Hydration free energies from kernel-based machine learning: Compound-database bias

Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids

(2020) Christoph Scherer et al.   Journal of Chemical Theory and Computation

Machine learning coarse grained models for water

(2019) Henry Chan et al.   Nature Communications

Drug–Membrane Permeability across Chemical Space

(2019) Roberto Menichetti et al.   ACS Central Science

Transfer-Learning-Based Coarse-Graining Method for Simple Fluids: Toward Deep Inverse Liquid-State Theory

(2019) Alireza Moradzadeh et al.   Journal of Physical Chemistry Letters

Solvent-Specific Featurization for Predicting Free Energies of Solvation through Machine Learning

(2019) Samuel T. Hutchinson et al.   Journal of Chemical Information and Modeling

Recent advances in machine learning towards multiscale soft materials design

(2019) Nicholas E Jackson et al.   Current Opinion in Chemical Engineering

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

(2019) Jiang Wang et al.   ACS Central Science

Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network

(2019) Roman Zubatyuk et al.   Science Advances

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

(2019) K. T. Schütt et al.   Nature Communications

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

(2019) Marta Glavatskikh et al.   Journal of Cheminformatics

Quantum Machine Learning in Chemical Compound Space

(2018) O. Anatole von Lilienfeld   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Mapping of the Available Chemical Space versus the Chemical Universe of Lead-Like Compounds

(2018) Arkadii Lin et al.   ChemMedChem

Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks

(2018) Fahimeh Ghasemi et al.   DRUG DISCOVERY TODAY

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

(2018) Tristan Bereau et al.   JOURNAL OF CHEMICAL PHYSICS

Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

(2018) Christoph Wehmeyer et al.   JOURNAL OF CHEMICAL PHYSICS

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

(2018) Andrea Grisafi et al.   PHYSICAL REVIEW LETTERS

Automated design of collective variables using supervised machine learning

(2018) Mohammad M. Sultan et al.   JOURNAL OF CHEMICAL PHYSICS

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

(2018) Benjamin Sanchez-Lengeling et al.   SCIENCE

Transferable Machine-Learning Model of the Electron Density

(2018) Andrea Grisafi et al.   ACS Central Science

D3R Grand Challenge 2: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies

(2017) Zied Gaieb et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials

(2017) S. T. John et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Machine learning and data science in soft materials engineering

(2017) Andrew L Ferguson   JOURNAL OF PHYSICS-CONDENSED MATTER

Machine learning molecular dynamics for the simulation of infrared spectra

(2017) Michael Gastegger et al.   Chemical Science

Quantum-chemical insights from deep tensor neural networks

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

Low Data Drug Discovery with One-Shot Learning

(2017) Han Altae-Tran et al.   ACS Central Science

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Mapping membrane activity in undiscovered peptide sequence space using machine learning

(2016) Ernest Y. Lee et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

The Chemical Space Project

(2015) Jean-Louis Reymond   ACCOUNTS OF CHEMICAL RESEARCH

The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities

(2015) Samuel Genheden et al.   Expert Opinion on Drug Discovery

Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules

(2015) Tristan Bereau et al.   Journal of Chemical Theory and Computation

g_mmpbsa—A GROMACS Tool for High-Throughput MM-PBSA Calculations

(2014) Rashmi Kumari et al.   Journal of Chemical Information and Modeling

FreeSolv: a database of experimental and calculated hydration free energies, with input files

(2014) David L. Mobley et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

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

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

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

Prediction of Absolute Solvation Free Energies using Molecular Dynamics Free Energy Perturbation and the OPLS Force Field

(2010) Devleena Shivakumar et al.   Journal of Chemical Theory and Computation

Predicting Small-Molecule Solvation Free Energies: An Informal Blind Test for Computational Chemistry

(2008) Anthony Nicholls et al.   JOURNAL OF MEDICINAL CHEMISTRY