WeChat Mini Program
Old Version Features

Interactive and Versatile Navigation of Structural Databases.

Journal of medicinal chemistry(2016)SCI 1区

Cambridge Crystallog Data Ctr

Cited 30|Views39
Abstract
We present CSD-CrossMiner, a novel tool for pharmacophore-based searches in crystal structure databases. Intuitive pharmacophore queries describing, among others, protein-ligand interaction patterns, ligand scaffolds, or protein environments can be built and modified interactively. Matching crystal structures are overlaid onto the query and visualized as soon as they are available, enabling the researcher to quickly modify a hypothesis on the fly. We exemplify the utility of the approach by showing applications relevant to real-world drug discovery projects, including the identification of novel fragments for a specific protein environment or scaffold hopping. The ability to concurrently search protein-ligand binding sites extracted from the Protein Data Bank (PDB) and small organic molecules from the Cambridge Structural Database (CSD) using the same pharmacophore query further emphasizes the flexibility of CSD-CrossMiner. We believe that CSD-CrossMiner closes an important gap in mining structural data and will allow users to extract more value from the growing number of available crystal structures.
More
Translated text
PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers

2/3d Pharmacophore Definitions And Their Application

R. A. Lewis, F. Sirockin
COMPREHENSIVE MEDICINAL CHEMISTRY III, VOL 3: IN SILICO DRUG DISCOVERY TOOLS 2017

被引用1

The Cambridge Structural Database (CSD)

Reference Module in Chemistry, Molecular Sciences and Chemical Engineering 2019

被引用7

Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:CSD-CrossMiner是一个新工具,支持通过药物虚拟模型进行结构数据库搜索,允许研究者交互式构建和修改药效团查询,并可视化匹配的晶体结构,从而快速调整假设。

方法】:工具通过直观的药效团查询,这些查询可以描述蛋白质-配体相互作用模式、配体支架或蛋白质环境,并可以交互式地构建和修改。

实验】:实验通过展示与实际药物发现项目相关的应用来证明方法的有效性,例如,为特定蛋白质环境或支架跳跃识别新的碎片。同时,该工具可以同时搜索使用相同药效团查询从蛋白质数据银行(PDB)提取的蛋白质-配体结合位点和小有机分子CSD数据库,显示了其灵活性。