Since the pioneering effort of Hansch and Fujita, quantitative structure-activity relationships (QSAR) have proved valuable in optimizing lead structures. Enriching classical 3D-QSAR analysis, which exploits the three-dimensional structure of ligands, with structural information of the target has helped to improve the interpretability of the derived models and to increase their predictive power...

The basic concepts of the rough set theory are introduced and adequately illustrated. An example of the rough set theory application to the QSAR classification problem is presented. Numerous earlier applications of rough set theory to the various scientific domains suggest that it also can be a useful tool for the analysis of inexact, uncertain, or vague chemical data. q 1999 Elsevier Science B...

We discuss some features of the orthogonalization methods commonly applied to QSPR QSAR studies. We outline the well known multivariable linear regression analysis in vector form in order to compare mainly Randic and Gram-Schmidt orthogonalization procedures and also cast the basis for other approaches like Löwdin’s one. We expect that present review may become the starting point for future dev...

A diverse training set composed of 76 in-house synthesized and 61 collected from the literature was subjected to molecular field topology analysis. This resulted in a high-quality quantitative structure-activity relationships model (R² = 0.932, Q² = 0.809) which was used for the topological functional core identification and prediction of vasodilatory activity of 19 novel pyridinecarbonitriles,...

Ensemble methods have become popular for QSAR modeling, but most studies have assumed balanced data, consisting of approximately equal numbers of active and inactive compounds. Cheminformatics data are often far from being balanced. We extend the application of ensemble methods to include cases of imbalance of class membership and to more adequately assess model output. Based on the extension, ...

Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure-activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some...

We present a new machine learning approach for 3D-QSAR, the task of predicting binding affinities of molecules to target proteins based on 3D structure. Our approach predicts binding affinity by using regression on substructures discovered by relational learning. We make two contributions to the state-of-the-art. First, we use multiple-instance (MI) regression, which represents a molecule as a ...

Many methods for quantitative structure-activity relationships (QSARs) deliver point estimates only, without quantifying the uncertainty inherent in the prediction. One way to quantify the uncertainy of a QSAR prediction is to predict the conditional density of the activity given the structure instead of a point estimate. If a conditional density estimate is available, it is easy to derive pred...