By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …

Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …

Hands-on Machine Learning with R provides a practical and applied approach to learning
and developing intuition into today's most popular machine learning methods. This book …

The correct use of model evaluation, model selection, and algorithm selection techniques is
vital in academic machine learning research as well as in many industrial settings. This …

The task of learning an expressive molecular representation is central to developing
quantitative structure–activity and property relationships. Traditional approaches rely on …

Quantitative structure-activity relationship (QSAR) methods are important for prediction of
biological effect of chemical compounds based on mathematical and statistical relations …

This is a book on data analysis with a specific focus on the practice of predictive modeling.
The term predictive modeling may stir associations such as machine learning, pattern …

Synthesis planning is the process of recursively decomposing target molecules into
available precursors. Computer-aided retrosynthesis can potentially assist chemists in …

Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk
Assessment describes the historical evolution of quantitative structure-activity relationship …

We present a stochastic integrated machine learning based multiscale approach for the
prediction of the macroscopic thermal conductivity in carbon nanotube reinforced polymeric …