SAR AND QSAR MODELING OF ALGICIDAL COMPOUNDS BASED ON PHYSICOCHEMICAL DESCRIPTORS

Аннотация

This study presents a comprehensive structure–activity relationship (SAR) and quantitative structure–activity relationship (QSAR) analysis of organic compounds with algicidal properties. By employing a dataset of structurally diverse molecules, we evaluated the predictive power of key physicochemical descriptors, including boiling point, melting point, vapor pressure, logP, and molecular weight. Statistical models were constructed using multiple linear regression and validated through cross-validation techniques to assess their accuracy and robustness. The results indicate that certain descriptors, particularly logP and vapor pressure, show a strong correlation with algicidal efficacy. The proposed models provide a valuable framework for the rational design and pre-screening of environmentally safe algicides, reducing the need for labor- intensive bioassays. Keywords: algicidal compounds, physicochemical descriptors, SAR, QSAR, predictive modeling, organic pesticides, environmental toxicity. Introduction: Harmful algal blooms (HABs) have become an increasing concern in aquatic ecosystems, causing ecological imbalance, biodiversity loss, and serious risks to public health and water quality. As conventional chemical treatments often lead to environmental toxicity and non-selective bioaccumulation, the demand for safer, more targeted algicidal agents has grown significantly. Among the promising alternatives are organic compounds with tunable structures and physicochemical properties that can be optimized for algicidal selectivity and environmental compatibility. Recent advancements in cheminformatics have enabled the use of structure–activity relationship (SAR) and quantitative structure–activity relationship (QSAR) modeling to predict biological activity based on molecular descriptors. These models reduce the reliance on time- 445 NEW RENAISSANCE 2025 JUNE INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE VOLUME 2 | ISSUE 6 consuming bioassays by identifying key structural and physicochemical factors—such as boiling point, vapor pressure, lipophilicity (logP), molecular weight, and hydrogen bonding capacity— that influence algicidal potential. Despite progress in insecticide and herbicide QSAR modeling, limited research has addressed the modeling of algicidal compounds using physicochemical descriptors. This study aims to fill that gap by developing SAR and QSAR models for a diverse set of organic molecules with known algicidal activity. By analyzing the relationships between molecular features and bioefficacy, this work contributes to the rational design of novel, environmentally friendly algicides. Literature review: The increasing prevalence of harmful algal blooms (HABs) has prompted a growing body of research into effective and environmentally safe algicidal agents. Traditional algicides such as copper sulfate and synthetic herbicides have demonstrated strong algal control, but they often cause adverse environmental effects, including toxicity to non-target organisms and bioaccumulation in aquatic food webs (Anderson et al., 2012). In recent years, organic compounds with selective algicidal properties have received considerable attention due to their structural diversity and biodegradability. Several studies have explored the relationship between molecular properties and algicidal activity. For example, Zhao et al. (2017) demonstrated that hydrophobicity (logP) and vapor pressure significantly influence a compound’s ability to penetrate algal cell membranes. Similarly, Wang et al. (2020) showed that compounds with moderate volatility and lower water solubility exhibited higher selectivity against Microcystis aeruginosa. Structure–activity relationship (SAR) and quantitative structure–activity relationship (QSAR) modeling have emerged as powerful tools for predicting the bioactivity of chemical compounds based on molecular descriptors. These computational approaches have been successfully applied in pharmaceutical and pesticide research, enabling high-throughput screening and rational compound design (Tropsha, 2010; Cherkasov et al., 2014). However, their application in algicide modeling remains relatively limited, with only a few studies developing predictive QSAR models specific to algal toxicity (Yang et al., 2019). Key physicochemical descriptors used in previous QSAR studies include boiling point, melting point, molecular weight, topological polar surface area (TPSA), and hydrogen bond donors/acceptors. These parameters influence compound solubility, transport, and reactivity, which are critical to algicidal function (Karelson et al., 1996). Advanced software tools such as PaDEL-Descriptor and the OECD QSAR Toolbox have further streamlined the generation and analysis of such descriptors for model building. Despite the progress made, there is still a need for robust, validated models that can accurately predict algicidal potential from simple molecular input. This study addresses this gap by construct