Early identification of chemical activity on MIE-relevant protein targets supports first-line toxicity assessment and helps researchers prioritize mechanisms for subsequent experimental investigation. Here we present an automated AI pipeline NeuroTox-KPGT that converts raw ChEMBL bioactivity data into optimized deep learning models for MIE prediction. The pipeline builds on the Knowledge-Guided Pre-training of Graph Transformer (KPGT) framework, which represents chemical structures as knowledge-enriched molecular graphs. It integrates data curation, molecular graph generation, and model training and tuning. This integration enables users to construct target-specific prediction models in a seamless and reproducible way, starting from initial data and ending with deployable AI. We demonstrate its use in a neural tube defect (NTD) case study, where fine-tuned KPGT models outperformed traditional Support Vector Machine models with a radial basis function kernel (SVM-RBF) when predicting MIEs linked to developmental toxicity. The results highlight the potential of AI-driven toxicity modeling to accelerate AOP development, improve endpoint prioritization, and prioritize chemicals for experimental follow-up. By providing an end-to-end, data-to-model workflow, the pipeline lowers the technical barrier to using modern graph-based neural architectures in toxicology. It offers a reproducible route to deployable MIE prediction models that support AOP development, compound prioritization, and early-stage chemical safety evaluation.

Code repository: https://github.com/MerelFlorian/NeuroTox-KPGT

Link to publication: A pipeline for developing AI-driven models to predict molecular initiating events: a case study on neural tube defects