Weiqiang Fu, Yujie Mo, Yi Xiao, Chang Liu, Feng Zhou, Yang Wang, Jielong Zhou*, Yingsheng J. Zhang*  June 3, 2024 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 A...

Feng Zhou, Haolin Du, Yang Wang, Weiqiang Fu, Bingchen Zhao, Jielong Zhou*, Yingsheng J. Zhang. June 5, 2024 DOI: https://doi.org/10.1021/acsmedchemlett.4c00047 Abstract: We employ a combination of accelerated molecular dynamics and machine learning to unravel how the dynamic characteristics of CBL-B and C–CBL confer their binding affinity and selectivity for ligands from subtle structural disparities within their binding pockets and dissociation pathways. Our predictive model of dissociation rate constants (koff) demonstrates a moderate correlation between predicted koff and experimental IC50 values, which is consistent with experimental koff and τ-random accelerated molecular dynamics (τRAMD) results. By employing a linear regression of dissociation trajectories, we identified key amino acids in binding pockets and along the dissociati...

Wei Feng, Lvwei Wang, Zaiyun Lin, Yanhao Zhu, Han Wang, Jianqiang Dong, Rong Bai, Huting Wang, Jielong Zhou, Wei Peng, Bo Huang & Wenbiao Zhou    15 January 2024  DOI: https://doi.org/10.1038/s42256-023-00775-6 Abstract： Generative models for molecules based on sequential line notation (for example, the simplified molecular-input line-entry system) or graph representation have attracted an increasing interest in the field of structure-based drug design, but they struggle to capture important three-dimensional (3D) spatial interactions and often produce undesirable molecular structures. To address these challenges, we introduce Lingo3DMol, a pocket-based 3D molecule generation method that combines language models and geometric deep learning technology. A new molecular representation, the fragment-based simplified molecular-input lin...

Di Wu, Qihao Chen, Zhuoya Yu, Bo Huang, Jun Zhao, Yuhang Wang, Jiawei Su, Feng Zhou, Rui Yan, Na Li, Yan Zhao & Daohua Jian. 11 December 2023 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 &Ari...

Bo Qiang, Yiran Zhou, Yuheng Ding, Ningfeng Liu, Song Song, Liangren Zhang, Bo Huang & Zhenming Liu. 05 December 2023 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 poss...

Lanying Wei, Yucui Xin,Mengchen Pu,Yingsheng Zhang. 17 November 2023.  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 g...