Machine Learning in Chemistry

Hugh Cartwright is a computational chemist, now retired. He spent almost three decades as a member of the Chemistry Faculty at Oxford University in the U.K., where his research focussed on the application of Artificial Intelligence related methods to problems in science, using Artificial Neural Networks, Genetic Algorithms, Self-Organising Maps and Support Vector Machines. He has written or edited several texts on the use of Artificial Intelligence in science, including Applications of Artificial Intelligence in Chemistry, and Using Artificial Intelligence in Chemistry and Biology: a Practical Guide.

Computers as Scientists;

How Do Machines Learn?;

MedChemInformatics: An Introduction to Machine Learning for Drug Discovery;

Machine Learning for Nonadiabatic Molecular Dynamics;

Machine Learning in Science – A Role for Mechanical Sympathy?;

A Prediction of Future States: AI-powered Chemical Innovation for Defense Applications;

Machine Learning for Chemical Synthesis;

Constraining Chemical Networks in Astrochemistry;

Machine Learning at the (Nano)materials-biology Interface;

Machine Learning Techniques Applied to a Complex Polymerization Process;

Machine Learning and Scoring Functions (SFs) for Molecular Drug Discovery: Prediction and Characterisation of Druggable Drugs and Targets;

Artificial Intelligence Applied to the Prediction of Organic Materials;

A New Era of Inorganic Materials Discovery Powered by Data Science;

Machine Learning Applications in Chemical Engineering;

Representation Learning in Chemistry;

Demystifying Artificial Neural Networks as Generators of New Chemical Knowledge: Antimalarial Drug Discovery as a Case Study;

Machine Learning for Core-loss Spectrum;

Autonomous Science: Big Data Tools for Small Data Problems in Chemistry;

Machine Learning for Heterogeneous Catalysis: Global Neural Network Potential from Construction to Applications;

A Few Guiding Principles for Practical Applications of Machine Learning to Chemistry and Materials