Literature DB >> 10896320

De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks.

G Schneider1, M L Lee, M Stahl, P Schneider.   

Abstract

An evolutionary algorithm was developed for fragment-based de novo design of molecules (TOPAS, TOPology-Assigning System). This stochastic method aims at generating a novel molecular structure mimicking a template structure. A set of approximately 25,000 fragment structures serves as the building block supply, which were obtained by a straightforward fragmentation procedure applied to 36,000 known drugs. Eleven reaction schemes were implemented for both fragmentation and building block assembly. This combination of drug-derived building blocks and a restricted set of reaction schemes proved to be a key for the automatic development of novel, synthetically tractable structures. In a cyclic optimization process, molecular architectures were generated from a parent structure by virtual synthesis, and the best structure of a generation was selected as the parent for the subsequent TOPAS cycle. Similarity measures were used to define 'fitness', based on 2D-structural similarity or topological pharmacophore distance between the template molecule and the variants. The concept of varying library 'diversity' during a design process was consequently implemented by using adaptive variant distributions. The efficiency of the design algorithm was demonstrated for the de novo construction of potential thrombin inhibitors mimicking peptide and non-peptide template structures.

Mesh:

Year:  2000        PMID: 10896320     DOI: 10.1023/a:1008184403558

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  18 in total

Review 1.  Recognizing molecules with drug-like properties.

Authors:  W P Walters; M A Murcko
Journal:  Curr Opin Chem Biol       Date:  1999-08       Impact factor: 8.822

Review 2.  Chance favors the prepared mind--from serendipity to rational drug design.

Authors:  H Kubinyi
Journal:  J Recept Signal Transduct Res       Date:  1999 Jan-Jul       Impact factor: 2.092

3.  Combinatorial docking and combinatorial chemistry: design of potent non-peptide thrombin inhibitors.

Authors:  H J Böhm; D W Banner; L Weber
Journal:  J Comput Aided Mol Des       Date:  1999-01       Impact factor: 3.686

Review 4.  Modern computational chemistry and drug discovery: structure generating programs.

Authors:  R S Bohacek; C McMartin
Journal:  Curr Opin Chem Biol       Date:  1997-08       Impact factor: 8.822

5.  RECAP--retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry.

Authors:  X Q Lewell; D B Judd; S P Watson; M M Hann
Journal:  J Chem Inf Comput Sci       Date:  1998 May-Jun

6.  Placement of medium-sized molecular fragments into active sites of proteins.

Authors:  M Rarey; S Wefing; T Lengauer
Journal:  J Comput Aided Mol Des       Date:  1996-02       Impact factor: 3.686

7.  Functionality map analysis of the active site cleft of human thrombin.

Authors:  P D Grootenhuis; M Karplus
Journal:  J Comput Aided Mol Des       Date:  1996-02       Impact factor: 3.686

8.  MAB, a generally applicable molecular force field for structure modelling in medicinal chemistry.

Authors:  P R Gerber; K Müller
Journal:  J Comput Aided Mol Des       Date:  1995-06       Impact factor: 3.686

Review 9.  Genetic algorithms in molecular recognition and design.

Authors:  P Willett
Journal:  Trends Biotechnol       Date:  1995-12       Impact factor: 19.536

10.  Pharmacological characterization of a new highly effective synthetic thrombin inhibitor.

Authors:  B Kaiser; J Hauptmann; A Weiss; F Markwardt
Journal:  Biomed Biochim Acta       Date:  1985
View more
  28 in total

1.  A genetic algorithm for structure-based de novo design.

Authors:  S C Pegg; J J Haresco; I D Kuntz
Journal:  J Comput Aided Mol Des       Date:  2001-10       Impact factor: 3.686

2.  Similarity searching in large combinatorial chemistry spaces.

Authors:  M Rarey; M Stahl
Journal:  J Comput Aided Mol Des       Date:  2001-06       Impact factor: 3.686

3.  Genetic algorithm for the design of molecules with desired properties.

Authors:  Stefan Kamphausen; Nils Höltge; Frank Wirsching; Corinna Morys-Wortmann; Daniel Riester; Ruediger Goetz; Marcel Thürk; Andreas Schwienhorst
Journal:  J Comput Aided Mol Des       Date:  2002 Aug-Sep       Impact factor: 3.686

4.  Further development and validation of empirical scoring functions for structure-based binding affinity prediction.

Authors:  Renxiao Wang; Luhua Lai; Shaomeng Wang
Journal:  J Comput Aided Mol Des       Date:  2002-01       Impact factor: 3.686

5.  Development and testing of a de novo drug-design algorithm.

Authors:  Eric Pellegrini; Martin J Field
Journal:  J Comput Aided Mol Des       Date:  2003-10       Impact factor: 3.686

Review 6.  Designing antimicrobial peptides: form follows function.

Authors:  Christopher D Fjell; Jan A Hiss; Robert E W Hancock; Gisbert Schneider
Journal:  Nat Rev Drug Discov       Date:  2011-12-16       Impact factor: 84.694

7.  Designing the molecular future.

Authors:  Gisbert Schneider
Journal:  J Comput Aided Mol Des       Date:  2011-11-30       Impact factor: 3.686

8.  ENPDA: an evolutionary structure-based de novo peptide design algorithm.

Authors:  Ignasi Belda; Sergio Madurga; Xavier Llorà; Marc Martinell; Teresa Tarragó; Mireia G Piqueras; Ernesto Nicolás; Ernest Giralt
Journal:  J Comput Aided Mol Des       Date:  2005-11-03       Impact factor: 3.686

9.  The concept of template-based de novo design from drug-derived molecular fragments and its application to TAR RNA.

Authors:  Andreas Schüller; Marcel Suhartono; Uli Fechner; Yusuf Tanrikulu; Sven Breitung; Ute Scheffer; Michael W Göbel; Gisbert Schneider
Journal:  J Comput Aided Mol Des       Date:  2007-12-07       Impact factor: 3.686

10.  Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning.

Authors:  Arun Prasad Pandurangan; Tom L Blundell
Journal:  Protein Sci       Date:  2019-11-25       Impact factor: 6.725

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.