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

Deep Generative Models for Molecular Science

(2018) Peter B. Jørgensen et al.   Molecular Informatics

Digitization of multistep organic synthesis in reactionware for on-demand pharmaceuticals

(2018) Philip J. Kitson et al.   SCIENCE

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

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

The Matter Simulation (R)evolution

(2018) Alán Aspuru-Guzik et al.   ACS Central Science

Inverse design in search of materials with target functionalities

(2018) Alex Zunger   Nature Reviews Chemistry

Planning chemical syntheses with deep neural networks and symbolic AI

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

Bayesian molecular design with a chemical language model

(2017) Hisaki Ikebata et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

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

Wavelet Scattering Regression of Quantum Chemical Energies

(2017) Matthew Hirn et al.   MULTISCALE MODELING & SIMULATION

The drug-maker's guide to the galaxy

(2017) Asher Mullard   NATURE

Solar fuels photoanode materials discovery by integrating high-throughput theory and experiment

(2017) Qimin Yan et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

ChemTS: an efficient python library for de novo molecular generation

(2017) Xiufeng Yang et al.   SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS

Quantum-chemical insights from deep tensor neural networks

(2017) Kristof T. Schütt et al.   Nature Communications

Molecular de-novo design through deep reinforcement learning

(2017) Marcus Olivecrona et al.   Journal of Cheminformatics

Optimizing Chemical Reactions with Deep Reinforcement Learning

(2017) Zhenpeng Zhou et al.   ACS Central Science

High-throughput screening of inorganic compounds for the discovery of novel dielectric and optical materials

(2017) Ioannis Petousis et al.   Scientific Data

Molecular graph convolutions: moving beyond fingerprints

(2016) Steven Kearnes et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

Natural Products as Sources of New Drugs from 1981 to 2014

(2016) David J. Newman et al.   JOURNAL OF NATURAL PRODUCTS

Mastering the game of Go with deep neural networks and tree search

(2016) David Silver et al.   NATURE

Machine-learning-assisted materials discovery using failed experiments

(2016) Paul Raccuglia et al.   NATURE

Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

(2016) Rafael Gómez-Bombarelli et al.   NATURE MATERIALS

Taking the Human Out of the Loop: A Review of Bayesian Optimization

(2016) Bobak Shahriari et al.   PROCEEDINGS OF THE IEEE

Neural Networks for the Prediction of Organic Chemistry Reactions

(2016) Jennifer N. Wei et al.   ACS Central Science

A redox-flow battery with an alloxazine-based organic electrolyte

(2016) Kaixiang Lin et al.   Nature Energy

Autonomy in materials research: a case study in carbon nanotube growth

(2016) Pavel Nikolaev et al.   npj Computational Materials

The Chemical Space Project

(2015) Jean-Louis Reymond   ACCOUNTS OF CHEMICAL RESEARCH

Organic Synthesis: March of the Machines

(2015) Steven V. Ley et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery

(2015) Edward O. Pyzer-Knapp et al.   Annual Review of Materials Research

The Electrolyte Genome project: A big data approach in battery materials discovery

(2015) Xiaohui Qu et al.   COMPUTATIONAL MATERIALS SCIENCE

Strategy To Discover Diverse Optimal Molecules in the Small Molecule Universe

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

First-Principles Molecular Structure Search with a Genetic Algorithm

(2015) Adriana Supady et al.   Journal of Chemical Information and Modeling

Accelerating Electrolyte Discovery for Energy Storage with High-Throughput Screening

(2015) Lei Cheng et al.   Journal of Physical Chemistry Letters

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

(2015) Katja Hansen et al.   Journal of Physical Chemistry Letters

Machine learning: Trends, perspectives, and prospects

(2015) M. I. Jordan et al.   SCIENCE

Dissolving the Periodic Table in Cubic Zirconia: Data Mining to Discover Chemical Trends

(2014) Bryce Meredig et al.   CHEMISTRY OF MATERIALS

Inverse quantum chemistry: Concepts and strategies for rational compound design

(2014) Thomas Weymuth et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

A metal-free organic–inorganic aqueous flow battery

(2014) Brian Huskinson et al.   NATURE

Exploring the Possibilities and Limitations of a Nanomaterials Genome

(2014) Chenxi Qian et al.   Small

Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project

(2013) Johannes Hachmann et al.   Energy & Environmental Science

First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties

(2013) O. Anatole von Lilienfeld   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Efficient Computational Screening of Organic Polymer Photovoltaics

(2013) Ilana Y. Kanal et al.   Journal of Physical Chemistry Letters

Stochastic Voyages into Uncharted Chemical Space Produce a Representative Library of All Possible Drug-Like Compounds

(2013) Aaron M. Virshup et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

Discovery of small molecule cancer drugs: Successes, challenges and opportunities

(2012) Swen Hoelder et al.   Molecular Oncology

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

A high-throughput infrastructure for density functional theory calculations

(2011) Anubhav Jain et al.   COMPUTATIONAL MATERIALS SCIENCE

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Inverse Design and Synthesis of acac-Coumarin Anchors for Robust TiO2Sensitization

(2011) Dequan Xiao et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

A Simple, Multidimensional Approach to High-Throughput Discovery of Catalytic Reactions

(2011) D. W. Robbins et al.   SCIENCE

Extended-Connectivity Fingerprints

(2010) David Rogers et al.   Journal of Chemical Information and Modeling