Literature DB >> 20838974

De novo drug design.

Markus Hartenfeller1, Gisbert Schneider.   

Abstract

Computer-assisted molecular design supports drug discovery by suggesting novel chemotypes and compound modifications for lead structure optimization. While the aspect of synthetic feasibility of the automatically designed compounds has been neglected for a long time, we are currently witnessing an increased interest in this topic. Here, we review state-of-the-art software for de novo drug design with a special emphasis on fragment-based techniques that generate druglike, synthetically accessible compounds. The importance of scoring functions that can be used to predict compound reactivity and potency is highlighted, and several promising solutions are discussed. Recent practical validation studies are presented that have already demonstrated that rule-based fragment assembly can result in novel synthesizable compounds with druglike properties and a desired biological activity.

Mesh:

Substances:

Year:  2011        PMID: 20838974     DOI: 10.1007/978-1-60761-839-3_12

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  23 in total

Review 1.  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

Review 2.  From laptop to benchtop to bedside: structure-based drug design on protein targets.

Authors:  Lu Chen; John K Morrow; Hoang T Tran; Sharangdhar S Phatak; Lei Du-Cuny; Shuxing Zhang
Journal:  Curr Pharm Des       Date:  2012       Impact factor: 3.116

3.  Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus.

Authors:  Daniel Reker; Tiago Rodrigues; Petra Schneider; Gisbert Schneider
Journal:  Proc Natl Acad Sci U S A       Date:  2014-03-03       Impact factor: 11.205

4.  Visualisation of the chemical space of fragments, lead-like and drug-like molecules in PubChem.

Authors:  Ruud van Deursen; Lorenz C Blum; Jean-Louis Reymond
Journal:  J Comput Aided Mol Des       Date:  2011-05-27       Impact factor: 3.686

5.  Visualisation and subsets of the chemical universe database GDB-13 for virtual screening.

Authors:  Lorenz C Blum; Ruud van Deursen; Jean-Louis Reymond
Journal:  J Comput Aided Mol Des       Date:  2011-05-27       Impact factor: 3.686

6.  Systemic evolutionary chemical space exploration for drug discovery.

Authors:  Chong Lu; Shien Liu; Weihua Shi; Jun Yu; Zhou Zhou; Xiaoxiao Zhang; Xiaoli Lu; Faji Cai; Ning Xia; Yikai Wang
Journal:  J Cheminform       Date:  2022-04-01       Impact factor: 5.514

7.  Predicting novel drug candidates against Covid-19 using generative deep neural networks.

Authors:  Santhosh Amilpur; Raju Bhukya
Journal:  J Mol Graph Model       Date:  2021-10-13       Impact factor: 2.518

8.  DOGS: reaction-driven de novo design of bioactive compounds.

Authors:  Markus Hartenfeller; Heiko Zettl; Miriam Walter; Matthias Rupp; Felix Reisen; Ewgenij Proschak; Sascha Weggen; Holger Stark; Gisbert Schneider
Journal:  PLoS Comput Biol       Date:  2012-02-16       Impact factor: 4.475

9.  De Novo Design of Bioactive Small Molecules by Artificial Intelligence.

Authors:  Daniel Merk; Lukas Friedrich; Francesca Grisoni; Gisbert Schneider
Journal:  Mol Inform       Date:  2018-01-10       Impact factor: 3.353

Review 10.  Computer-aided drug discovery.

Authors:  Jürgen Bajorath
Journal:  F1000Res       Date:  2015-08-26
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.