Recalcitrant nail plate infections can be life-long problems because localizing antifungal agents into infected tissues is problematic. In this systematic review, guided by the SPIDER method, we extracted chemical nail permeation data for 38 compounds from 16 articles, and analyzed the data using quantitative structure-property relationships (QSPRs). Our analysis demonstrated that low-molecular weight was essential for effective nail penetration, with <120 g/mol being preferred. Interestingly, chemical polarity had little effect on nail penetration; therefore, small polar molecules, which effectively penetrate the nail, but not the skin, should be set as the most desirable target chemical property in new post-screen onychomycosis candidate selections.

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 the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structure-activity relationships (SARs), are pivotal in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. Due to embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.

Dehalogenation is one of the highly important degradation reactions for halogenated organic compounds (HOCs) in the environment, which is also being developed as a potential type of the remediation technologies. In combination with the experimental results, intensive efforts have recently been devoted to the development of efficient theoretical methodologies (e.g. multi-scale simulation) to investigate the mechanisms for dehalogenation of HOCs. This review summarizes the structural characteristics of neutral molecules, anionic species and excited states of HOCs as well as their adsorption behavior on the surface of graphene and the Fe cluster. It discusses the key physiochemical properties (e.g. frontier orbital energies and thermodynamic properties) calculated at various levels of theory (e.g. semiempirical, ab initio, density functional theory (DFT) and the periodic DFT) as well as their connections to the reactivity and reaction pathway for the dehalogenation. This paper also reviews the advances in the linear and nonlinear quantitative structure-property relationship models for the dehalogenation kinetics of HOCs and in the mathematical modeling of the dehalogenation processes. Furthermore, prospects of further expansion and exploration of the current research fields are described in this article.

This paper presents a review on recent progress in quantitative structure-property relationship (QSPR) studies of surfactants and applications of various molecular descriptors. QSPR studies on critical micelle concentration (cmc) and surface tension (gamma) of surfactants are introduced. Studies on charge distribution in ionic surfactants by quantum chemical calculations and its effects on the structures and properties of the colloids of surfactants are also reviewed. The trends of QSPR studies on cloud point (for nonionic surfactants), biodegradation potential and some other properties of surfactants are evaluated.