The models display similar trends with increasing T values for all the analyzed factors across datasets, with varying magnitude (Figure ). In general, the validity decreases with increasing temperature (as previously observed ), with the highest effect observed for GPT (median validity across training setups getting lower than 40%, Figure ). Both S4 and LSTM show higher robustness than GPT to increasing temperature values (with LSTM performing slightly better for T ≥ 1.75), suggesting that sequential generation can boost chemical space exploration. S4 outperforms LSTM in terms of rediscovery rate (Figure ), in agreement with our previous results on bioactivity (Figure ). We also compute the exact rediscovery rate (identical molecular structure) and observe that no model can consistently generate held-out actives. When it comes to the diversity of the designs (Figure ), LSTM can Analysis of the number of diverse groups of scaffolds generated per method. Scaffolds were clustered together if they had a Tanimoto similarity (computed on extended connectivity fingerprints) larger than 60%. For each plot, the solid line indicates the median obtained across the five analyzed protein targets (PKM2, MAPK1, mTORC1, and TP53) and 10 runs, while the shaded area indicates the interquartile range. The statistics per individual target can be found in Figure .