We present here the first catalytic desymmetrization of the 2,2',6,6'-tetrabromobiphenyl 1 and analogues, by a bromine-lithium exchange catalyzed by either diamines or diether derivatives (0.5 equiv.), yielding axially chiral compounds in high yield (up to 89%) and high enantioselectivity (up to 82%)

Upon further examination of the NMR spectra for the products of the reduction of the Se-phenyl 1-methoxybicyclo[2.2.2]oct-5-ene-2-carboselenoate 5 f the authors of this Communication have found that the reaction did not give the expected 1-methoxy-bicyclo-[2.2.2]oct-2-ene 6 f in 67 % yield as reported in Table 1 (entry f). Instead, three major products were obtained in a combined yield of 67 %: the expected product 6 f (16 %) along with the two products of cyclopropylcarbinyl radical rearrangement, the endo (major, 57 %) and the exo (minor, 27 %) isomers of 2-methoxybicyclo[3.2.1]oct-6-ene, the former being a known compound. [1] [1] a) M. A. Battiste, J. M. Coxon, A.

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Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule “synthesizability”, but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), which computes a synthetic feasibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). We start by comparing several synthetic feasibility scores to a binary ground truth of synthetic feasibility as estimated by chemists on a bench of generated molecules, as a first experimental validation that the RScore is a reliable synthetic feasibility score. We then describe a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables our molecular generators to produce more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on https://github.com/iktos/generation-under- synthetic-constraint.