The bulk supply of the antiviral C -nucleoside analogue remdesivir is largely hampered by a low-yielding C -glycosylation step in which the base is coupled to the pentose unit. Here, we disclose a significantly improved methodology for this critical transformation. By utilizing diisopropylamine as a cost-effective additive, the addition reaction furnishes an optimal yield of 75% of the desired ribofuranoside adduct, representing the highest yield obtained thus far for this key step. The method proved suitable for hectogram scale synthesis without column chromatographic operations.

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Denoising Diffusion models have demonstrated their proficiency for generative sampling. However, generating good samples often requires many iterations. Consequently, techniques such as binary time-distillation (BTD) have been proposed to reduce the number of network calls for a fixed architecture. In this paper, we introduce TRAnsitive Closure Time-distillation (TRACT), a new method that extends BTD. For single step diffusion, TRACT improves FID by up to 2.4× on the same architecture, and achieves new single-step Denoising Diffusion Implicit Models (DDIM) state-of-the-art FID (7.4 for ImageNet64, 3.8 for CIFAR10). Finally we tease apart the method through extended ablations. The PyTorch [37] implementation will be released soon.

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