.png){kind=icon}
返回
Ligand-Based Virtual Screening Using Bayesian Networks
.png){kind=icon}
PRE
AI
DOI：10.1021/ci100090p.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A Bayesian inference network (BIN) provides an interesting alternative to existing tools for similarity-based virtual screening. The BIN is particularly effective when the active molecules being sought have a high degree of structural homogeneity but has been found to perform less well with structurally heterogeneous sets of actives. In this paper, we introduce an alternative network model, called a Bayesian belief network (BBN), that seeks to overcome this limitation of the BIN approach. Simulated virtual screening experiments with the MDDR, WOMBAT and MUV data sets show that the BIN and BBN methods allow effective screening searches to be carried out. However, the results obtained are not obviously superior to those obtained using a much simpler approach that is based on the use of the Tanimoto coefficient and of the square roots of fragment occurrence frequencies.
Keyword:
MOLECULAR SIMILARITY
期刊
IF:
5.3
论文数:
9.1K
被引数:
4.0W
机构
引用论文
.png){kind=icon}
分享收藏Hypoformer: Hybrid Decomposition Transformer for Edge-friendly Neural Machine TranslationHypoformer：面向边缘友好的混合分解Transformer神经机器翻译
OAAI
OAAI分享收藏
查看更多内容