Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search

Bayesian reaction optimization as a tool for chemical synthesis

(2021) Benjamin J. Shields et al.   NATURE

Deep Learning Model for Identifying Critical Structural Motifs in Potential Endocrine Disruptors

(2021) Arpan Mukherjee et al.   Journal of Chemical Information and Modeling

Siamese Recurrent Neural Network with a Self-Attention Mechanism for Bioactivity Prediction

(2021) Daniel Fernández-Llaneza et al.   ACS Omega

Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge

(2021) Florian Häse et al.   Applied Physics Reviews

Evaluating Scalable Uncertainty Estimation Methods for Deep Learning-Based Molecular Property Prediction

(2020) Gabriele Scalia et al.   Journal of Chemical Information and Modeling

Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals

(2020) Juhwan Noh et al.   Journal of Chemical Information and Modeling

Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost

(2020) Peter C. St. John et al.   Nature Communications

Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization

(2020) Jon Paul Janet et al.   ACS Central Science

Array programming with NumPy

(2020) Charles R. Harris et al.   NATURE

Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels

(2020) Marcus M. Noack et al.   Scientific Reports

Applied machine learning for predicting the lanthanide-ligand binding affinities

(2020) Suryanaman Chaube et al.   Scientific Reports

Investigating Active Learning and Meta-Learning for Iterative Peptide Design

(2020) Rainier Barrett et al.   Journal of Chemical Information and Modeling

GFN2-xTB—An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions

(2019) Christoph Bannwarth et al.   Journal of Chemical Theory and Computation

ReSimNet: Drug Response Similarity Prediction using Siamese Neural Networks

(2019) Minji Jeon et al.   BIOINFORMATICS

Machine Learning-Directed Navigation of Synthetic Design Space: A Statistical Learning Approach to Controlling the Synthesis of Perovskite Halide Nanoplatelets in the Quantum-Confined Regime

(2019) Erick J. Braham et al.   CHEMISTRY OF MATERIALS

Machine learning guided design of functional materials with targeted properties

(2019) Prasanna V. Balachandran   COMPUTATIONAL MATERIALS SCIENCE

The machine learning revolution in materials?

(2019) Kristofer G. Reyes et al.   MRS BULLETIN

Reaction-Based Enumeration, Active Learning, and Free Energy Calculations To Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin-Dependent Kinase 2 Inhibitors

(2019) Kyle D. Konze et al.   Journal of Chemical Information and Modeling

Deep Metric Learning: A Survey

(2019) Kaya et al.   Symmetry-Basel

Machine learning and the physical sciences

(2019) Giuseppe Carleo et al.   REVIEWS OF MODERN PHYSICS

Query-by-committee improvement with diversity and density in batch active learning

(2018) Seho Kee et al.   INFORMATION SCIENCES

Less is more: Sampling chemical space with active learning

(2018) Justin S. Smith et al.   JOURNAL OF CHEMICAL PHYSICS

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

(2018) Aditya Nandy et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Error-Controlled Exploration of Chemical Reaction Networks with Gaussian Processes

(2018) Gregor N. Simm et al.   Journal of Chemical Theory and Computation

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Phoenics: A Bayesian Optimizer for Chemistry

(2018) Florian Häse et al.   ACS Central Science

Machine learning in materials design and discovery: Examples from the present and suggestions for the future

(2018) J. E. Gubernatis et al.   PHYSICAL REVIEW MATERIALS

Matched Molecular Pair Analysis in Short: Algorithms, Applications and Limitations

(2017) Christian Tyrchan et al.   Computational and Structural Biotechnology Journal

Use of machine learning approaches for novel drug discovery

(2016) Angélica Nakagawa Lima et al.   Expert Opinion on Drug Discovery

molSimplify: A toolkit for automating discovery in inorganic chemistry

(2016) Efthymios I. Ioannidis et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Pseudo expected improvement criterion for parallel EGO algorithm

(2016) Dawei Zhan et al.   JOURNAL OF GLOBAL OPTIMIZATION

Multi-objective active machine learning rapidly improves structure–activity models and reveals new protein–protein interaction inhibitors

(2016) D. Reker et al.   Chemical Science

Active-learning strategies in computer-assisted drug discovery

(2015) Daniel Reker et al.   DRUG DISCOVERY TODAY

Matched Molecular Pair Analysis: Significance and the Impact of Experimental Uncertainty

(2014) Christian Kramer et al.   JOURNAL OF MEDICINAL CHEMISTRY

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

Matched Molecular Pair Analysis in drug discovery

(2013) Alexander G. Dossetter et al.   DRUG DISCOVERY TODAY

Consistent Estimation of Gibbs Energy Using Component Contributions

(2013) Elad Noor et al.   PLoS Computational Biology

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

The removal of CO2 and N2 from natural gas: A review of conventional and emerging process technologies

(2012) T.E. Rufford et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Theoretical Examination of the Thermodynamic Factors in the Selective Extraction of Am3+ from Eu3+ by Dithiophosphinic Acids

(2011) Jason M. Keith et al.   INORGANIC CHEMISTRY

Extended-Connectivity Fingerprints

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