The multinomial sampling process incorporates an adaptive mechanism to generate a specified number of unique molecules. It employs an iterative approach that dynamically adjusts the temperature parameter. Starting from an initial value of 1, the temperature is incrementally increased by 0.1 for each iteration if the desired number of unique molecules is not achieved. This process continues until either the target number of unique molecules is generated, the temperature reaches a maximum value of 3, or the iteration count hits a limit of 500. This adaptive strategy ensures a balance between diversity and computational efficiency, while guaranteeing the termination of the sampling process. The improvement cycle is a crucial component of our approach, designed to enhance the MMT model's adaptability to new chemical spaces. By iteratively generating analogs, simulating their spectra, and fine-tuning the model, we aim to improve its performance on previously unseen molecular structures. This process is particularly important for bridging the gap between simulated training data and realworld applications, where novel molecular structures are frequently encountered.