Literature DB >> 20297844

Drug- and lead-likeness, target class, and molecular diversity analysis of 7.9 million commercially available organic compounds provided by 29 suppliers.

Alexander Chuprina1, Oleg Lukin, Robert Demoiseaux, Alexander Buzko, Alexander Shivanyuk.   

Abstract

A database of 7.9 million compounds commercially available from 29 suppliers in 2008-2009 was assembled and analyzed. 5.2 million structures of this database were identified to be unique and were subjected to an assessment of physical and biological properties and estimation of molecular diversity. The rules of Lipinski and Veber were applied to the molecular weight, the calculated water/n-octanol partition coefficients (Clog P), the calculated aqueous solubility (log S), the numbers of hydrogen-bond donors and acceptors, and the calculated Caco-2 membrane permeability to identify the drug-like compounds, whereas the toxicity/reactivity filters were used to remove the structures with biologically undesired functional groups. This filtering resulted in 2.0 million (39%) structures perfectly suitable for high-throughput screening of biological activity. Modified filters applied to identify lead-like structures revealed that 16% of the unique compounds could be potential leads. Assessment of the biological activities, the analysis of diversity, and the sizes of exclusive sets of compounds are presented.

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Year:  2010        PMID: 20297844     DOI: 10.1021/ci900464s

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  23 in total

1.  Dark chemical matter as a promising starting point for drug lead discovery.

Authors:  Anne Mai Wassermann; Eugen Lounkine; Dominic Hoepfner; Gaelle Le Goff; Frederick J King; Christian Studer; John M Peltier; Melissa L Grippo; Vivian Prindle; Jianshi Tao; Ansgar Schuffenhauer; Iain M Wallace; Shanni Chen; Philipp Krastel; Amanda Cobos-Correa; Christian N Parker; John W Davies; Meir Glick
Journal:  Nat Chem Biol       Date:  2015-10-19       Impact factor: 15.040

2.  Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds.

Authors:  Vinicius M Alves; Eugene Muratov; Denis Fourches; Judy Strickland; Nicole Kleinstreuer; Carolina H Andrade; Alexander Tropsha
Journal:  Toxicol Appl Pharmacol       Date:  2015-01-03       Impact factor: 4.219

3.  Emerging topics in structure-based virtual screening.

Authors:  Giulio Rastelli
Journal:  Pharm Res       Date:  2013-03-07       Impact factor: 4.200

4.  Computational and biophysical approaches to protein-protein interaction inhibition of Plasmodium falciparum AMA1/RON2 complex.

Authors:  Emilie Pihan; Roberto F Delgadillo; Michelle L Tonkin; Martine Pugnière; Maryse Lebrun; Martin J Boulanger; Dominique Douguet
Journal:  J Comput Aided Mol Des       Date:  2015-03-31       Impact factor: 3.686

5.  Identification of potential trypanothione reductase inhibitors among commercially available β-carboline derivatives using chemical space, lead-like and drug-like filters, pharmacophore models and molecular docking.

Authors:  Jorge Rodríguez-Becerra; Lizethly Cáceres-Jensen; José Hernández-Ramos; Lorena Barrientos
Journal:  Mol Divers       Date:  2017-06-27       Impact factor: 2.943

6.  Solid-phase synthesis and screening of N-acylated polyamine (NAPA) combinatorial libraries for protein binding.

Authors:  Jaclyn A Iera; Lisa M Miller Jenkins; Hiroshi Kajiyama; Jeffrey B Kopp; Daniel H Appella
Journal:  Bioorg Med Chem Lett       Date:  2010-09-17       Impact factor: 2.823

Review 7.  Machine learning in chemoinformatics and drug discovery.

Authors:  Yu-Chen Lo; Stefano E Rensi; Wen Torng; Russ B Altman
Journal:  Drug Discov Today       Date:  2018-05-08       Impact factor: 7.851

8.  An informatic pipeline for managing high-throughput screening experiments and analyzing data from stereochemically diverse libraries.

Authors:  Carol A Mulrooney; David L Lahr; Michael J Quintin; Willmen Youngsaye; Dennis Moccia; Jacob K Asiedu; Evan L Mulligan; Lakshmi B Akella; Lisa A Marcaurelle; Philip Montgomery; Joshua A Bittker; Paul A Clemons; Stephen Brudz; Sivaraman Dandapani; Jeremy R Duvall; Nicola J Tolliday; Andrea De Souza
Journal:  J Comput Aided Mol Des       Date:  2013-04-13       Impact factor: 3.686

9.  Combinatorial synthesis of chemical building blocks 1. Azomethines.

Authors:  Sergey V Ryabukhin; Dmitriy M Panov; Andrey S Plaskon; Alexander Chuprina; Sergey E Pipko; Andrey A Tolmachev; Alexander N Shivanyuk
Journal:  Mol Divers       Date:  2012-10-30       Impact factor: 2.943

10.  ARF6 Is an Actionable Node that Orchestrates Oncogenic GNAQ Signaling in Uveal Melanoma.

Authors:  Jae Hyuk Yoo; Dallas S Shi; Allie H Grossmann; Lise K Sorensen; ZongZhong Tong; Tara M Mleynek; Aaron Rogers; Weiquan Zhu; Jackson R Richards; Jacob M Winter; Jie Zhu; Christine Dunn; Ashok Bajji; Mark Shenderovich; Alan L Mueller; Scott E Woodman; J William Harbour; Kirk R Thomas; Shannon J Odelberg; Kirill Ostanin; Dean Y Li
Journal:  Cancer Cell       Date:  2016-06-02       Impact factor: 31.743

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