Molecular generative model based on conditional variational autoencoder for de novo molecular design

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

(2018) Rafael Gómez-Bombarelli et al.   ACS Central Science

Chemical Space Mimicry for Drug Discovery

(2017) William Yuan et al.   Journal of Chemical Information and Modeling

Generative Recurrent Networks for De Novo Drug Design

(2017) Anvita Gupta et al.   Molecular Informatics

druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

(2017) Artur Kadurin et al.   MOLECULAR PHARMACEUTICS

Molecular de-novo design through deep reinforcement learning

(2017) Marcus Olivecrona et al.   Journal of Cheminformatics

Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

(2017) Marwin H. S. Segler et al.   ACS Central Science

Strategy To Discover Diverse Optimal Molecules in the Small Molecule Universe

(2015) Chetan Rupakheti et al.   Journal of Chemical Information and Modeling

PubChem Substance and Compound databases

(2015) Sunghwan Kim et al.   NUCLEIC ACIDS RESEARCH

Estimation of the size of drug-like chemical space based on GDB-17 data

(2013) P. G. Polishchuk et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review

(2012) Tiejun Cheng et al.   AAPS Journal

Recognizing Pitfalls in Virtual Screening: A Critical Review

(2012) Thomas Scior et al.   Journal of Chemical Information and Modeling

ZINC: A Free Tool to Discover Chemistry for Biology

(2012) John J. Irwin et al.   Journal of Chemical Information and Modeling

Enabling future drug discovery by de novo design

(2011) Markus Hartenfeller et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

Chemical space as a source for new drugs

(2010) Jean-Louis Reymond et al.   MedChemComm