Zeyu Yin | Searcharxiv

arXiv2026

Valley-contrasting Spin Textures in Janus Metal Phosphochalcogenides

Momentum-resolved spin textures and potential valley-contrasting physical properties in the momentum space are two intriguing characteristics of noncentrosymmetric materials, and they have broad applications in spintronics and valleytronics. The realization of diverse spin textures within a single material, along with their further coupling to the valley degree of freedom, is highly desirable. Via first-principles calculations, we investigate electronic properties of Janus MP$_2$S$_3$Se$_3$ monolayers, which exhibits distinct spin textures at different valleys. While Ising-type spin textures are located at $K_\pm$ valleys, the symmetry breaking from the Janus structure brings about a coexistence of Weyl-type and Rashba-type spin textures at $\Gamma$ valley. In addition to valley-contrasting spin textures, valley dependence also occurs in Berry-curvature-driven anomalous Hall currents and optical selectivity. Besides, energy differences between $\Gamma$ and $K_\pm$, as well as band gaps, are highly tunable by applied strain. These findings present an intriguing coupling between diverse spin textures and multiple valleys, and pave the way for designing advanced electronic devices that leverage spin and valley degrees of freedom.

cond-mat.mtrl-sci↗

arXiv2024

Language model driven: a PROTAC generation pipeline with dual constraints of structure and property

The imperfect modeling of ternary complexes has limited the application of computer-aided drug discovery tools in PROTAC research and development. In this study, an AI-assisted approach for PROTAC molecule design pipeline named LM-PROTAC was developed, which stands for language model driven Proteolysis Targeting Chimera, by embedding a transformer-based generative model with dual constraints on structure and properties, referred to as the DCT. This study utilized the fragmentation representation of molecules and developed a language model driven pipeline. Firstly, a language model driven affinity model for protein compounds to screen molecular fragments with high affinity for the target protein. Secondly, structural and physicochemical properties of these fragments were constrained during the generation process to meet specific scenario requirements. Finally, a two-round screening of the preliminary generated molecules using a multidimensional property prediction model to generate a batch of PROTAC molecules capable of degrading disease-relevant target proteins for validation in vitro experiments, thus achieving a complete solution for AI-assisted PROTAC drug generation. Taking the tumor key target Wnt3a as an example, the LM-PROTAC pipeline successfully generated PROTAC molecules capable of inhibiting Wnt3a. The results show that DCT can efficiently generate PROTAC that targets and hydrolyses Wnt3a.

q-bio.QM↗