Literature DB >> 29154440

Mapping of the Available Chemical Space versus the Chemical Universe of Lead-Like Compounds.

Arkadii Lin1, Dragos Horvath1, Valentina Afonina1,2, Gilles Marcou1, Jean-Louis Reymond3, Alexandre Varnek1.   

Abstract

This is, to our knowledge, the most comprehensive analysis to date based on generative topographic mapping (GTM) of fragment-like chemical space (40 million molecules with no more than 17 heavy atoms, both from the theoretically enumerated GDB-17 and real-world PubChem/ChEMBL databases). The challenge was to prove that a robust map of fragment-like chemical space can actually be built, in spite of a limited (≪105 ) maximal number of compounds ("frame set") usable for fitting the GTM manifold. An evolutionary map building strategy has been updated with a "coverage check" step, which discards manifolds failing to accommodate compounds outside the frame set. The evolved map has a good propensity to separate actives from inactives for more than 20 external structure-activity sets. It was proven to properly accommodate the entire collection of 40 m compounds. Next, it served as a library comparison tool to highlight biases of real-world molecules (PubChem and ChEMBL) versus the universe of all possible species represented by FDB-17, a fragment-like subset of GDB-17 containing 10 million molecules. Specific patterns, proper to some libraries and absent from others (diversity holes), were highlighted.
© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords:  computer chemistry; generative topographic mapping; library comparison; molecular diversity; structure analysis

Mesh:

Substances:

Year:  2018        PMID: 29154440     DOI: 10.1002/cmdc.201700561

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


  7 in total

1.  Multi-task generative topographic mapping in virtual screening.

Authors:  Arkadii Lin; Dragos Horvath; Gilles Marcou; Bernd Beck; Alexandre Varnek
Journal:  J Comput Aided Mol Des       Date:  2019-02-09       Impact factor: 3.686

Review 2.  Synthetic Biology and Computer-Based Frameworks for Antimicrobial Peptide Discovery.

Authors:  Marcelo D T Torres; Jicong Cao; Octavio L Franco; Timothy K Lu; Cesar de la Fuente-Nunez
Journal:  ACS Nano       Date:  2021-02-04       Impact factor: 15.881

3.  Parallel Generative Topographic Mapping: An Efficient Approach for Big Data Handling.

Authors:  Arkadii Lin; Igor I Baskin; Gilles Marcou; Dragos Horvath; Bernd Beck; Alexandre Varnek
Journal:  Mol Inform       Date:  2020-04-29       Impact factor: 3.353

4.  LEADD: Lamarckian evolutionary algorithm for de novo drug design.

Authors:  Alan Kerstjens; Hans De Winter
Journal:  J Cheminform       Date:  2022-01-15       Impact factor: 5.514

Review 5.  Computational analyses of mechanism of action (MoA): data, methods and integration.

Authors:  Maria-Anna Trapotsi; Layla Hosseini-Gerami; Andreas Bender
Journal:  RSC Chem Biol       Date:  2021-12-22

6.  Progress on open chemoinformatic tools for expanding and exploring the chemical space.

Authors:  José L Medina-Franco; Norberto Sánchez-Cruz; Edgar López-López; Bárbara I Díaz-Eufracio
Journal:  J Comput Aided Mol Des       Date:  2021-06-18       Impact factor: 4.179

Review 7.  Antimicrobial Susceptibility Testing of Antimicrobial Peptides to Better Predict Efficacy.

Authors:  Derry K Mercer; Marcelo D T Torres; Searle S Duay; Emma Lovie; Laura Simpson; Maren von Köckritz-Blickwede; Cesar de la Fuente-Nunez; Deborah A O'Neil; Alfredo M Angeles-Boza
Journal:  Front Cell Infect Microbiol       Date:  2020-07-07       Impact factor: 5.293

  7 in total

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