Undersampling Techniques for Nonlinear Chemical Space Visualization [0.03%]
化学非线性空间可视化 undersampling技术
Akash Surendran,Krisztina Zsigmond,Ramón Alain Miranda-Quintana
Akash Surendran
The visualization of high-dimensional chemical space is a critical tool for understanding molecular diversity, structure-property relationships, and for guiding compound selection. However, the performance of non-linear dimensionality reduc...
To What Extent Can We Extrapolate Proteochemometric Models: A Case Study for the SLC6 Transporter Family [0.03%]
我们能在多大程度上外推药化计量模型：以SLC6转运蛋白家族为例的研究
Uday Abu-Shehab,Gerhard Ecker
Uday Abu-Shehab
Proteochemometrics (PCM) modeling combines protein and ligand information to create predictive models for biological activity. It aims to extrapolate information across targets, enabling its application in screening drug candidates across a...
DATTs: A Database of Disease-Associated Therapeutic Targets With Required Actions for Treatment [0.03%]
具有治疗作用的疾病相关靶点数据库及其对治疗的需求行动:DATTs数据库
Ryusuke Sawada,Noriko Yuyama Otani,Michio Iwata et al.
Ryusuke Sawada et al.
Proteins involved in pathophysiological mechanisms are widely recognized as promising therapeutic targets for drug discovery. The therapeutic effects of drugs are primarily mediated through the inhibition or activation of target proteins. T...
SpaceExpander: An Automated System for Drafting Markush Claims to Expand Chemical Space [0.03%]
空间扩展器：一种用于起草马库什权利要求以扩大化学空间的自动化系统
Rui Wu,Liyun Mao,Yanyan Diao et al.
Rui Wu et al.
Drafting chemical compound patents, particularly those involving Markush structures, is a complex task often hindered by manual workflows that are time-consuming and error-prone and may result in incomplete protection. To address these chal...
A Structure-Informed Atlas of Venom-Derived Peptides Reveals the Organization of Chemical Space [0.03%]
基于毒液衍生肽的结构信息图谱揭示了化学空间的组织方式
Thaís Caroline Gonçalves,Eduardo Henrique Toral Cortez,Danilo T Amaral
Thaís Caroline Gonçalves
Venom-derived peptides are structurally diverse bioactive scaffolds with growing relevance for computational discovery and molecular design. However, their organization within chemical space remains poorly characterized in integrated framew...
ConGen: Targeted Molecule Generation Through Contrastive Learning and Latent Optimization [0.03%]
基于对比学习和潜在优化的目标分子生成模型ConGen
Can Koban,Gökçe Uludoğan,Elif Ozkirimli et al.
Can Koban et al.
The discovery of novel compounds for protein targets is an important step in the drug discovery process. Target-specific molecules can be designed based on protein structures, but molecule prediction based on protein sequences alone remains...
Amodini A P,Chandra Mohan Dasari,Santhosh Amilpur
Amodini A P
De novo molecular generation remains a central challenge in drug discovery due to the vastness of chemical space and the difficulty of generating molecules that are simultaneously valid, diverse, and property-aware. While graph-based genera...
An Attention-Driven Graph Transformer With Nonlinear Modeling and Neuro-Fuzzy Fusion for High-Order Toxic Molecular Graph Learning [0.03%]
一种非线性建模和神经模糊融合的注意力驱动图变换器用于高阶有毒分子图学习
Phu Pham
Phu Pham
Learning expressive representations for complex, size-varied molecular graphs remains a fundamental challenge in toxic molecular property prediction and regression. The inherent nonlinearity of atomic interactions, together with intricate s...
Dmytro M Volochnyuk,Serhiy V Ryabukhin
Dmytro M Volochnyuk
The special issue collects recent contributions from Ukrainian researchers, both from academia and industry, in the field of chemoinformatics. It contains 6 publications from leading Ukrainian scientists in the field. These articles represe...
ADME-DTI: Augmented Deep Meta Ensemble for Drug-Target Interaction Prediction [0.03%]
增强型深度元集成药物靶点相互作用预测方法 ADME-DTI
Tariq Sha&#x;ban,Ahmad M Mustafa,Mostafa Z Ali et al.
Tariq Sha&#x;ban et al.
Drug-target interaction represents a critical focus area in computational drug discovery and pharmaceutical research. However, the process of identifying and analyzing these interactions is often resource-intensive, requiring extensive expe...