Researchers explored procedural pretraining for molecular property prediction, finding it can improve performance even after molecular pretraining. A three-stage training pipeline was used, consisting of procedural pretraining, molecular pretraining, and downstream fine-tuning. Results showed a 4.8% reduction in test error for Lipophilicity, and the benefit was strongest under data scarcity.