The use of computational toxicology is increasingly embedded across regulatory submissions, product development processes, and hazard classification frameworks. The following practical use cases illustrate how in silico methods are applied across various sectors and toxicological endpoints.

A computational toxicology assessment uses a 2-D representation of a chemical structure, typically encoded as a SMILES (Simplified Molecular Input Line Entry System) string or drawn within a structural file (e.g., SDF (Structure Data File) or MOL (part of the MDL-file format family)). From this input, a variety of descriptors (quantitative representations of molecular properties) are calculated. These include both physicochemical descriptors, such as molecular weight, logP (octanol-water partition coefficient), and polar surface area, as well as structural descriptors that capture the presence or absence of specific substructures (Myatt et al., 2018). Chemical fingerprints are binary or hashed representations of molecular features and are commonly used to encode the presence or absence of specific substructures or patterns (Yang et al., 2022). Fingerprints (e.g., MACCS (Molecular ACCess System) keys, ECFP (Extended-Connectivity Fingerprint), or proprietary fingerprint sets transform chemical input data into standardized formats and enable similarity-based methods and machine learning models (Myatt et al., 2018). In addition, more complex features (topological, electronic, quantum mechanical) may be generated to encode structural information for use in machine learning or statistical models. These descriptors form the foundation for predictive modeling approaches such as (Q)SARs, which link specific combinations of molecular features to biological or toxicological outcomes.

Expert review is an important aspect of in silico toxicology, particularly in regulatory contexts. Computational models provide predictions based on chemical structure and training/reference data; however, an evaluation of model predictions is prudent to ensure and communicate prediction reliability (Myatt et al., 2017).

For a comprehensive discussion on assessing and improving prediction reliability through expert review, the reader is referred to Myatt et al. (2018). For brevity, the following are items considered as part of an expert review.

Evaluating model outputs, including confidence scores, consensus between different methodologies, and resolving any potential conflicting predictions

An expert review considers the limitations of each methodology, including the reliability of training data, out-of-domain predictions, and metabolic activation/deactivation. While the expert review is inherently subjective, it is recognized as an important step for deriving reliable and scientifically defensible predictions. The expert review is especially important in regulatory submissions, where any uncertainty must be transparently documented and communicated (Myatt et al., 2018; Amberg et al., 2019; Barber et al., 2015).

Developing competency in computational toxicology requires interdisciplinary training. It is important for users of computational tools to have a comprehensive understanding of modeling techniques and toxicological principles. This involves a combination of skills which includes the ability to critically evaluate the underlying toxicity data and understand how they influence the model output. Knowledge of the (regulatory) context of application is also critical, as it defines the optimal way to integrate in silico predictions into chemical safety assessments. Building this skill set is essential for evaluating the reliability of a prediction. Figure 1 presents a conceptual framework that outlines the progression from foundational understanding to practical application in computational toxicology education. It highlights the interdisciplinary skills that are required and also illustrates how these competencies are operationalized through education. A comprehensive computational toxicology education facilitates interpretation and domain fluency, which consequently enables good application of the theory.

Each in silico methodology has strengths and limitations and these are profiled in Table 1. Rather than focusing solely on how individual tools function, educational programs should also emphasize the methodological approaches, including statistical-based methods, expert rule-based systems, and read-across (Table 1). A good understanding of the strengths and weaknesses of in silico methodologies is important as it enables users to select appropriate methods, evaluate model outputs, and understand when a prediction is of sufficient reliability. In addition to enabling the interpretation of model results, this knowledge also facilitates efficient communication with interdisciplinary teams that may include toxicologists, chemists, modelers, and regulatory scientists.

The demand for non-animal toxicity assessments continues to grow (National Institutes of Health, 2025). Therefore, there is a need to cultivate a workforce which is fluent in the principles and practices of NAMs, including, computational toxicology. This includes an understanding of machine learning algorithms, chemical descriptor generation and interpretation, structural alert interpretation, and the integration of in silico results into weight-of-evidence assessments. Additionally, as regulatory agencies continue to evaluate and adopt the use of computational methods in safety assessment frameworks, it is important that scientists are prepared to meet these evolving standards through both theoretical understanding and application.

Computational toxicology requires knowledge of toxicological principles and also the ability to interpret computational results, particularly when integrating diverse data types (Myatt et al., 2018). An understanding of key toxicological endpoints; for example, mutagenicity, carcinogenicity, reproductive and developmental toxicity, acute toxicity, sensitization as well as ADME (absorption, distribution, metabolism, and excretion) enables professionals to interpret the biological relevance of computational outputs (Myatt et al., 2018). It is therefore beneficial to educate from both theoretical and practical points of view. As such, educators can embed case studies (Johnson et al., 2022), mechanistic frameworks such as adverse outcome pathways (AOPs (Leist et al., 2017)), and toxicological principles into coursework.

As data availability increases, data science capabilities are increasingly valuable. In addition to the interpretation of prediction results, being able to manage, analyze, and visualize complex datasets, and apply machine learning or artificial intelligence (AI) based methods to extract meaningful patterns and/or predictions is advantageous. Exercises that include the analysis of datasets can provide exposure to data harmonization procedures. Data harmonization processes are used to standardize data across various sources and study types, where applicable. Experience developing a well curated dataset could reinforce the practical importance of this process for extracting reliable results. Collaboration between departments, such as informatics and toxicology departments, can help bridge disciplinary gaps and provide students with technical experience.

