Delving into the Latest Updates on WuXi AppTec (Shanghai) Co. Ltd. with Synapse

作者: Cui, Weiren ; Hsueh, Kuang-Lung ; He, Yunyun ; Liu, Huan ; Su, Wenji ; Deng, Jason ; Kuai, Letian ; Han, Shuai ; Song, Yidan ; Guo, Xinyun ; Wang, Min

DNA-encoded library (DEL) technology, especially when combined with machine learning (ML), is a powerful method to discover novel inhibitors. DEL-ML can expand a larger chemical space and boost cost-effectiveness during hit finding. Heme oxygenase-1 (HO-1), a heme-degrading enzyme, is linked to diseases such as cancer and neurodegenerative disorders. The discovery of five series of new scaffold HO-1 hits is reported here, using a DEL-ML workflow, which emphasizes the model's uncertainty quantification and domain of applicability. This model exhibits a strong extrapolation ability, identifying new structures beyond the DEL chemical space. About 37% of predicted molecules showed a binding affinity of K _D < 20 μM, with the strongest being 141 nM, amd 14 of those molecules displayed >100-fold selectivity for HO-1 over heme oxygenase-2 (HO-2). These molecules also showed structural novelty compared to existing HO-1 inhibitors. Docking simulations provided insights into possible selectivity rationale.

2024-08-30·Science Advances

Discovery of SARS-CoV-2 papain-like protease (PL ^pro ) inhibitors with efficacy in a murine infection model

Article

作者: Lengyel-Zhand, Zsofia ; Rothan, Hussin ; Hurst, Brett ; Polivkova, Jana ; Korczynska, Magdalena ; Eng, Heather ; Zhang, Lei ; Wu, Huixian ; Zhu, Yuao ; Robinson, Matthew C. ; Yurgelonis, Irina ; Foley, Tim ; Rai, Devendra ; Chang, Jeanne S. ; Mashalidis, Ellene ; Behzadi, Amin ; Meredith, Hannah R. ; Balesano, Amanda ; Gao, Liping ; Aschenbrenner, Lisa ; Qi, Wenying ; Ford, Kristen K. ; Garnsey, Michelle R. ; Hao, Li ; Boras, Britton ; Yu, Aijia ; Ephron, Andrew ; Zhang, Jinzhi ; Frick, James M. ; Vargo, Thomas R. ; Kalgutkar, Amit S. ; Cardin, Rhonda ; Patel, Nandini C. ; Rose, Colin R. ; Tillotson, Joseph ; Liu, Yiping ; Sakata, Sylvie K. ; Lee, Alpha A. ; Nguyen, Luong T. ; Gibson, Scott

Vaccines and first-generation antiviral therapeutics have provided important protection against COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there remains a need for additional therapeutic options that provide enhanced efficacy and protection against potential viral resistance. The SARS-CoV-2 papain-like protease (PL pro ) is one of the two essential cysteine proteases involved in viral replication. While inhibitors of the SARS-CoV-2 main protease have demonstrated clinical efficacy, known PL pro inhibitors have, to date, lacked the inhibitory potency and requisite pharmacokinetics to demonstrate that targeting PL pro translates to in vivo efficacy in a preclinical setting. Here, we report the machine learning–driven discovery of potent, selective, and orally available SARS-CoV-2 PL pro inhibitors, with lead compound PF-07957472 ( 4 ) providing robust efficacy in a mouse-adapted model of COVID-19 infection.

2023-04-25·mBio

Structure-Aided Identification of an Inhibitor Targets Mps1 for the Management of Plant-Pathogenic Fungi

Article

作者: Cui, Weiren ; Tang, Liu ; Yang, Jun ; Zhang, Xiaokang ; Liu, Junfeng ; Wang, Dongli ; Su, Wenji ; Kong, Zhiwei ; Zhang, Xin ; Kuai, Letian ; Cai, Jiaxi ; Li, Saijie ; Wang, Yuli ; Chen, Yitong ; Zhou, Feng ; Peng, You-Liang

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