A search for noncarbohydrate sLe(x) mimics led to the development of quinic acid derivatives as selectin inhibitors. At Wyeth we solved the first cocrystal structure of a small molecule, quinic acid, with E-selectin. In the cocomplex two hydroxyls of quinic acid mimic the calcium-bound fucose of the tetrasaccharide sLe(x). The X-ray structure, together with structure based computational methods, was used to design quinic acid based libraries that were synthesized and evaluated for their ability to block the interaction of sLex with P-selectin. A large number of analogues were prepared using solution-phase parallel synthesis. Selected compounds showed decrease in leukocyte rolling in the IVM mouse model. Compound 2 inhibited neutrophil influx in the murine TIP model and demonstrated good plasma exposure.

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Training multiple agents to perform safe and cooperative control in the complex scenarios of autonomous driving has been a challenge. For a small fleet of cars moving together, this paper proposes Lepus, a new approach to training multiple agents. Lepus adopts a pure cooperative manner for training multiple agents, featured with the shared parameters of policy networks and the shared reward function of multiple agents. In particular, Lepus pre-trains the policy networks via an adversarial process, improving its collaborative decision-making capability and further the stability of car driving. Moreover, for alleviating the problem of sparse rewards, Lepus learns an approximate reward function from expert trajectories by combining a random network and a distillation network. We conduct extensive experiments on the MADRaS simulation platform. The experimental results show that multiple agents trained by Lepus can avoid collisions as many as possible while driving simultaneously and outperform the other four methods, that is, DDPG-FDE, PSDDPG, MADDPG, and MAGAIL(DDPG) in terms of stability.