In Silico Approaches to Unravelling Toxicity Mechanisms in Molecular Drugs
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Pharmacology".
Deadline for manuscript submissions: 30 November 2025 | Viewed by 30
Special Issue Editor
Interests: computational toxicology; pharmacokinetics; pharmacovigilance
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, the integration of cheminformatics into in silico methodologies has attracted considerable attention in the context of drug safety evaluation. These approaches facilitate the consolidation of diverse data types to enhance the accuracy and mechanistic interpretation of toxicity predictions. These data types can include chemical structure descriptors, especially three-dimensional molecular descriptors and detailed electronic and topological features that capture atomistic conformations and charge distributions, pharmacokinetic/pharmacodynamic (PK/PD) parameters, omics datasets, and known toxicity pathways. Moreover, molecular docking and molecular dynamics simulations provide atomic-resolution insights into drug–target and off-target interactions, elucidating the structural basis of adverse effects.
This Special Issue will showcase state-of-the-art research focused on data-driven strategies for elucidating drug-induced toxicities and developing predictive models. We welcome submissions addressing various facets of computational toxicology, including the design and application of quantitative structure–activity relationship (QSAR) models, machine learning and artificial intelligence–based algorithms for toxicity prediction, the development and integration of toxicity databases, and in silico analyses aimed at garnering mechanistic insights into toxicity pathways. Studies combining omics data with cheminformatics for holistic toxicity assessments, as well as those identifying novel toxicity biomarkers and incorporating them into predictive frameworks, are particularly encouraged.
By bringing together these contributions, this Special Issue will highlight cutting-edge advances in computational toxicology, foster interdisciplinary collaboration, and inform future directions in the predictive assessment of drug safety.
Prof. Dr. Yoshihiro Uesawa
Guest Editor
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Keywords
- cheminformatics
- in silico analysis
- QSAR
- toxicity prediction
- machine learning
- toxicity mechanisms
- omics data integration
- toxicity biomarkers
- artificial intelligence
- adverse event database
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