Literature DB >> 30118593

Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery.

Kristina Preuer1, Philipp Renz1, Thomas Unterthiner1, Sepp Hochreiter1, Günter Klambauer1.   

Abstract

The new wave of successful generative models in machine learning has increased the interest in deep learning driven de novo drug design. However, method comparison is difficult because of various flaws of the currently employed evaluation metrics. We propose an evaluation metric for generative models called Fréchet ChemNet distance (FCD). The advantage of the FCD over previous metrics is that it can detect whether generated molecules are diverse and have similar chemical and biological properties as real molecules.

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Year:  2018        PMID: 30118593     DOI: 10.1021/acs.jcim.8b00234

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


  22 in total

1.  Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity.

Authors:  Chuipu Cai; Pengfei Guo; Yadi Zhou; Jingwei Zhou; Qi Wang; Fengxue Zhang; Jiansong Fang; Feixiong Cheng
Journal:  J Chem Inf Model       Date:  2019-02-15       Impact factor: 4.956

Review 2.  Applications of machine learning in drug discovery and development.

Authors:  Jessica Vamathevan; Dominic Clark; Paul Czodrowski; Ian Dunham; Edgardo Ferran; George Lee; Bin Li; Anant Madabhushi; Parantu Shah; Michaela Spitzer; Shanrong Zhao
Journal:  Nat Rev Drug Discov       Date:  2019-06       Impact factor: 84.694

Review 3.  De novo molecular drug design benchmarking.

Authors:  Lauren L Grant; Clarissa S Sit
Journal:  RSC Med Chem       Date:  2021-06-03

4.  FAME: Fragment-based Conditional Molecular Generation for Phenotypic Drug Discovery.

Authors:  Thai-Hoang Pham; Lei Xie; Ping Zhang
Journal:  Proc SIAM Int Conf Data Min       Date:  2022

Review 5.  Deep generative models for peptide design.

Authors:  Fangping Wan; Daphne Kontogiorgos-Heintz; Cesar de la Fuente-Nunez
Journal:  Digit Discov       Date:  2022-03-31

6.  Memory augmented recurrent neural networks for de-novo drug design.

Authors:  Naveen Suresh; Neelesh Chinnakonda Ashok Kumar; Srikumar Subramanian; Gowri Srinivasa
Journal:  PLoS One       Date:  2022-06-23       Impact factor: 3.752

Review 7.  Artificial Intelligence for Drug Toxicity and Safety.

Authors:  Anna O Basile; Alexandre Yahi; Nicholas P Tatonetti
Journal:  Trends Pharmacol Sci       Date:  2019-08-02       Impact factor: 14.819

8.  Selecting molecules with diverse structures and properties by maximizing submodular functions of descriptors learned with graph neural networks.

Authors:  Tomohiro Nakamura; Shinsaku Sakaue; Kaito Fujii; Yu Harabuchi; Satoshi Maeda; Satoru Iwata
Journal:  Sci Rep       Date:  2022-01-21       Impact factor: 4.996

Review 9.  Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design.

Authors:  Eugene Lin; Chieh-Hsin Lin; Hsien-Yuan Lane
Journal:  Molecules       Date:  2020-07-16       Impact factor: 4.411

10.  Randomized SMILES strings improve the quality of molecular generative models.

Authors:  Josep Arús-Pous; Simon Viet Johansson; Oleksii Prykhodko; Esben Jannik Bjerrum; Christian Tyrchan; Jean-Louis Reymond; Hongming Chen; Ola Engkvist
Journal:  J Cheminform       Date:  2019-11-21       Impact factor: 5.514

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