BAND NN: A Deep Learning Framework for Energy Prediction and Geometry Optimization of Organic Small Molecules

PPI-Detect: A support vector machine model for sequence-based prediction of protein-protein interactions

(2019) Sandra Romero-Molina et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

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

Deep Learning in Chemistry

(2019) Adam C. Mater et al.   Journal of Chemical Information and Modeling

MLatom : A program package for quantum chemical research assisted by machine learning

(2019) Pavlo O. Dral   JOURNAL OF COMPUTATIONAL CHEMISTRY

SchNet – A deep learning architecture for molecules and materials

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

Representing molecular and materials data for unsupervised machine learning

(2018) E. Swann et al.   MOLECULAR SIMULATION

MoleculeNet: a benchmark for molecular machine learning

(2018) Zhenqin Wu et al.   Chemical Science

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Planning chemical syntheses with deep neural networks and symbolic AI

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

Molecular Dynamics Simulation for All

(2018) Scott A. Hollingsworth et al.   NEURON

Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks

(2018) Jack Hanson et al.   BIOINFORMATICS

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

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

Deep learning for computational chemistry

(2017) Garrett B. Goh et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Protein secondary structure prediction: A survey of the state of the art

(2017) Qian Jiang et al.   JOURNAL OF MOLECULAR GRAPHICS & MODELLING

Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network

(2017) Kun Yao et al.   Journal of Physical Chemistry Letters

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

Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

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

ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules

(2017) Justin S. Smith et al.   Scientific Data

An Empirical Polarizable Force Field Based on the Classical Drude Oscillator Model: Development History and Recent Applications

(2016) Justin A. Lemkul et al.   CHEMICAL REVIEWS

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

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

Machine-learning-assisted materials discovery using failed experiments

(2016) Paul Raccuglia et al.   NATURE

A general-purpose machine learning framework for predicting properties of inorganic materials

(2016) Logan Ward et al.   npj Computational Materials

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

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

(2015) Yann LeCun et al.   NATURE

Molecular Mechanics

(2014) Kenno Vanommeslaeghe et al.   CURRENT PHARMACEUTICAL DESIGN

Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network

(2014) James Lyons et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

On representing chemical environments

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

Optimization of the Additive CHARMM All-Atom Protein Force Field Targeting Improved Sampling of the Backbone ϕ, ψ and Side-Chain χ1 and χ2 Dihedral Angles

(2012) Robert B. Best et al.   Journal of Chemical Theory and Computation

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Systematic optimization of long-range corrected hybrid density functionals

(2008) Jeng-Da Chai et al.   JOURNAL OF CHEMICAL PHYSICS