# The Artificial Intelligence-Driven Intelligent Laboratory for Organic Chemistry Synthesis
## 3 AI-Driven Intelligent Chemical Experiments
### Artificial Intelligence-Driven Organic Chemistry Research
#### 3.2.1 Product Prediction  
In the fields of drug discovery and advanced materials engineering, multidimensional prediction of compound properties has become a critical technology. The multimodal modeling framework that integrates quantum chemical calculations with high-throughput experimental data has reduced prediction errors in molecular polarizability. Researchers have deployed a separation parameter prediction system based on deep tensor networks on the TLC/HPLC automated platform. This intelligent closed loop of computation and experimentation is reconstructing the research and development paradigm of molecular engineering. [PERSON] et al. (2018) proposed a general and transferable multi-level graph convolutional neural network (MGCN) for molecular property prediction. This method directly integrates the quantum interactions of molecules, representing each molecule as a graph to preserve its internal structure, and uses a carefully designed hierarchical graph neural network to extract features from conformation and spatial information, thereby achieving effective prediction of molecular properties. Experimental results show that MGCN performs well on both equilibrium and non-equilibrium molecular datasets, which proves the validity, versatility, and transferability of the model. Uncertainty Quantification (UQ) [49] plays a crucial role in predicting molecular properties, especially in the field of drug discovery, where unexpected inaccuracies in model predictions during the experimental design process can waste valuable time and resources. Although neural network models are becoming increasingly common in quantitative structure-activity relationship (QSAR) modeling, their poor interpretability makes it particularly difficult to assess their robustness, out-of-domain applicability, and potential failure modes. This paper systematically evaluates several UQ methods for regression tasks and tests them on five datasets using various complementary performance metrics. The study shows that no single method significantly outperforms others across all datasets, nor does any method produce particularly reliable error rankings. While existing tools may be effective for certain tasks, researchers still encounter tasks where all existing methods perform poorly. Therefore, the further development of reliable UQ methods for regression tasks is necessary. Additionally, the paper provides a detailed description of several UQ strategies, including ensemble methods, distance methods, mean-variance estimation, and joint methods, and compares these methods using evaluation metrics such as Spearman rank correlation coefficient, miscalibration area, and negative log-likelihood. Finally, based on results from four benchmark datasets and one synthetic dataset, practical recommendations are provided for which techniques are relatively superior in different scenarios. [PERSON] et al. developed a Kriging learning framework that can accurately interpret the response of atomic electron density to changes in the positions of nearby atoms. By modifying their geometric structures through local minimum position vibrations on the Ramachandran plot, the generalization ability of this Kriging model was verified in aromatic amino acid (histidine, phenylalanine, etc.) test systems, where its predicted electrostatic potential energy showed high consistency with actual measurements in novel molecular configurations [50]. The ability to accurately and effectively predict compound molecular performance is crucial to the design of organic substances. [PERSON]'s team developed a series of effective methods to estimate molecular energy, successfully achieving a theoretical leap from classical empirical methods to a machine learning-driven paradigm in the molecular energy calculation system by constructing a technological evolution path from basic atomic contribution models to multi-body quantum synergy analysis frameworks [51]. This approach helps predict electronic properties such as polarizability and molecular frontier orbital energy.