To ensure the robustness of this scheme, one nuance must be addressed. For each X value, the natural products are sorted based on the overall toxicity measure defined by that specific X value. In principle it is possible that a completely different set of 10% of the drugs could consistently show very low toxicity for different X values. If this were the case, selecting the same set of 30,000 natural products and recalculating their overall toxicity for all X values might result in less robust findings. Fortunately, this is not the case. I selected our final set of 30,000 natural products from the sorted list defined by X = 2, then recalculated the average overall toxicity values for all X values for this set. The results are shown in Table3. As one can see, the average overall toxicity values increased only slightly compared to the corresponding values in Table2 (except for X = 2.0, which obviously remains identical), and the results still indicate very low toxicity across all X values. The qualitative conclusion remains robust: this set of natural compounds appears much less toxic than the safest third of approved drugs. This indicates that selecting these natural products, enumerating all their key conformations using a robust tool like our QFConfsearchDFT, establishing QM-grade geometries, and computing ab initio DFT strain energies with an accurate basis set (e.g., def2-TZVP ) and a modern functional like rev-SCAN creates a highly valuable specialized conformational database for ongoing and future research. By identifying potent inhibitors from this database via our QFD docking method, we can be confident that the conformations represent physically feasible, lowenergy states, exhibit strong interactions with the target protein, and are highly likely to be non-toxic.