As computational toxicology is often applied within regulatory contexts, an understanding of which computational approaches could be used to support chemical registration and safety decisions is beneficial. As demonstrated in Figure 1, knowledge of regulatory or context of use frameworks determines the selection of a model, documentation requirements, and expert interpretation. Simulating regulatory submissions for mock assessments and including a computational toxicology component into regulatory science modules can provide practical exposure. Training in science communication also forms an important component in explaining expert review and read-across outcomes. That is, the ability to clearly explain model predictions, and associated uncertainties and limitations, is an important skill. A solid foundation in chemistry, including familiarity with physicochemical and structural descriptors (as outlined in Section 3.1), supports meaningful interpretation of model outputs. There are several opportunities to build these competencies, including, internships, interdisciplinary team projects, presentations, and peer review exercises. As programs are more aligned with technical, regulatory, and collaborative requirements students can be expected to contribute meaningfully to the next-generation of predictive toxicology.

Market leaders in computational toxicology include private and public companies, public consortia, government and academic institutions. Some companies have supported academic training through initiatives such as academic licensing, providing training resources, and partnerships that support course development. Public initiatives and regulatory bodies have developed open-access tools and datasets that serve as valuable resources for educational needs. Platforms such as the National Toxicology Program’s Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov), and the OECD toolbox (https://www.oecd.org/en/data/tools/oecd-qsar-toolbox.html) offer access to data and various methodologies. These platforms provide students and professionals with practical exposure to computational tools. Cross-industry collaborations and professional societies can also create opportunities to gain practical experience with the computational methodologies. Consortia and collaborative initiatives, such as EU-ToxRisk (https://cordis.europa.eu/project/id/681002/reporting), ONTOX (https://ontox-project.eu/), and HESI (https://hesiglobal.org/about-hesi/) demonstrate how research initiatives support early-career scientists through fellowships, postdoctoral training, sharing technologies, and institutional expertise. Guest lectures by experienced professionals as well as professional workshops can also provide exposure to fundamental skills in regulatory or use case applications. In these contexts, Figure 1 provides a framework for designing training pipelines that connect foundational understanding with applied experience.

Notably, academic and research institutions contribute to the advancement of the field through the study of adverse outcome pathways (AOPs), development of new methodologies and approaches, and more recently, the use of AI into predictive frameworks (Hartung and Kleinstreuer, 2025; Hartung and Tsaioun, 2024; Scheufen et al., 2025; Watanabe-Sailor et al., 2019; Sinitsyn et al., 2022; Arnesdotter et al., 2021; Belfield et al., 2025; Roberts and Aptula, 2014).

A variety of collaborative strategies can help reinforce collaboration and ensure sustainable training pathways. These strategies include training pipelines that link academic learning with applied experience, as well as fellowship, mentorship and internship opportunities which connect to real-world experiences. Additionally cross-sector dialogue to keep educational content aligned with evolving regulatory and industry needs is valuable. Such opportunities provide students and young professionals with the experience needed to meet current and future needs in computational toxicology.

It is important for practitioners to understand how computational toxicology methods are adopted into regulations. Tools with regulatory acceptance must be built on curated, high-quality data, demonstrate reliability, and be linked to a clearly defined toxicological endpoint. In line with established best practices and validation principles, such as those outlined by the OECD in the (Q)SAR Assessment Framework (OECD, 2023) criteria such as the model’s domain of applicability, algorithm, and validation must be documented. It is also important to note that computational models are updated as new data or knowledge becomes available and, as such, models may be refined over time (Hasselgren et al., 2020). Knowledge of the full lifecycle of computational methods (including development to use case application, e.g., regulatory use) prepares individuals to be agile participants in an evolving regulatory and application landscape. As illustrated in Figure 1, use case knowledge is a critical component of computational toxicology education that can be emphasized curriculum design.

The future of computational toxicology is influenced by regulatory evolution, and technological advancement. Traditional AI and machine learning approaches have long supported predictive toxicology; however, the integration of more broadly defined AI models into predictive frameworks requires careful implementation (Hartung and Kleinstreuer, 2025). Regulatory preference is for models that support transparency (a clearly defined endpoint, interpretability, validation) and overall regulatory readiness (FDA. U.S. Department of Health and Human Services; EMA/CHMP/CVMP/83833/2023, 2024). In January 2025, the FDA issued draft guidance on the use of AI to support regulatory decision making. The guidance outlines a risk-based credibility framework for evaluating AI models that considers the context of use, an assessment of model risk, and documentation of metadata to establish the AI model credibility and adequacy within the use context (FDA. U.S. Department of Health and Human Services, 2025). There is growing interest and potential for the application of AI in drug discovery, development and toxicological research (Hartung and Kleinstreuer, 2025; National Center for Toxicological Research NCTR, 2024; Sinha et al., 2023) although careful integration with scientific and regulatory principles will be required to successfully integrate broadly defined AI approaches into regulatory decision-making frameworks (Hartung and Kleinstreuer, 2025). Nonetheless, the educational framework presented, which includes an analysis of application standards based on context of use (e.g., regulatory and discovery applications), theoretical understanding and practical experience is applicable towards educational objectives pertaining to the responsible use of AI. Given the increasing regulatory and industry focus on AI model transparency and credibility, integrating these principles into computational toxicology education is essential to prepare trainees to responsibly develop, evaluate, and apply predictive models in both research and regulatory contexts. For additional examples of the use of AI in toxicology and risk assessment see Haßmann et al. (2024), Bueso-Bordils et al. (2024) and Luechtefeld and Hartung (2025).