This work introduces the first application of QuantumFuture's Docking (QFD), a novel method that integrates highlevel quantum mechanical (QM) conformational data into conventional docking workflows-to identify potential Indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors. A centerpiece of our strategy is a carefully curated library of about 30,000 natural compounds rigorously screened for low toxicity, ranking them, on average, safer than the least toxic third of approved drugs. By leveraging ab initio DFT-based conformational strain energies, QFD substantially reduces inaccuracies commonly seen in traditional docking methods that rely on less precise conformational searches. We applied QFD to 16 distinct protein targets (eight containing a heme cofactor and eight without), generating comprehensive top hit lists that prioritize both robust binding interactions and minimal predicted toxicity. Visual and computational analyses confirmed that many high-ranking ligands exhibit strong dispersion and hydrogen-bonding interactions in both protein-only and protein-heme systems. Notably, we identified not merely a handful but several dozen likely non-toxic, high-affinity candidates across both scoring functions. Moreover, the parallel use of vinardo and dkoes revealed that each scoring function emphasizes slightly different subsets of low-toxicity compounds, bolstering the overall confidence in our hit lists. Future work will focus on refining these docked complexes with higher-accuracy QM, QM/MM, or advanced MD simulations to capture essential ligand and protein flexibility, particularly in cases involving a heme cofactor. We also encourage experimental collaborators to validate the most promising, nontoxic leads through in vitro and in vivo studies, which could accelerate their development into therapeutics for Alzheimer's disease, Parkinson's disease, and cancer. Looking beyond IDO1, the QFD approach can be readily extended to other disease-relevant targets, promising a broader impact in computational drug discovery.