Mapping the Molecular Universe: Exploring Chemical Compound Space by Multiscale High-Throughput Screening and Machine Learning [0.03%]
绘制分子宇宙地图：通过多尺度高通量筛选和机器学习探索化学化合物空间
Thereza A Soares,Ariane Nunes-Alves,Tristan Bereau et al.
Thereza A Soares et al.
Finding Balance: Multiobjective Optimization in Molecular Generative Modeling [0.03%]
兼顾多目标优化的分子生成模型研究
Laura Landolfi,Bruno Catalanotti,Jon Paul Janet
Laura Landolfi
Identifying novel therapeutics active against a single target that balance the requirements for potency, safety, metabolic stability, and a favorable pharmacodynamic profile remains a major challenge, further exacerbated by recent interest ...
Energetics of Noncovalent Interactions of Protein-Ligand Complexes for Drug Discovery [0.03%]
用于药物发现的蛋白质-配体复合物非共价相互作用的热力学研究
Yingze Wang,Dong Jun Shin,Martin Head-Gordon et al.
Yingze Wang et al.
Accurate modeling of noncovalent protein-ligand interactions is critical for applications such as enzyme engineering and drug discovery. Here, we present a data set of 14,905 protein-ligand interaction energies using experimental structures...
Modeling hERG Channel Liability: From Structural Insight to Highly Accurate Qualitative and Quantitative Models [0.03%]
基于结构的hERG通道安全性的成药性模型研究
Hongmao Sun,Yuhong Wang,Min Shen
Hongmao Sun
Drug-induced QT interval prolongation, most commonly resulting from the blockade of a voltage-dependent potassium ion channel encoded by the hERG (human ether-à-go-go-related gene), has been recognized as a critical side-effect of noncardi...
Efficient Prediction of Transition-Metal NMR Chemical Shifts Using Machine Learning: Do Two-Dimensional Descriptors Suffice? [0.03%]
利用机器学习有效预测过渡金属NMR化学位移：二维描述符就够了吗？
Yaroslav I Isaev,Alexey E Kovalev,Alexander A Ksenofontov et al.
Yaroslav I Isaev et al.
Accurate prediction of nuclear magnetic resonance chemical shifts for transition-metal nuclei remains a challenging problem due to the high computational cost of quantum-chemical methods and the limited availability of experimental data. In...
PAIRMAP: A Unified Geometry-Aware Pairwise-Map Framework for Molecular Representation Learning [0.03%]
PAIRMAP：一种用于分子表示学习的统一的几何感知成对映射框架
Zhejiong Wang,Zhengjun Hu,Lichen Zhu et al.
Zhejiong Wang et al.
Molecular representation learning is fundamental to drug discovery, yet existing methods have key limitations: they often miss the pairwise interactions that determine molecular properties, rely on fixed geometric basis functions unable to ...
Learning High-Resolution Protein Embeddings from Multimodal Data via Self-Supervised Integration [0.03%]
基于自监督整合的多模态数据高分辨率蛋白质嵌入学习方法
Yong-Jia Liang,Qian-Yi Wang,Qian Zhou et al.
Yong-Jia Liang et al.
A massive volume of multimodal protein data such as amino acid sequences, structures, gene ontology (GO) annotations, and microscope images has been accumulated, but the experimentally validated function-related annotations of proteins rema...
Advancing the CL&Pol Polarizable Force Field for Accurate Modeling of Mg2+, Ca2+, and Zn2+ Electrolytes [0.03%]
用于精确模拟Mg2+、Ca2+和Zn2+电解质的CL&Pol可极化力场的发展
Mathieu Cancade,Heigo Ers,Aurélien Zavadil et al.
Mathieu Cancade et al.
Divalent cations such as Mg2+, Ca2+, or Zn2+ are promising candidates for powerful and affordable post-Li batteries due to their high natural abundance and an expected two-electron transfer per charge carrier. But an accurate representation...
Structural Basis for Stepwise Substrate Transport and Disease Phenotypic Heterogeneity of the Mitochondrial ADP/ATP Carrier [0.03%]
线粒体ADP/ATP载体的底物转运机制及其介导疾病的表型异质性的结构基础
Shihao Yao,Xiao He,Qiuzi Yi et al.
Shihao Yao et al.
The mitochondrial ADP/ATP carrier (AAC) is essential for cellular energy metabolism and responsible for exchanging ADP for ATP across the inner mitochondrial membrane. However, the precise molecular determinants of substrate binding and the...
Toward Class Imbalance and Uncertainty in Powder XRD Analysis: A Dual-Channel Fusion Network for Space Group Classification [0.03%]
面向粉末X射线衍射分析中的类不平衡和不确定性：空间群分类的双通道融合网络
Shencheng Zhou,Qingzhe Cui,Quan Qian
Shencheng Zhou
Accurate identification of space groups from powder X-ray diffraction (pXRD) is essential for understanding crystal structures and accelerating materials discovery. However, this task remains highly challenging due to inherent peak overlap,...