arXiv:2410.05289v4 Announce Type: replace-cross Abstract: Background: Neurosymbolic (NeSy) artificial intelligence describes the combination of logic or rule-based techniques with neural networks. Compared to neural approaches, NeSy methods often possess enhanced interpretability, which is particularly promising for biomedical applications like drug discovery. However, no clear guidelines exist to assess the biological plausibility of model interpretations. Methods: To assess interpretability in the context of drug discovery, we devise a novel prediction task, called drug mechanism-of-action (
The increasing complexity of drug discovery and the demand for more transparent AI models are converging, making neurosymbolic AI a timely solution for enhanced interpretability in biomedical applications.
This research provides a concrete methodological advancement in making AI-driven drug discovery more explainable and trustworthy, which can accelerate the development of new therapeutics and improve regulatory acceptance.
The ability to assess the biological plausibility of AI model interpretations begins to bridge the gap between black-box AI predictions and medical understanding, fostering greater confidence in AI-generated drug candidates.
- · Pharmaceutical companies
- · Synthetic biology researchers
- · AI ethics and safety organizations
- · Patients with unmet medical needs
- · Traditional drug discovery methods
- · AI models lacking interpretability
- · Companies unable to integrate NeSy approaches
More efficient and targeted drug candidate identification through interpretable AI.
Reduced late-stage drug trial failures due to better understanding of mechanism-of-action from early discovery.
Accelerated approval of novel drugs and treatments, potentially leading to faster market entry for innovative therapies.
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