reduction technique used in various fields, including molecular systems. Unlike PCA, the dimensions obtained with t-SNE do not have a straightforward interpretation, so it is primarily used for visualization or exploratory data analysis. t-SNE maps high-dimensional data into a lower-dimensional space (embedding) based on similarities between data points, calculated using a Gaussian kernel or a Student's t-distribution. It constructs a neighborhood graph where each node is connected to its nearest neighbors, forming local relationships. The parameter perplexity controls the bala nce between preserving local and capturing global structures; higher perplexity values capture global relationships, while lower values emphasize local structure. t-SNE then creates probability distributions for both high-and low-dimensional spaces and uses an optimization algorithm, such as gradient descent, to iteratively adjust the positions of the mapped points until the divergence between the distributions is minimized or the maximum number of iterations is reached. The resulting embedding can be visualized in scatter plots or further analyzed for insights into the data structure.