Literature DB >> 19714705

Molecular fingerprint recombination: generating hybrid fingerprints for similarity searching from different fingerprint types.

Britta Nisius1, Jürgen Bajorath.   

Abstract

Molecular fingerprints have a long history in computational medicinal chemistry and continue to be popular tools for similarity searching. Over the years, a variety of fingerprint types have been introduced. We report an approach to identify preferred bit subsets in fingerprints of different design and "recombine" these bit segments into "hybrid fingerprints". These compound class-directed fingerprint representations are found to increase the similarity search performance of their parental fingerprints, which can be rationalized by the often complementary nature of distinct fingerprint features.

Mesh:

Year:  2009        PMID: 19714705     DOI: 10.1002/cmdc.200900243

Source DB:  PubMed          Journal:  ChemMedChem        ISSN: 1860-7179            Impact factor:   3.466


  7 in total

1.  Freely available compound data sets and software tools for chemoinformatics and computational medicinal chemistry applications.

Authors:  Ye Hu; Jurgen Bajorath
Journal:  F1000Res       Date:  2012-08-14

2.  Follow up: Compound data sets and software tools for chemoinformatics and medicinal chemistry applications: update and data transfer.

Authors:  Ye Hu; Jürgen Bajorath
Journal:  F1000Res       Date:  2014-03-11

3.  Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning.

Authors:  Liangxu Xie; Lei Xu; Ren Kong; Shan Chang; Xiaojun Xu
Journal:  Front Pharmacol       Date:  2020-12-18       Impact factor: 5.810

4.  Novel Solubility Prediction Models: Molecular Fingerprints and Physicochemical Features vs Graph Convolutional Neural Networks.

Authors:  Sumin Lee; Myeonghun Lee; Ki-Won Gyak; Sung Dug Kim; Mi-Jeong Kim; Kyoungmin Min
Journal:  ACS Omega       Date:  2022-04-04

5.  Enhancing adverse drug event detection in electronic health records using molecular structure similarity: application to pancreatitis.

Authors:  Santiago Vilar; Rave Harpaz; Lourdes Santana; Eugenio Uriarte; Carol Friedman
Journal:  PLoS One       Date:  2012-07-24       Impact factor: 3.240

6.  Detection of drug-drug interactions by modeling interaction profile fingerprints.

Authors:  Santiago Vilar; Eugenio Uriarte; Lourdes Santana; Nicholas P Tatonetti; Carol Friedman
Journal:  PLoS One       Date:  2013-03-08       Impact factor: 3.240

7.  How Sure Can We Be about ML Methods-Based Evaluation of Compound Activity: Incorporation of Information about Prediction Uncertainty Using Deep Learning Techniques.

Authors:  Igor Sieradzki; Damian Leśniak; Sabina Podlewska
Journal:  Molecules       Date:  2020-03-23       Impact factor: 4.411

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.