Collaborating Authors

A short de novo synthesis of nucleoside analogs


Nucleotide analogs are valuable tools and therapeutics because of their ability to interfere with processes such as DNA synthesis, which are vital to rapidly dividing cells and replicating viruses. These molecules are challenging to synthesize chemically. Meanwell et al. developed a “ribose last” synthetic strategy in which a fluorinated acyclic nucleic acid is formed by an l- or d-proline–catalyzed aldol reaction (see the Perspective by Miller). This intermediate can then be cyclized to yield the nucleic acid analog in one pot with control of anomeric conformation based on cyclization conditions. Nucleotide analogs accessible by this strategy include those with modifications at C2′ and C4′, purines and pyrimidines, and locked and protected products. Science , this issue p. [725][1]; see also p. [623][2] Nucleoside analogs are commonly used in the treatment of cancer and viral infections. Their syntheses benefit from decades of research but are often protracted, unamenable to diversification, and reliant on a limited pool of chiral carbohydrate starting materials. We present a process for rapidly constructing nucleoside analogs from simple achiral materials. Using only proline catalysis, heteroaryl-substituted acetaldehydes are fluorinated and then directly engaged in enantioselective aldol reactions in a one-pot reaction. A subsequent intramolecular fluoride displacement reaction provides a functionalized nucleoside analog. The versatility of this process is highlighted in multigram syntheses of d- or l-nucleoside analogs, locked nucleic acids, iminonucleosides, and C2′- and C4′-modified nucleoside analogs. This de novo synthesis creates opportunities for the preparation of diversity libraries and will support efforts in both drug discovery and development. [1]: /lookup/doi/10.1126/science.abb3231 [2]: /lookup/doi/10.1126/science.abd1283

On the Effect of Analog Noise in Discrete-Time Analog Computations

Neural Information Processing Systems

We introduce a model for noise-robust analog computations with discrete time that is flexible enough to cover the most important concrete cases, such as computations in noisy analog neural nets and networks of noisy spiking neurons. We show that the presence of arbitrarily small amounts of analog noise reduces the power of analog computational models to that of finite automata, and we also prove a new type of upper bound for the VC-dimension of computational models with analog noise. 1 Introduction Analog noise is a serious issue in practical analog computation. However there exists no formal model for reliable computations by noisy analog systems which allows us to address this issue in an adequate manner. The investigation of noise-tolerant digital computations in the presence of stochastic failures of gates or wires had been initiated by [von Neumann, 1956]. We refer to [Cowan, 1966] and [Pippenger, 1989] for a small sample of the nllmerous results that have been achieved in this direction. The same framework (with stochastic failures of gates or wires) hac; been applied to analog neural nets in [Siegelmann, 1994].