Tetrahedron Computer Methodology最新文献_第4页

A method to detect common features necessary for biological activity: Application of ANALOGS for aldose reductase inhibitors 一种检测生物活性所必需的共同特征的方法:醛糖还原酶抑制剂的应用

Tetrahedron Computer Methodology Pub Date : 1990-01-01 Epub Date: 2004-08-27 DOI: 10.1016/0898-5529(90)90116-P

Tamio Yasukawa ∗ , Katsunori Satoh , Noriaki Gotoh , Toshimasa Ishida ∗ , Shiegeuki Sumiya , Kunihiro Kitamura ∗

引用次数: 1

Search for the bullvalene-like structures 寻找瓣状结构

Tetrahedron Computer Methodology Pub Date : 1990-01-01 Epub Date: 2004-11-11 DOI: 10.1016/0898-5529(90)90067-I

Dmitri E. Lushnikov, Ekaterina V. Gordeeva, Nikolai S. Zefirov ∗

引用次数: 0

3D database searching and de novo construction methods in molecular design 分子设计中的三维数据库检索与从头构建方法

Tetrahedron Computer Methodology Pub Date : 1990-01-01 Epub Date: 2004-10-01 DOI: 10.1016/0898-5529(90)90168-8

Joseph B. Moon, W.Jeffrey Howe

{"title":"3D database searching and de novo construction methods in molecular design","authors":"Joseph B. Moon, W.Jeffrey Howe","doi":"10.1016/0898-5529(90)90168-8","DOIUrl":"10.1016/0898-5529(90)90168-8","url":null,"abstract":"<div><p>Computer-based lead finding algorithms which attempt to design, on a de novo basis, ligands that will complement a known receptor site cavity face some major problems in terms of a combinatorial design space and the synthesizability of the designed molecules. On the other hand, typical 3D database search methods provide a different set of challenges. Both of these approaches are ultimately pointed toward the same goal and can be used together productively. In this article we describe advances in both areas: we first describe extensions to our de novo ligand design software which combines (a) a tree-based conformational search over a library of fragments, and (b) a form of simulated annealing which allows designed ligands to crawl around the binding site even as their structures are changing. In the second part, we then discuss an implementation of the database approach which allows users to formulate 3D substructure, superstructure, or similarity queries based upon demonstrated or hypothetical requirements for activity. Finally, we draw the two approaches together with an example of current research interest, showing how one method can feed the other.</p></div>","PeriodicalId":101214,"journal":{"name":"Tetrahedron Computer Methodology","volume":"3 6","pages":"Pages 697-711"},"PeriodicalIF":0.0,"publicationDate":"1990-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0898-5529(90)90168-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77837362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 31

Building 3D structural databases: Experiences with MDDR-3D and FCD-3D 建立3D结构数据库:有使用mdr -3D和FCD-3D的经验

Tetrahedron Computer Methodology Pub Date : 1990-01-01 Epub Date: 2004-10-01 DOI: 10.1016/0898-5529(90)90155-2

Douglas R. Henry, Phil J. McHale, Bradley D. Christie, Daniel Hillman

引用次数: 13

Machine learning of generic reactions: 3. an efficient algorithm for maximal common substructure determination 通用反应的机器学习:一种确定最大公共子结构的有效算法

Tetrahedron Computer Methodology Pub Date : 1990-01-01 Epub Date: 2004-11-11 DOI: 10.1016/0898-5529(90)90061-C

Christian Tonnelier, Philippe Jauffret ∗, Thierry Hanser, Gérard Kaufmann

引用次数: 17

Symbolic computing on reaction pathways 反应路径的符号计算

Tetrahedron Computer Methodology Pub Date : 1990-01-01 Epub Date: 2004-08-27 DOI: 10.1016/0898-5529(90)90104-G

Raúl E. Valdés-Pérez

引用次数: 13

Selection of screens for three-dimensional substructure searching 三维子结构搜索的筛选

Tetrahedron Computer Methodology Pub Date : 1990-01-01 Epub Date: 2004-08-27 DOI: 10.1016/0898-5529(90)90119-S

Janey K. Cringean, Catherine A. Pepperrell, Andrew R. Poirrette, Peter Willett ∗

引用次数: 27

A protein structure predictor based on an energy model with learned parameters 基于学习参数的能量模型的蛋白质结构预测器

Tetrahedron Computer Methodology Pub Date : 1990-01-01 Epub Date: 2005-01-10 DOI: 10.1016/0898-5529(90)90048-D

Joseph D. Bryngelson , J.J. Hopfield , Samuel N. Southard Jr

引用次数: 14

Neural network analysis of protein tertiary structure 蛋白质三级结构的神经网络分析

Tetrahedron Computer Methodology Pub Date : 1990-01-01 Epub Date: 2005-01-10 DOI: 10.1016/0898-5529(90)90052-A

George L. Wilcox , Marius Poliac , Michael N. Liebman

{"title":"Neural network analysis of protein tertiary structure","authors":"George L. Wilcox , Marius Poliac , Michael N. Liebman","doi":"10.1016/0898-5529(90)90052-A","DOIUrl":"10.1016/0898-5529(90)90052-A","url":null,"abstract":"<div><p>We describe a large scale application of a back-propagation neural network to the analysis, classification and prediction of protein secondary and tertiary structure from sequence information alone. A back-propagation network called BigNet has been implemented along with a Network Description Language (NDL) on the 512 MWord Cray 2 at the Minnesota Supercomputer Center. The proof-of-concept experiments described here used a small, heterologous training set of small protein structures (15 proteins each with less than 133 residues) from the Brookhaven Protein Data Bank (PDB). Simulations with one hidden layer and one half to ten million connections execute at three to five million connection updates per second in full back-propagation learning mode and routinely converge to solutions where input of hydrophobicity-coded sequence yields output distance matrices with 0.3 to 1.5% RMS deviation from actual distance matrices. Although the training set used is too small to expect useful generalization, some evidence of generalization was evident in similarity of learning progress of homologous pairs within the training set and in production of novel distance matrix outputs upon presentation with novel input sequences. The discussion addresses limitations in the current implementation, plans for software improvements, and characteristics of future training sets.</p></div>","PeriodicalId":101214,"journal":{"name":"Tetrahedron Computer Methodology","volume":"3 3","pages":"Pages 191-204, IN4, 205-211"},"PeriodicalIF":0.0,"publicationDate":"1990-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0898-5529(90)90052-A","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87743259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 30

The golden age of computational chemistry 计算化学的黄金时代

Tetrahedron Computer Methodology Pub Date : 1990-01-01 Epub Date: 2004-11-11 DOI: 10.1016/0898-5529(90)90064-F

W.Todd Wipke (Editor-in-Chief)

引用次数: 0