Extensive research has focused on applying machine learning (ML) models to predict drug properties, sparked in 2012 by Merck's 'Merck Molecular Activity Challenge' competition. The competition's winner pioneered multitask deep neural networks, laying the foundation for in silico drug property prediction. The subsequent emergence of graph neural networks (GNNs) enabled the integration of atom-level and bond-level information, significantly enhancing prediction accuracy. More recently, advances in transformer models have demonstrated promising results in drug property prediction. Although ML models have achieved impressive performance with in silico drug properties predictions, their application requires a combination of domain knowledge of the search objective and expertise in model construction. Moreover, extensive experiments are compulsory for assessing numerous features, architectures, and model parameters. These models are inherently data-dependent and target-specific, thus limiting their transferability. Substantial effort is needed to adapt them to different molecules-or even the same molecules sourced from varying laboratory environments-due to unavoidable experimental variations. To address these challenges, we introduce the model training engine (MTE), an automated machine learning (AutoML) pipeline designed to eliminate the tedious tasks of ML. MTE empowers users to independently build customized drug prediction models at scale, tailored to their project's specific needs and discover hidden patterns in the chemical data. With the continual growth of available data, AI-aided approaches have become increasingly critical in drug discovery. The MTE removes the delay in the generation of high-quality targeted ML models by taking care of the full drug prediction model construction process, from data preprocessing, model training, fine-tuning to model selection. To accelerate the training process and reduce the cost, we introduce a parallel hyperparameter optimization framework coupled with a median pruner and an scalable cross-validation scheme. We validate the effectiveness of the MTE pipeline by predicting the absorption, distribution, metabolism, excretion, and toxicity (ADMET) endpoints on twenty-two benchmark datasets and comparing the performance with five state-of-the-art models for these datasets.