Artificial intelligence and specifically machine learning applications are nowadays used in a
variety of scientific applications and cutting-edge technologies, where they have a …

Chemical compound space (CCS), the set of all theoretically conceivable combinations of
chemical elements and (meta-) stable geometries that make up matter, is colossal. The first …

The process of developing predictive models includes many stages. Most resources focus
on the modeling algorithms but neglect other critical aspects of the modeling process. This …

Background We address the problem of selecting and assessing classification and
regression models using cross-validation. Current state-of-the-art methods can yield models …

The medicinal chemistry community has become increasingly aware of the value of tracking
calculated physical properties such as molecular weight, topological polar surface area …

We show here that machine learning is a powerful new tool for predicting the elastic
response of zeolites. We built our machine learning approach relying on geometric features …

Here, the employment of multilayer perceptrons, a type of artificial neural network, is
proposed as part of a computational funneling procedure for high‐throughput organic …

The crucial step of building a high performance QSAR/QSPR model is the detection of
outliers in the model. Detecting outliers in a multivariate point cloud is not trivial, especially …

We have applied the k-nearest neighbor (k NN) modeling technique to the prediction of
melting points. A data set of 4119 diverse organic molecules (data set 1) and an additional …

This paper attempts to elucidate differences in QSPR models of aqueous solubility (Log S),
melting point (T m), and octanol− water partition coefficient (Log P), three properties of …