In this experiment, we employed the Butina clustering methodology to cluster the HMDB dataset. We selected the largest molecule from the 17 largest clusters, and for each chosen molecule we generated 10 analogues using a molecule generator transformer networ k with the matched molecule pairs (MMP) setting. We implemented an additional weight (±1) and Tanimoto similarity filter (≥0.6) to ensure that the generated molecules were not structurally too distinct. This allowed us to prevent the generation of analogues that had d ramatically different structures compared to their original counterparts, while still allowing for some variation in structures. The maximum number of trials for the transformer network was set to 100.000 which succeeded in generating 10 different variations for nearly all mo lecules. We then used the generated molecules, including the correct structure, with each simulation technique and matching experiment to identify the accurate molecule from each set of 10 options.