have been employed to model and optimize drug release kinetics from different dosage forms. They can assist in determining optimal formulations and predict the release behavior of active pharmaceutical ingredients (APIs) under various conditions.	[96]
Support Vector Machines (SVMs)	SVMs have been used in dosage form optimization to predict and model relationships between formulation variables, such as excipient composition, processing parameters, and drug release profiles. They aid in optimizing formulation design space.	[97]
Particle Swarm Optimization (PSO)	PSO is a population-based optimization algorithm that can be used for dosage form optimization. It has been applied to optimize particle size distribution, dissolution profiles, and other formulation parameters.	[98]
Artificial Intelligence-based Expert Systems	Expert systems utilize AI techniques, including rule-based systems and fuzzy logic, to simulate the decision-making process of human experts. They can be applied to dosage form optimization by considering multiple formulation and process variables.	[99]
Monte Carlo Simulation	Monte Carlo simulation methods have been used to optimize drug product performance by considering uncertainties and variability in formulation and process parameters. They aid in robust formulation and process design.	[100]
Computational Fluid Dynamics (CFD)	CFD simulations enable the optimization of fluid flow and mixing within dosage form manufacturing processes, such as granulation, coating, and drying. They help in designing efficient and uniform processes.	[101,102]
Response Surface Methodology (RSM)	RSM is a statistical technique that helps optimize dosage form formulations by modeling and analyzing the relationship between multiple variables and their effect on formulation responses. It aids in understanding and optimizing formulation parameters.	[103,104,105]
Artificial Neural Network–Genetic Algorithm (ANN-GA) Hybrid Models	Hybrid models combining ANN and GA techniques have been used for dosage form optimization. They can efficiently search the formulation space to identify optimal solutions and predict formulation characteristics.
Multivariate Analysis Techniques	Multivariate analysis methods, such as principal component analysis (PCA) and partial least squares (PLS), have been employed in dosage form optimization. They aid in identifying critical formulation variables, reducing dimensionality, and optimizing formulation performance.	[106,107,108]