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Abstract This article aims to summarize recent and ongoing efforts to simulate continuous-variable quantum systems using flow-based variational quantum Monte Carlo techniques, focusing for pedagogical purposes on the example of bosons in the field amplitude (quadrature) basis. Particular emphasis is placed on the variational real- and imaginary-time evolution problems, carefully reviewing the stochastic estimation of the time-dependent variational principles and their relationship with information geometry. Some practical instructions are provided to guide the implementation of a PyTorch code. The review is intended to be accessible to researchers interested in machine learning and quantum information science. 

D089-0563 is a highly promising anti-cancer compound that selectively binds the transcription-silencing G-quadruplex element (Pu27) at the promoter region of the human c-MYC oncogene; however, its binding mechanism remains elusive. The structure of Pu27 is not available due to its polymorphism, but the G-quadruplex structures of its two shorter derivatives in complex with a ligand (Pu24/Phen-DC3 and Pu22/DC-34) are available and show significant structural variance as well as different ligand binding patterns in the 3′ region. Because D089-0563 shares the same scaffold as DC34 while having a significantly different scaffold from Phen-DC3, we picked Pu24 instead of Pu22 for this study in order to gain additional ligand binding insight. Using free ligand molecular dynamics binding simulations (33 μs), we probed the binding of D089-0563 to Pu24. Our clustering analysis identified three binding modes (top, side, and bottom) and subsequent MMPBSA binding energy analysis identified the top mode as the most thermodynamically stable. Our Markov State Model (MSM) analysis revealed that there are three parallel pathways for D089-0563 to the top mode from unbound state and that the ligand binding follows the conformational selection mechanism. Combining our predicted complex structures with the two experimental structures, it is evident that structural differences in the 3′ region between Pu24 and Pu22 lead to different binding behaviors despite having similar ligands; this also explains the different promoter activity caused by the two G-quadruplex sequences observed in a recent synthetic biology study. Based on interaction insights, 625 D089-0563 derivatives were designed and docked; 59 of these showed slightly improved docking scores.