In modern drug discovery, the combination of chemoinformatics and quantitative structure–activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness vast potential machine learning (ML) techniques for predictive molecular design analysis. This review delves into fundamental aspects chemoinformatics, elucidating intricate nature chemical data cruci...

Drug resistance to existing antibiotics poses alarming threats to global public health, which inspires heightened interests in searching for new antibiotics, including antimicrobial peptides (AMPs). Accurate prediction of antibacterial activities of AMPs may expedite novel AMP design and reduce the costs and efforts involved in laboratory screening. In the present study, a novel quantitative pr...

Despite highly active antiretroviral therapy (HAART) implementation, there is a continuous need to search for new anti-HIV agents. HIV-1 integrase (HIV-1 IN) is a recently validated biological target for AIDS therapy. In this work, a four-dimensional quantitative structure-activity relationship (4D-QSAR) study using the new methodology named LQTA-QSAR approach with a training set of 85 HIV-1 IN...

Polo-like kinase 1, an important enzyme with diverse biological actions in cell mitosis, is a promising target for developing novel anticancer drugs. A combined molecular docking, structure-based pharmacophore modeling and three-dimensional quantitative structure-activity relationship (3D-QSAR) study was performed on a set of 4,5-dihydro-1H-pyrazolo[4,3-h]quinazoline derivatives as PLK1 inhibit...

The vastness of chemical space and the relatively small coverage by experimental data recording molecular properties require us to identify subspaces, or domains, for which we can confidently apply QSAR models. The prediction of QSAR models in these domains is reliable, and potential subsequent investigations of such compounds would find that the predictions closely match the experimental value...

A comprehensive review of quantitative structure-activity relationships (QSAR) allowing the prediction of the fate of organic compounds in the environment from their molecular properties was done. The considered processes were water dissolution, dissociation, volatilization, retention on soils and sediments (mainly adsorption and desorption), degradation (biotic and abiotic), and absorption by ...

The method for QSAR modelling of rat acute toxicity based on the combination of QNA (Quantitative Neighbourhoods of Atoms) descriptors, PASS (Prediction of Activity Spectra for Substances) predictions and self-consistent regression (SCR) is presented. PASS predicted biological activity profiles are used as independent input variables for QSAR modelling with SCR. QSAR models were developed using...

In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure-Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model. Verification of the relationships between descriptors and toxicity or other activity in the QSAR model has a vital role in understanding the mechanisms of action. The well-know...

A novel scheme for modeling 3D QSAR has been developed. A method involving multiple self-organizing neural network adjusted to be analyzed by the PLS (partial least squares) analysis was used to model 3D QSAR of the selected colchicinoids. The model obtained allows the identification of some structural determinants of the biological activity of compounds.

Three- and four-dimensional quantitative structure activity relationship (3D/4D-QSAR) pharmacophore models of competitive inhibitors of CYP2D6 were constructed using data from our laboratory or the literature. The 3D-QSAR pharmacophore models of the common structural features of CYP2D6 inhibitors were built using the program Catalyst (Molecular Simulations, San Diego, CA, USA). These 3D-QSAR mo...