Deep learning enables rapid identification of potent DDR1 kinase inhibitors

GuacaMol: Benchmarking Models for de Novo Molecular Design

(2019) Nathan Brown et al.   Journal of Chemical Information and Modeling

DDR1 role in fibrosis and its pharmacological targeting

(2019) Solange Moll et al.   BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR CELL RESEARCH

Reinforced Adversarial Neural Computer for de Novo Molecular Design

(2018) Evgeny Putin et al.   Journal of Chemical Information and Modeling

De Novo Design of Bioactive Small Molecules by Artificial Intelligence

(2018) Daniel Merk et al.   Molecular Informatics

Adversarial Threshold Neural Computer for Molecular de Novo Design

(2018) Evgeny Putin et al.   MOLECULAR PHARMACEUTICS

3D Molecular Representations Based on the Wave Transform for Convolutional Neural Networks

(2018) Denis Kuzminykh et al.   MOLECULAR PHARMACEUTICS

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

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

Prototype-Based Compound Discovery Using Deep Generative Models

(2018) Shahar Harel et al.   MOLECULAR PHARMACEUTICS

Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery

(2018) Daniil Polykovskiy et al.   MOLECULAR PHARMACEUTICS

Planning chemical syntheses with deep neural networks and symbolic AI

(2018) Marwin H. S. Segler et al.   NATURE

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

(2018) Benjamin Sanchez-Lengeling et al.   SCIENCE

DNA-Encoded Library-Derived DDR1 Inhibitor Prevents Fibrosis and Renal Function Loss in a Genetic Mouse Model of Alport Syndrome

(2018) Hans Richter et al.   ACS Chemical Biology

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

Applications of Deep Learning in Biomedicine

(2016) Polina Mamoshina et al.   MOLECULAR PHARMACEUTICS

The $2.6 Billion Pill — Methodologic and Policy Considerations

(2015) Jerry Avorn   NEW ENGLAND JOURNAL OF MEDICINE

ZINC: A Free Tool to Discover Chemistry for Biology

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

Tensor-Train Decomposition

(2011) I. V. Oseledets   SIAM JOURNAL ON SCIENTIFIC COMPUTING

How to improve R&D productivity: the pharmaceutical industry's grand challenge

(2010) Steven M. Paul et al.   NATURE REVIEWS DRUG DISCOVERY