Beyond Predictions: Why Benchmarking Computational Tools for Toxicokinetic and Physicochemical Properties is Essential
- Elena Fioravanzo
- 7 days ago
- 2 min read
In the world of cheminformatics and toxicology, computational tools have become indispensable for predicting a vast array of chemical properties. From early drug discovery to environmental risk assessment, in silico methods offer speed and efficiency, helping to reduce the reliance on costly and time-consuming experimental approaches.
However, as the number and complexity of these tools grow, a critical question arises: How reliable are these predictions, and how do we choose the right tool for the job?
This is precisely the question addressed by a recent paper we found particularly interesting: "Comprehensive benchmarking of computational tools for predicting toxicokinetic and physicochemical properties of chemicals" published in the Journal of Cheminformatics.
Given the increasing importance of these predictions, the principles of ADME prediction will be soon a component of our "NAMs - Use and application of QSAR and read-across" course. This course provides practical guidance on leveraging New Approach Methodologies (NAMs) for regulatory purposes. Learn more and enroll here: https://courses.toxnavigation.com/course/nams.
You can access the full paper here:
While we are not authors of this paper, we believe it offers invaluable insights for anyone working with computational toxicology, drug discovery, or chemical safety. The authors undertook a rigorous benchmarking study, evaluating twelve different software tools and their Quantitative Structure-Activity Relationship (QSAR) models for predicting 17 key physicochemical (PC) and toxicokinetic (TK) properties. These properties are fundamental to understanding a chemical's Absorption, Distribution, Metabolism, and Excretion (ADME), which in turn impacts its toxicity and environmental fate.
Why is this benchmarking study so important for our readers?
Informed Tool Selection: The paper provides a clear overview of the performance of various widely used computational tools, guiding users towards those best suited for specific prediction tasks.
Understanding Strengths and Limitations: It highlights where different models excel (e.g., PC properties generally outperforming TK properties) and identifies recurring optimal choices, allowing for more confident application of in silico data.
Enhanced Reliability: By assessing models against 41 curated external validation datasets, the study emphasizes external predictivity and the importance of a tool's applicability domain – crucial for ensuring the reliability of predictions.
State-of-the-Art Overview: It serves as an excellent resource for understanding the current landscape of computational methods for ADMET prediction, a vital area for chemical safety and regulatory compliance.
In essence, this paper empowers users to make more informed decisions when selecting and applying computational tools, ultimately leading to more robust and trustworthy in silico predictions. For those committed to advancing chemical safety and reducing animal testing, understanding the capabilities and limitations of predictive software is paramount.
We encourage you to read this comprehensive benchmarking study to enhance your understanding and optimize your use of computational tools in your own work.
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