AI-driven QSAR modelling and virtual screening in the discovery of selective dopamine D2 receptor ligands [0.03%]
基于人工智能的QSAR模型与虚拟筛选在发现选择性多巴胺D2受体配体中的应用
N Maliyakkal,H C Vishwakarma,S Kumar et al.
N Maliyakkal et al.
The dopamine D2 receptor (DRD2) is a key therapeutic target for several neuropsychiatric disorders, driving the need for new ligands with improved safety and efficacy. To find possible DRD2 inhibitors, we developed an integrated in silico w...
Integrating machine learning and pharmacogenomics for biomarker discovery, identification and prioritization of potential drug candidates in ovarian cancer [0.03%]
基于机器学习和药物基因组学的卵巢癌生物标志物发现、潜在药物候选物识别与优先排序
S Yadav,A C Kaushik,G Srivastava et al.
S Yadav et al.
Ovarian cancer remains a major global health concern and leading cause of mortality among women due to late diagnosis, therapeutic resistance, and limited predictive biomarkers for treatment response. There is an urgent need for integrative...
Multi-target QSAR modelling for identification of novel inhibitors of class I HDACs [0.03%]
多靶标QSAR建模以寻找I类HDACs的新型抑制剂
G G Tu
G G Tu
In this study, the multi-target QSAR (mt-QSAR) models were constructed which can predict the inhibitory activity of compounds against various class I HDACs isoforms under different experimental conditions. Models based on mt-QSAR classifica...
QSAR-based evaluation of plant-derived larvicidal agents against Zika vector Aedes aegypti via Monte Carlo optimization and SMILES-based descriptors and molecular docking [0.03%]
基于QSAR的植物源蚊幼虫剂评价研究及对寨卡病毒传播媒介埃及伊蚊的作用机制探究
S Lotfi,F Nooraei,S Ahmadi
S Lotfi
Aedes aegypti is the principal vector responsible for the transmission of several arboviral diseases, including dengue, Zika, chikungunya, and yellow fever, posing a significant threat to global public health. Controlling its population is ...
P Rani,K Dutta,V Kumar
P Rani
Malignant diseases are considered the most prominent and widespread causes of death affecting populations globally. Synergistic drug combinations have shown beneficial therapeutic results in the treatment of malignant diseases. Although tec...
Ionic liquids as emerging environmental contaminants: a critical review of fate, toxicity mechanisms, and sustainable design strategies [0.03%]
离子液体新兴环境污染物本体：命运、毒性机制及可持续设计策略批判性评述
D Wang,H Ren,H Liu
D Wang
Ionic liquids (ILs) are increasingly detected in various environments, raising profound concerns regarding their ecological impacts and long-term sustainability. While their tunability offers immense application potential, it concurrently i...
Integrated chemometric modelling of histamine H3 receptor for the identification of ligands from a natural product repository [0.03%]
整合化学计量学模型组胺H3受体识别来自天然产品仓库配体
N S Jandali,L A Samuor,K Tawaha et al.
N S Jandali et al.
The histamine H3 receptor (H3R) is a GPCR that regulates the release of multiple neurotransmitters and has emerged as an attractive target for CNS disorders. An integrated computational workflow was applied to identify H3R ligands from natu...
Linear models, quantum molecular descriptors, and DSSC efficiency: an approach for evaluating potential new sensitizing dyes [0.03%]
线性模型、量子分子描述符和DSSC效率：评估潜在新敏化染料的一种方法
E F S Mattos,I F Vieira,G S Mendonça et al.
E F S Mattos et al.
The growing energy demand has accelerated the search for renewable energy sources, with dye-sensitized solar cells (DSSCs) emerging as promising candidates. To streamline the experimental process and reduce associated costs, we developed pr...
QSPR models for water solubility of organic compounds using correlation intensity index and Las Vegas algorithm [0.03%]
基于相关强度指数和拉斯维加斯算法的有机物水溶性QSAR模型研究
A P Toropova,A A Toropov,G Selvestrel et al.
A P Toropova et al.
Water solubility is an important factor in environmental and toxicological science because it determines the mobility, bioavailability, and potential for absorption by living organisms. Higher solubility often correlates with greater enviro...
Mitigating silent failures in toxicity prediction: a conformalized heteroscedastic Bayesian framework [0.03%]
一种异方差贝叶斯框架下的毒性预测隐蔽失效的缓解方法
B Özlüer Başer
B Özlüer Başer
Accurate uncertainty quantification is a prerequisite for reliable toxicity assessments in drug discovery. Traditional QSAR models provide point estimates but fail to communicate prediction reliability, particularly for structurally complex...