De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

High-Throughput Gene Expression Profiles to Define Drug Similarity and Predict Compound Activity

(2018) Hans De Wolf et al.   ASSAY AND DRUG DEVELOPMENT TECHNOLOGIES

StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

(2018) Han Zhang et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

De Novo Design of Bioactive Small Molecules by Artificial Intelligence

(2018) Daniel Merk et al.   Molecular Informatics

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

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

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

(2018) Kristina Preuer et al.   Journal of Chemical Information and Modeling

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Genetic and transcriptional evolution alters cancer cell line drug response

(2018) Uri Ben-David et al.   NATURE

Inverse molecular design using machine learning: Generative models for matter engineering

(2018) Benjamin Sanchez-Lengeling et al.   SCIENCE

Deep reinforcement learning for de novo drug design

(2018) Mariya Popova et al.   Science Advances

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

The Drug Repurposing Hub: a next-generation drug library and information resource

(2017) Steven M Corsello et al.   NATURE MEDICINE

Toxicogenomic module associations with pathogenesis: a network-based approach to understanding drug toxicity

(2017) J J Sutherland et al.   PHARMACOGENOMICS JOURNAL

A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury

(2017) Pekka Kohonen et al.   Nature Communications

A novel descriptor based on atom-pair properties

(2017) Masataka Kuroda   Journal of Cheminformatics

Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

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

Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics

(2017) Michio Iwata et al.   Scientific Reports

De Novo Design at the Edge of Chaos

(2016) Petra Schneider et al.   JOURNAL OF MEDICINAL CHEMISTRY

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

(2016) Alexander Aliper et al.   MOLECULAR PHARMACEUTICS

The ChEMBL database in 2017

(2016) Anna Gaulton et al.   NUCLEIC ACIDS RESEARCH

Pesticide toxicogenomics across scales: in vitro transcriptome predicts mechanisms and outcomes of exposure in vivo

(2016) Immacolata Porreca et al.   Scientific Reports

The Calculation of Molecular Structural Similarity: Principles and Practice

(2014) Peter Willett   Molecular Informatics

Transcriptional data: a new gateway to drug repositioning?

(2012) Francesco Iorio et al.   DRUG DISCOVERY TODAY

Using transcriptome sequencing to identify mechanisms of drug action and resistance

(2012) Sarah A Wacker et al.   Nature Chemical Biology

Molecular Evolution of a Peptide GPCR Ligand Driven by Artificial Neural Networks

(2012) Sebastian Bandholtz et al.   PLoS One

Virtual screening: an endless staircase?

(2010) Gisbert Schneider   NATURE REVIEWS DRUG DISCOVERY

High-throughput andin silicoscreenings in drug discovery

(2009) Sharangdhar S Phatak et al.   Expert Opinion on Drug Discovery

Quantifying biogenic bias in screening libraries

(2009) Jérôme Hert et al.   Nature Chemical Biology

Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions

(2009) Peter Ertl et al.   Journal of Cheminformatics