# The Artificial Intelligence-Driven Intelligent Laboratory for Organic Chemistry Synthesis
## 3 AI-Driven Intelligent Chemical Experiments
### Artificial Intelligence-Driven Organic Chemistry Research
#### 3.2.3 Artificial Intelligence-Driven Reaction Prediction  
In the field of cheminformatics, particularly within quantitative structure-activity relationship (QSAR) studies, multiple linear regression (MLR) has evolved into a standard method for multidimensional feature modeling. Unlike univariate linear models, which capture isolated parameter effects, MLR considers multiple predictive variables simultaneously, providing a computational framework that combines interpretability with predictive accuracy for optimizing complex organic synthesis conditions. In molecular synthesis, although many reactions are theoretically feasible, achieving high reactivity and selectivity is rare. Typically, most reactions reach the desired efficiency and selectivity only through a clever combination of substrates, catalysts, and conditions. Therefore, accurately assessing the reactivity and selectivity of synthesis predictions is crucial to the successful design of molecular synthesis. However, due to the vast molecular structure space and the diversity of control factors, no simple formula currently exists to quantitatively describe the universal laws of molecular synthesis. Synthetic prediction remains one of the core challenges in AI-driven synthesis. Traditional research often relies on experience-driven strategies: by summarizing the available data, synthetic chemists can derive local structure-activity relationships for specific goals for rational design and improvement of synthetic transformations. However, this empirical approach lacks precision and predictive capability, potentially leading to contradictory rules, which result in inevitable random choices and trial-and-error testing during actual synthetic exploration. Recently, data-driven methods have brought new perspectives to solving synthesis prediction problems. With the aid of advanced AI algorithms and abundant chemical data, research has shown that machine learning models can accurately predict reaction yields and selectivity, even surpassing the judgments of experienced chemists in some cases. These models can assist chemists in efficiently screening new catalysts for target reactions, providing powerful AI tools for molecular synthesis. These studies reveal the immense potential of machine learning technology in synthetic chemistry, promising to accelerate the process from synthesis method development to the discovery of functional molecules. The LHASA program developed by [PERSON] and colleagues facilitates the design of complex molecular synthesis pathways through an interactive graphical interface, embodying the concept of retrosynthetic reasoning from target molecules to simple starting materials, thereby optimizing synthetic routes [61]. This retrosynthetic analysis emphasizes logical and systematic synthesis planning, effectively preventing combinatorial explosion. These advancements demonstrate how enhanced computational capabilities foster the development of scientific research tools, providing robust support to scientists and driving progress in scientific exploration, fulfilling [PERSON]'s prophecy of computers accelerating scientific revolutions. Toste FD's research constructs multiscale theoretical models of chiral anion catalytic systems, revealing the dynamic structure-activity relationship between catalysts and substrates, providing a new theoretical paradigm for regulating stereoelectronic effects induced by asymmetry [62]. [PERSON]'s team delves deep into cross-coupling reactions, conducting in-depth research. This study, through a cross-dimensional synergistic framework of multiscale computation and multi-conformational molecular dynamics simulation (MDMC), successfully decouples the nonlinear coupling mechanism of stereoselectivity, offering an engineerable system of stereocontrol parameters for the precise regulation of asymmetric catalytic reactions [63]. On this basis, [PERSON] et al. [64] in 2019 pioneered the enantioselective supramolecular regulation theory to provide synthetic design guidance. [PERSON] et al.