Literature DB >> 15177078

The signature molecular descriptor. 3. Inverse-quantitative structure-activity relationship of ICAM-1 inhibitory peptides.

Carla J Churchwell1, Mark D Rintoul, Shawn Martin, Donald P Visco, Archana Kotu, Richard S Larson, Laurel O Sillerud, David C Brown, Jean-Loup Faulon.   

Abstract

We present a methodology for solving the inverse-quantitative structure-activity relationship (QSAR) problem using the molecular descriptor called signature. This methodology is detailed in four parts. First, we create a QSAR equation that correlates the occurrence of a signature to the activity values using a stepwise multilinear regression technique. Second, we construct constraint equations, specifically the graphicality and consistency equations, which facilitate the reconstruction of the solution compounds directly from the signatures. Third, we solve the set of constraint equations, which are both linear and Diophantine in nature. Last, we reconstruct and enumerate the solution molecules and calculate their activity values from the QSAR equation. We apply this inverse-QSAR method to a small set of LFA-1/ICAM-1 peptide inhibitors to assist in the search and design of more-potent inhibitory compounds. Many novel inhibitors were predicted, a number of which are predicted to be more potent than the strongest inhibitor in the training set. Two of the more potent inhibitors were synthesized and tested in-vivo, confirming them to be the strongest inhibiting peptides to date. Some of these compounds can be recycled to train a new QSAR and develop a more focused library of lead compounds.

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Year:  2004        PMID: 15177078     DOI: 10.1016/j.jmgm.2003.10.002

Source DB:  PubMed          Journal:  J Mol Graph Model        ISSN: 1093-3263            Impact factor:   2.518


  14 in total

1.  Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures.

Authors:  A Varnek; D Fourches; F Hoonakker; V P Solov'ev
Journal:  J Comput Aided Mol Des       Date:  2005-11-16       Impact factor: 3.686

2.  Reverse engineering chemical structures from molecular descriptors: how many solutions?

Authors:  Jean-Loup Faulon; W Michael Brown; Shawn Martin
Journal:  J Comput Aided Mol Des       Date:  2005-11-03       Impact factor: 3.686

3.  Can topological indices transmit information on properties but not on structures?

Authors:  Alexandru T Balaban
Journal:  J Comput Aided Mol Des       Date:  2005-11-23       Impact factor: 3.686

4.  Prediction of beta-strand packing interactions using the signature product.

Authors:  W Michael Brown; Shawn Martin; Joseph P Chabarek; Charlie Strauss; Jean-Loup Faulon
Journal:  J Mol Model       Date:  2005-12-07       Impact factor: 1.810

5.  A constructive approach for discovering new drug leads: Using a kernel methodology for the inverse-QSAR problem.

Authors:  William Wl Wong; Forbes J Burkowski
Journal:  J Cheminform       Date:  2009-04-28       Impact factor: 5.514

Review 6.  On exploring structure-activity relationships.

Authors:  Rajarshi Guha
Journal:  Methods Mol Biol       Date:  2013

7.  Molecular structures enumeration and virtual screening in the chemical space with RetroPath2.0.

Authors:  Mathilde Koch; Thomas Duigou; Pablo Carbonell; Jean-Loup Faulon
Journal:  J Cheminform       Date:  2017-12-19       Impact factor: 5.514

8.  Exploring differential evolution for inverse QSAR analysis.

Authors:  Tomoyuki Miyao; Kimito Funatsu; Jürgen Bajorath
Journal:  F1000Res       Date:  2017-07-31

9.  Molecular de-novo design through deep reinforcement learning.

Authors:  Marcus Olivecrona; Thomas Blaschke; Ola Engkvist; Hongming Chen
Journal:  J Cheminform       Date:  2017-09-04       Impact factor: 5.514

10.  BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry.

Authors:  Igor V Tetko; Ola Engkvist; Uwe Koch; Jean-Louis Reymond; Hongming Chen
Journal:  Mol Inform       Date:  2016-07-28       Impact factor: 3.353

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