Literature DB >> 30844149

PubChem and ChEMBL beyond Lipinski.

Alice Capecchi1, Mahendra Awale1, Daniel Probst1, Jean-Louis Reymond1.   

Abstract

Seven million of the currently 94 million entries in the PubChem database break at least one of the four Lipinski constraints for oral bioavailability, 183,185 of which are also found in the ChEMBL database. These non-Lipinski PubChem (NLP) and ChEMBL (NLC) subsets are interesting because they contain new modalities that can display biological properties not accessible to small molecule drugs. Unfortunately, the current search tools in PubChem and ChEMBL are designed for small molecules and are not well suited to explore these subsets, which therefore remain poorly appreciated. Herein we report MXFP (macromolecule extended atom-pair fingerprint), a 217-D fingerprint tailored to analyze large molecules in terms of molecular shape and pharmacophores. We implement MXFP in two web-based applications, the first one to visualize NLP and NLC interactively using Faerun (http://faerun.gdb.tools/), the second one to perform MXFP nearest neighbor searches in NLP and NLC (http://similaritysearch.gdb.tools/). We show that these tools provide a meaningful insight into the diversity of large molecules in NLP and NLC. The interactive tools presented here are publicly available at http://gdb.unibe.ch and can be used freely to explore and better understand the diversity of non-Lipinski molecules in PubChem and ChEMBL.
© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords:  biomolecules; chemical space; databases; fingerprints; visualization

Mesh:

Substances:

Year:  2019        PMID: 30844149     DOI: 10.1002/minf.201900016

Source DB:  PubMed          Journal:  Mol Inform        ISSN: 1868-1743            Impact factor:   3.353


  6 in total

Review 1.  Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling.

Authors:  Linlin Zhao; Heather L Ciallella; Lauren M Aleksunes; Hao Zhu
Journal:  Drug Discov Today       Date:  2020-07-11       Impact factor: 7.851

2.  One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome.

Authors:  Alice Capecchi; Daniel Probst; Jean-Louis Reymond
Journal:  J Cheminform       Date:  2020-06-12       Impact factor: 5.514

Review 3.  Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.

Authors:  Vertika Gautam; Anand Gaurav; Neeraj Masand; Vannajan Sanghiran Lee; Vaishali M Patil
Journal:  Mol Divers       Date:  2022-07-11       Impact factor: 3.364

4.  Calculation of the Global and Local Conceptual DFT Indices for the Prediction of the Chemical Reactivity Properties of Papuamides A-F Marine Drugs.

Authors:  Norma Flores-Holguín; Juan Frau; Daniel Glossman-Mitnik
Journal:  Molecules       Date:  2019-09-11       Impact factor: 4.411

Review 5.  Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.

Authors:  Maryam Bagherian; Elyas Sabeti; Kai Wang; Maureen A Sartor; Zaneta Nikolovska-Coleska; Kayvan Najarian
Journal:  Brief Bioinform       Date:  2021-01-18       Impact factor: 11.622

6.  Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning.

Authors:  Alice Capecchi; Jean-Louis Reymond
Journal:  Biomolecules       Date:  2020-09-28
  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.