Dataset’s chemical diversity limits the generalizability of machine learning predictions

Machine learning model for non-equilibrium structures and energies of simple molecules

(2019) E. Iype et al.   JOURNAL OF CHEMICAL PHYSICS

Solving the electronic structure problem with machine learning

(2019) Anand Chandrasekaran et al.   npj Computational Materials

Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra

(2019) Kunal Ghosh et al.   Advanced Science

Accurate molecular polarizabilities with coupled cluster theory and machine learning

(2019) David M. Wilkins et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Training Neural Nets to Learn Reactive Potential Energy Surfaces Using Interactive Quantum Chemistry in Virtual Reality

(2019) Silvia Amabilino et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models

(2019) Chenru Duan et al.   Journal of Chemical Theory and Computation

Data sampling scheme for reproducing energies along reaction coordinates in high-dimensional neural network potentials

(2019) Yasuharu Okamoto   JOURNAL OF CHEMICAL PHYSICS

A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules

(2019) Lixue Cheng et al.   JOURNAL OF CHEMICAL PHYSICS

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges

(2019) Oliver T. Unke et al.   Journal of Chemical Theory and Computation

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

(2019) Justin S. Smith et al.   Nature Communications

Hierarchical modeling of molecular energies using a deep neural network

(2018) Nicholas Lubbers et al.   JOURNAL OF CHEMICAL PHYSICS

SchNet – A deep learning architecture for molecules and materials

(2018) K. T. Schütt et al.   JOURNAL OF CHEMICAL PHYSICS

Alchemical and structural distribution based representation for universal quantum machine learning

(2018) Felix A. Faber et al.   JOURNAL OF CHEMICAL PHYSICS

A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information

(2018) Oliver T. Unke et al.   JOURNAL OF CHEMICAL PHYSICS

Predicting molecular properties with covariant compositional networks

(2018) Truong Son Hy et al.   JOURNAL OF CHEMICAL PHYSICS

Constant size descriptors for accurate machine learning models of molecular properties

(2018) Christopher R. Collins et al.   JOURNAL OF CHEMICAL PHYSICS

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

(2018) K. T. Schütt et al.   Journal of Chemical Theory and Computation

Comparison Study on the Prediction of Multiple Molecular Properties by Various Neural Networks

(2018) Fang Hou et al.   JOURNAL OF PHYSICAL CHEMISTRY A

PubChem 2019 update: improved access to chemical data

(2018) Sunghwan Kim et al.   NUCLEIC ACIDS RESEARCH

Transferable Machine-Learning Model of the Electron Density

(2018) Andrea Grisafi et al.   ACS Central Science

PubChemQC Project: A Large-Scale First-Principles Electronic Structure Database for Data-Driven Chemistry

(2017) Maho Nakata et al.   Journal of Chemical Information and Modeling

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

(2017) Felix A. Faber et al.   Journal of Chemical Theory and Computation

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

Quantum-chemical insights from deep tensor neural networks

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

Comparative analyses of structural features and scaffold diversity for purchasable compound libraries

(2017) Jun Shang et al.   Journal of Cheminformatics

Machine learning unifies the modeling of materials and molecules

(2017) Albert P. Bartók et al.   Science Advances

Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals

(2016) Florbela Pereira et al.   Journal of Chemical Information and Modeling

Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity

(2016) Bing Huang et al.   JOURNAL OF CHEMICAL PHYSICS

Understanding deep convolutional networks

(2016) Stéphane Mallat   PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Scaffold analysis of PubChem database as background for hierarchical scaffold-based visualization

(2016) Jakub Velkoborsky et al.   Journal of Cheminformatics

Consensus Diversity Plots: a global diversity analysis of chemical libraries

(2016) Mariana González-Medina et al.   Journal of Cheminformatics

Many Molecular Properties from One Kernel in Chemical Space

(2015) Raghunathan Ramakrishnan et al.   CHIMIA

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

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

InChI, the IUPAC International Chemical Identifier

(2015) Stephen R Heller et al.   Journal of Cheminformatics

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

Scaffold network generator: a tool for mining molecular structures

(2013) Matt K. Matlock et al.   BIOINFORMATICS

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

(2013) Katja Hansen et al.   Journal of Chemical Theory and Computation

Machine learning of molecular electronic properties in chemical compound space

(2013) Grégoire Montavon et al.   NEW JOURNAL OF PHYSICS

On representing chemical environments

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

Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17

(2012) Lars Ruddigkeit et al.   Journal of Chemical Information and Modeling

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Lessons Learned from Molecular Scaffold Analysis

(2011) Ye Hu et al.   Journal of Chemical Information and Modeling

Open Babel: An open chemical toolbox

(2011) Noel M O'Boyle et al.   Journal of Cheminformatics

Functionality Pattern Matching as an Efficient Complementary Structure/Reaction Search Tool: an Open-Source Approach

(2010) Norbert Haider   MOLECULES

Interactive exploration of chemical space with Scaffold Hunter

(2009) Stefan Wetzel et al.   Nature Chemical Biology

PubChem: a public information system for analyzing bioactivities of small molecules

(2009) Y. Wang et al.   NUCLEIC ACIDS RESEARCH

Structural Diversity of Organic Chemistry. A Scaffold Analysis of the CAS Registry

(2008) Alan H. Lipkus et al.   JOURNAL OF ORGANIC CHEMISTRY