DNA reactions play a pivotal role in numerous biological processes, synthetic biology applications, and emerging fields like DNA computing. Accurate prediction of the thermodynamic and kinetic parameters of these reactions is crucial for understanding molecular interactions, designing functional DNA-based systems, and advancing various technological applications. Existing software for predicting thermodynamics of DNA reactions such as Mfold and Nupack rely on the nearest neighbor model. The model calculates the free energy of DNA molecule formation as the sum of non-overlapping independent motifs, including stacks, dangles and loops. The predictions exhibit deviations from experimental measurements, with errors in the predicted melt temperature reaching 1.6°C. . This discrepancy stems from several factors, including inaccuracies in experimental thermodynamic measurements, limitations in the linear summation approach of all motifs within the nearest neighbor model, and the limited accuracy of deriving stack free energy due to mathematical approximations. Recent advancements by Wang and Bae et al. have improved the accuracy of experimental thermodynamic measurements for dangles and loops using noncovalent catalysis and toehold exchange energy measurement, but the limitations of the nearest neighbor model and the estimation of stack free energy still persist. Additionally, the next-nearest neighbor model proposed by Owczarzy et al. did not significantly enhance prediction accuracy. Machine learning has demonstrated great potential in discovering patterns and rules within large data, making it a valuable tool for enhancing scientific models to address intricate problems involving nonlinear processes or broad chemical spaces. For instance, Zhang et al. proposed two machine learning models, the weighted neighbor voting model and the deep learning model. These models were specifically designed to predict DNA hybridization and strand displacement rate constants based on sequences and experimental conditions. Impressively, they achieved a significant 50% reduction in root mean square error compared to traditional approaches such as the three-step model and worm-like chain model. Despite these advancements, it is important to note the limitations that arise in this process, including challenges related to randomly selecting training data, reconstructing data, and handling imbalanced data. To overcome these limitations and enhance accuracy, the integration of quantum chemistry with machine learning offers a powerful solution for accurately predicting macroscopic physical quantities by capturing the complex nonlinear relationships between microscopic features. This integrated approach has shown notable success in predicting chemical properties and reaction pathways. Quantum chemistry calculations provide precise descriptions of electronic structure and atom behavior, granting researchers insights that are often challenging to measure or reproduce. For example, Hopfinger et al. demonstrated significant agreement between energy calculations obtained from quantum chemistry simulations and parameters derived from a series of optical melting experiments. Their approach successfully predicts the thermodynamic parameters of modified nucleotides. In this study, we propose a quantum chemistry-based deep learning model to predict the thermodynamic and kinetic parameters of DNA reactions with enhanced accuracy and efficiency (Figure ). This model integrates quantum chemistry calculations, new designed descriptor matrices, and deep learning algorithms. It comprehensively describes the energy variations associated with DNA reactions by expanding stacks and incorporating other relevant factors. Quantum chemistry calculations are used to obtain energy values, while a physical motivated statistical model estimates entropy values. Furthermore, to overcome the challenge of limited labelled training data, we employ an active learning method, that iteratively selects informative samples of DNA reactions. This approach not only enhances the model's performance but also optimizes the utilization of available data resources of DNA reactions. Through extensive evaluations and comparisons with existing models, we demonstrate the superior predictive capabilities of our model in accurately predicting DNA hybridization free energies and strand displacement rate constants. These results highlight the potential of integrating quantum chemistry with deep learning for advancing our understanding of DNA reactions and enabling more precise design and optimization of DNA-based systems.