Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

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.

The growing concentrations of micropollutants in aquatic ecosystems are a global water quality issue. Understanding micropollutants varied chemical composition and potency is essential to solving this complex issue. Micropollutants management requires identifying contaminants to reduce, optimal reduction targets, and the best wastewater recycling locations. Management requires appropriate technological measures. Pharmaceuticals, antibiotics, hormones, and other micropollutants can enter the aquatic environment from point and diffuse sources, with wastewater treatment plants (WWTPs) distributing them in urban areas. Micropollutants like pharmaceuticals and hormones may not be removed by conventional WWTPs. Micropollutants affect the EU, especially in densely populated areas where surface water is consumed. This review examines several technological options that can be integrated into existing treatment methods to address this issue. In this work, oxidation, activated carbon, and their combinations as potential solutions, considering their efficacy and cost were evaluated. This study illuminates micropollutants origin and physico-chemical properties, which affect distribution, persistence, and environmental impacts. Understanding these factors helps us develop targeted micropollutant mitigation strategies to protect water quality. This review can inform policy and decision-making to reduce micropollutant impacts on aquatic ecosystems and human health.

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.

Given the rapid development of nanotechnology, it is crucial to understand the effects of nanoparticles on living organisms. However, it is laborious to perform toxicological tests on a case-by-case basis. Quantitative structure-activity relationship (QSAR) is an effective computational technique because it saves time, costs, and animal sacrifice. Therefore, this review presents general procedures for the construction and application of nano-QSAR models of metal-based and metal-oxide nanoparticles (MBNPs and MONPs). We also provide an overview of available databases and common algorithms. The molecular descriptors and their roles in the toxicological interpretation of MBNPs and MONPs are systematically reviewed and the future of nano-QSAR is discussed. Finally, we address the growing demand for novel nano-specific descriptors, new computational strategies to address the data shortage, in situ data for regulatory concerns, a better understanding of the physicochemical properties of NPs with bioactivity, and, most importantly, the design of nano-QSAR for real-life environmental predictions rather than laboratory simulations.

BACKGROUND: In silico methods for toxicity prediction have increased significantly in recent years due to the 3Rs principle. This also applies to predicting reproductive toxicology, which is one of the most critical factors in pesticide approval. The widely used quantitative structure-activity relationship (QSAR) models use experimental toxicity data to create a model that relates experimentally observed toxicity to molecular structures to predict toxicity. Aim of the study was to evaluate the available prediction models for developmental and reproductive toxicity regarding their strengths and weaknesses in a pesticide database.
METHODS: The reproductive toxicity of 315 pesticides, which have a GHS classification by ECHA, was compared with the prediction of different in silico models: VEGA, OECD (Q)SAR Toolbox, Leadscope Model Applier, and CASE Ultra by MultiCASE.
RESULTS: In all models, a large proportion (up to 77%) of all pesticides were outside the chemical space of the model. Analysis of the prediction of remaining pesticides revealed a balanced accuracy of the models between 0.48 and 0.66.
CONCLUSIONS: Overall, predictions were only meaningful in rare cases and therefore always require evaluation by an expert. The critical factors were the underlying data and determination of molecular similarity, which offer great potential for improvement.

Advanced oxidation processes (AOPs) are widely used as efficient technologies to treat highly toxic and harmful substances in wastewater. Taking the most representative aromatic compounds (monosubstituted benzenes, substituted phenols and heterocyclic compounds) as examples, this paper firstly introduces their structures and the structural descriptors studied in AOPs before, and the influence of structural differences in AOPs with different reactive oxygen species (ROS) on the degradation rate was discussed in detail. The structure-activity relationship of pollutants has been previously analyzed through quantitative structure-activity relationship (QSAR) model, in which ROS is a very important influencing factor. When electrophilic oxidative species attacks pollutants, aromatic compounds with electron donating groups are more favorable for degradation than aromatic compounds with electron donating groups. While nucleophilic oxidative species comes to the opposite conclusion. The choice of advanced oxidation processes, the synergistic effect of various active oxygen species and the used catalysts will also change the degradation mechanism. This makes the structure-dependent activity relationship uncertain, and different conclusions are obtained under the influence of various experimental factors.

An important focus for innovation in photodynamic therapy (PDT) is theoretical investigations. They employ mostly methods based on Time-Dependent Density Functional Theory (TD-DFT) to study the photochemical properties of photosensitizers. In the current article we review the existing state-of-the-art TD-DFT methods (and beyond) which are employed to study the properties of porphyrinoid-based systems. The review is organized in such a way that each paragraph is devoted to a separate aspect of the PDT mechanism, e.g., correct prediction of the absorption spectra, determination of the singlet-triplet intersystem crossing, and interaction with molecular oxygen. Aspects of the calculation schemes are discussed, such as the choice of the most suitable functional and inclusion of a solvent. Finally, quantitative structure-activity relationship (QSAR) methods used to explore the photochemistry of porphyrinoid-based systems are discussed.

Nitroaromatic compounds (NACs) are ubiquitous in the environment due to their extensive industrial applications. The recalcitrance of NACs causes their arduous degradation, subsequently bringing about potential threats to human health and environmental safety. The problem of how to effectively predict the toxicity of NACs has drawn public concern over time. Quantitative structure-activity relationship (QSAR) is introduced as a cost-effective tool to quantitatively predict the toxicity of toxicants. Both OECD (Organization for Economic Co-operation and Development) and REACH (Registration, Evaluation and Authorization of Chemicals) legislation have promoted the use of QSAR as it can significantly reduce living animal testing. Although numerous QSAR studies have been conducted to evaluate the toxicity of NACs, systematic reviews related to the QSAR modeling of NACs toxicity are less reported. The purpose of this review is to provide a thorough summary of recent QSAR studies on the toxic effects of NACs according to the corresponding classes of toxic response endpoints.

Malaria still persists as one of the deadliest infectious disease having a huge morbidity and mortality affecting the higher population of the world. Structure and ligand-based drug design methods like molecular docking and MD simulations, pharmacophore modeling, QSAR and virtual screening are widely used to perceive the accordant correlation between the antimalarial activity and property of the compounds to design novel dominant and discriminant molecules. These modeling methods will speed-up antimalarial drug discovery, selection of better drug candidates for synthesis and to achieve potent and safer drugs. In this work, we have extensively reviewed the literature pertaining to the use and applications of various ligand and structure-based computational methods for the design of antimalarial agents. Different classes of molecules are discussed along with their target interactions pattern, which is responsible for antimalarial activity. Communicated by Ramaswamy H. Sarma.