Skin sensitization is a critical endpoint in toxicology, especially in the context of drug discovery and development process for topical and transdermal treatments. Accurate evaluation of skin sensitization potential is essential to ensure the safety...
Machine learning is a vital tool in advancing drug development by accurately predicting the physical, chemical, and biological properties of various compounds. This study utilizes MATLAB program-based algorithms to calculate topological indices and m...
Ionic liquids (ILs) with tunable structures have emerged as promising next-generation biocides. In this study, we presented an ML-based q-RASTR framework, along with i-qRASTTR approach, to predict the toxicity of ILs against different bacteria. Vario...
This study explores how topological indices (TIs), which are mathematical descriptors of a drug's molecular structure, can support to predict vital properties and biological activities. This understanding is a key for more effective drug design. We f...
The global rise of antibiotic-resistant infections has been driven in part by the spread of bacteria producing metallo-β-lactamase (MBL), particularly New Delhi metallo-β-lactamase-1 (NDM-1). Currently, there are no clinically approved inhibitors tar...
Micro- and nanoplastics (MNPs) are emerging pollutants that accumulate in ecosystems, food chains, and the human body, raising concerns about human health risks. However, understanding their toxicity remains challenging due to limited experimental da...
Journal of chemical information and modeling
Jul 22, 2025
Fraction unbound in plasma () is a crucial parameter in physiologically based toxicokinetic (PBTK) models, representing the fraction of a chemical compound that is not sequestered by plasma proteins when present in the bloodstream. This is often used...
The accumulation of organic pollutants in the environment has significantly impacted the lives of flora and fauna, resulting in disruptions in the biological ecosystem. Carcinogenicity has been one of the most alarming adverse effects exhibited by th...
In this paper, we present a predictive model based on artificial neural network (ANN) to evaluate principal physicochemical properties of a set of anti-inflammatory drugs based on chosen topological indices. The molecular descriptors were calculated ...
The process of drug discovery is intricate, and encompasses a series of detailed phases of research, development, and testing, aimed at evaluating the safety and effectiveness of prospective therapeutic agents. Artificial Intelligence has emerged as ...
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