The comprehension of the chemical properties of biological molecules is foundational for understanding their impact on living organisms and the environment. These properties encompass physicochemical properties, such as lipophilicity and aqueous solubility, as well as pharmacokinetics properties, such as absorption, distribution, metabolism, excretion, and toxicity properties, playing a crucial role in toxicological assessments. In this context, quantitative structure-activity relationships (QSARs) emerge as a substantive facet in property prediction of chemicals and pharmaceuticals. Predictive QSAR models using computational techniques and artificial intelligence (AI) find application within various regulatory agencies as an alternative method to the use of animals. This chapter delves into five key themes related to QSAR application for predicting chemical properties, specifically focusing on the development of QSAR models for cardiotoxicity, endocrine disruption, skin sensitization, cytotoxicity, and environmental toxicology endpoints. These models significantly contribute to our understanding of toxicological data, shedding light on underlying mechanisms of action. Investigating intricate interactions between chemicals and biological systems through QSAR is imperative for advancing our comprehension and promoting effective approaches in assessing biological properties that impact human health and the environment. The discussion encompasses the broader implications of QSAR in pharmacology, toxicology, and environmental science, emphasizing its role as a valuable tool in property prediction for biological molecules.