Weiqiang Fu, Yujie Mo, Yi Xiao, Chang Liu, Feng Zhou, Yang Wang, Jielong Zhou*, Yingsheng J. Zhang DOI: https://doi.org/10.1021/acs.jctc.3c01181 Abstract: Exclusively prioritizing the precision of energy prediction frequently proves inadequate in satisfying multifaceted requirements. A heightened focus is warranted on assessing the rationality of potential energy curves predicted by machine learning-based force fields (MLFFs), alongside evaluating the pragmatic utility of these MLFFs. This study introduces SWANI, an optimized neural network potential stemming from the ANI framework. Through the incorporation of supplementary physical constraints, SWANI aligns more cohesively with chemical expectations, yielding rational potential energy profiles. It also exhibits superior predictive precision compared with that of the ANI model. Addition...

Di Wu, Qihao Chen, Zhuoya Yu, Bo Huang, Jun Zhao, Yuhang Wang, Jiawei Su, Feng Zhou, Rui Yan, Na Li, Yan Zhao & Daohua Jiang DOI: https://doi.org/10.1038/s41586-023-06926-4 Abstract: Vesicular monoamine transporter 2 (VMAT2) accumulates monoamines in presynaptic vesicles for storage and exocytotic release, and has a vital role in monoaminergic neurotransmission1,2,3. Dysfunction of monoaminergic systems causes many neurological and psychiatric disorders, including Parkinson’s disease, hyperkinetic movement disorders and depression4,5,6. Suppressing VMAT2 with reserpine and tetrabenazine alleviates symptoms of hypertension and Huntington’s disease7,8, respectively. Here we describe cryo-electron microscopy structures of human VMAT2 complexed with serotonin and three clinical drugs at 3.5–2.8 Å, demonstrating...

Bo Qiang, Yiran Zhou, Yuheng Ding, Ningfeng Liu, Song Song, Liangren Zhang, Bo Huang & Zhenming Liu DOI: https://doi.org/10.1038/s42256-023-00764-9 Abstract: Chemical reactions are the fundamental building blocks of drug design and organic chemistry research. In recent years, there has been a growing need for a large-scale deep-learning framework that can efficiently capture the basic rules of chemical reactions. In this paper, we have proposed a unified framework that addresses both the reaction-representation learning and molecule generation tasks, which allows for a more holistic approach. Inspired by the organic chemistry mechanism, we develop a new pretraining framework that enables us to incorporate inductive biases into the model. Our framework achieves state-of-the-art results in performance of challenging downstream tasks. By possessing chemical kno...

Lanying Wei, Yucui Xin,Mengchen Pu,Yingsheng Zhang DOI: https://doi.org/10.26508/lsa.202302253 Abstract: To effectively understand the underlying mechanisms of disease and inform the development of personalized therapies, it is critical to harness the power of differential co-expression (DCE) network analysis. Despite the promise of DCE network analysis in precision medicine, current approaches have a major limitation: they measure an average differential network across multiple samples, which means the specific etiology of individual patients is often overlooked. To address this, we present Cosinet, a DCE-based single-sample network rewiring degree quantification tool. By analyzing two breast cancer datasets, we demonstrate that Cosinet can identify important differences in gene co-expression patterns between individual patients and generate scores for each in...

Mengchen Pu , Kaiyang Cheng, Xiaorong Li , Yucui Xin, Lanying Wei, Sutong Jin, Weisheng Zheng, Gongxin Peng, Qihong Tang, Jielong Zhou, Yingsheng Zhang DOI: https://doi.org/10.1016/j.csbj.2023.10.011 Abstract: Synthetic lethal (SL) pairs are pairs of genes whose simultaneous loss-of-function results in cell death, while a damaging mutation of either gene alone does not affect the cell’s survival. This makes SL pairs attractive targets for precision cancer therapies, as targeting the unimpaired gene of the SL pair can selectively kill cancer cells that already harbor the impaired gene. Limited by the difficulty of finding true SL pairs, especially on specific cell types, current computational approaches provide only limited insights because of overlooking the crucial aspects of cellular context dependency and mechanistic understanding of SL pai...

Wenzhi Ma, Yuan Le, Xiaoxuan Shi, Qingbo Xu, Yang Xiao, Yueying Dou, Xiaoman Wang, Wenbiao Zhou, Hongbo Zhang, Bo Huang DOI: https://doi.org/10.1038/s42004-023-00984-5 Abstract: The quest for effective virtual screening algorithms is hindered by the scarcity of training data, calling for innovative approaches. This study presents the use of experimental electron density (ED) data for improving active compound enrichment in virtual screening, supported by ED’s ability to reflect the time-averaged behavior of ligands and solvents in the binding pocket. Experimental ED-based grid matching score (ExptGMS) was developed to score compounds by measuring the degree of matching between their binding conformations and a series of multi-resolution experimental ED grids. The efficiency of ExptGMS was validated using both in silico tests with the Dir...