We’re thrilled to present ComptoxAI—a new resource that’s meant to act as a ‘home base’ for advanced applications of artificial intelligence (AI) in the field of computational toxicology! A complete set of user guides and documentation pages are available on the main site, but we’re writing this post to give a brief introduction to ComptoxAI and illustrate why it is important to the translational research community.
What exactly is computational toxicology?¶
Computational toxicology (or ‘comptox’) is, broadly speaking, the application of computers and mathematics to describing and predicting the adverse effects that toxic chemicals have on the human body. The chemicals involved in comptox research include environmental pollutants, naturally occurring compounds, and intentionally engineered chemicals such as pharmaceutical drugs.
Toxicology is an old field (on the scale of biomedical research), but relatively little is known about how toxic exposures lead to adverse health effects. This is exacerbated by the sheer number of chemicals in existance that need to be studied for toxicological characteristics. The current standard for evaluating toxicological risk involves arduous experimental research and systematic reviews of prior research for individual chemicals. Not only is this an expensive process (both time- and money-wise), but it is also unsustainable for the hundreds of thousands—or even millions—of potentially toxic chemicals that are of interest to researchers. One of the main aims of comptox is to alleviate this issue by instead performing ‘virtual screening’ of compounds to estimate which chemicals are most likely to be toxic (which can then be followed up on by experimental toxicologists).
Why does AI matter for computational toxicology?¶
The current ‘state-of-the-art’ in comptox is very similar to what it was 50 years ago. One of the most popular methods is QSAR (Quantitative Structure-Activity Relationship) modeling, where researchers feed chemical structure data for compounds with known toxicologic activity into a machine learning model (usually something simple like logistic regression), and then use the model to generate activity predictions for structural data on chemicals with unknown activity. While this has been useful in some cases, modern toxicologists largely agree that QSAR performance is pretty lacking.
AI has the potential to change this, as it has for many other fields over the past decade or so. More complex AI and machine learning / deep learning models have the structural capacity to not only incorporate information about chemical properties and clinical outcomes, but also the myriad of translational processes that occur in between (such as genetic factors, enzymatic and network interactions, and tissue/organ functions). They also can incorporate other important sources of knowledge, such as clinical trial outcomes, data from similar chemicals, experimental assay results, and information extracted from published research articles. Together, these types of data and knowledge can paint a much more complete picture of important toxicological events than structural data can alone.
What kinds of things can ComptoxAI do?¶
ComptoxAI can be split up into 5 major components:
A large graph database for storing and retrieving data relevant to comptox
A collection of graph algorithms for analyzing the contents and overall structure of the database
An OWL ontology used to structure and validate the graph database
This website, intended to serve as both documentation for ComptoxAI as well as as informational platform to teach and disseminate comptox concepts and research
A gallery of machine learning models designed to be used on ComptoxAI’s database
At the time of writing this post, each of these have been developed to varying degrees of completeness. We strongly encourage interested users to get in touch with us to report bugs, request changes, or even just to ask us questions about ComptoxAI and the work we do.
What kinds of things will ComptoxAI do eventually?¶
We have a lot of features in the works for ComptoxAI. This is why we refer to it as a “toolchain” for comptox, rather than simply a database or a Python package. Some of these planned features include:
A public, hosted instance of the graph database, so users can interact with ComptoxAI without having to build the entire database on their local computer
Improved options for query submission and knowledge retrieval from the graph database that don’t require an intricate knowledge of web APIs or the Cypher query language
Detailed blog posts for each new major feature, explaining what they are and why they are useful in an informal way
Modules within the Python package dedicated to specific tasks in toxicology, such as automated prediction of Cramer classes, Thresholds of Toxicological Concern, and automated read-across for various applications
More complete documentation, especially for graph algorithms and machine learning models
Tutorials and other guides that are geared towards bench toxicologists and new informaticians, enabling them to run AI workflows on data relevant to specific questions that are important to their research