Literature DB >> 24372539

De novo design of drug-like molecules by a fragment-based molecular evolutionary approach.

Kentaro Kawai1, Naoya Nagata, Yoshimasa Takahashi.   

Abstract

This paper describes a similarity-driven simple evolutionary approach to producing candidate molecules of new drugs. The aim of the method is to explore the candidates that are structurally similar to the reference molecule and yet somewhat different in not only peripheral chains but also their scaffolds. The method employs a known active molecule of our interest as a reference molecule which is used to navigate a huge chemical space. The reference molecule is also used to obtain seed fragments. An initial set of individual structures is prepared with the seed fragments and additional fragments using several connection rules. The fragment library is preferably prepared from a collection of known molecules related to the target of the reference molecule. Every fragment of the library can be used for fragment-based mutation. All the fragments are categorized into three classes; rings, linkers, and side chains. New individuals are produced by the crossover and the fragment-based mutation with the fragment library. Computer experiments with our own fragment library prepared from GPCR SARfari verified the feasibility of our approach to drug discovery.

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Year:  2014        PMID: 24372539     DOI: 10.1021/ci400418c

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


  11 in total

Review 1.  Computational Fragment-Based Drug Design: Current Trends, Strategies, and Applications.

Authors:  Yuemin Bian; Xiang-Qun Sean Xie
Journal:  AAPS J       Date:  2018-04-09       Impact factor: 4.009

Review 2.  Trends in application of advancing computational approaches in GPCR ligand discovery.

Authors:  Siyu Zhu; Meixian Wu; Ziwei Huang; Jing An
Journal:  Exp Biol Med (Maywood)       Date:  2021-02-27

3.  A graph-based approach to construct target-focused libraries for virtual screening.

Authors:  Misagh Naderi; Chris Alvin; Yun Ding; Supratik Mukhopadhyay; Michal Brylinski
Journal:  J Cheminform       Date:  2016-03-15       Impact factor: 5.514

4.  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

5.  Bayesian molecular design with a chemical language model.

Authors:  Hisaki Ikebata; Kenta Hongo; Tetsu Isomura; Ryo Maezono; Ryo Yoshida
Journal:  J Comput Aided Mol Des       Date:  2017-03-09       Impact factor: 3.686

6.  AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization.

Authors:  Jacob O Spiegel; Jacob D Durrant
Journal:  J Cheminform       Date:  2020-04-17       Impact factor: 5.514

7.  Denovo designing, retro-combinatorial synthesis, and molecular dynamics analysis identify novel antiviral VTRM1.1 against RNA-dependent RNA polymerase of SARS CoV2 virus.

Authors:  Vishvanath Tiwari
Journal:  Int J Biol Macromol       Date:  2021-01-07       Impact factor: 6.953

8.  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

9.  Transmol: repurposing a language model for molecular generation.

Authors:  Rustam Zhumagambetov; Ferdinand Molnár; Vsevolod A Peshkov; Siamac Fazli
Journal:  RSC Adv       Date:  2021-07-27       Impact factor: 4.036

10.  De novo design, retrosynthetic analysis and combinatorial synthesis of a hybrid antiviral (VTAR-01) to inhibit the interaction of SARS-CoV2 spike glycoprotein with human angiotensin-converting enzyme 2.

Authors:  Vishvanath Tiwari
Journal:  Biol Open       Date:  2020-10-15       Impact factor: 2.422

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