Review of the latest progress of AI and machine learning methods in the free energy kinetics estimation and synthesis analysis for organic chemistry applications

Abstract

The accurate prediction of free energy, kinetics, and reaction outcomes in organic chemistry has long presented significant computational challenges, which are often limited by the prohibitive cost of high-precision ab initio methods for complex systems. ^1-2 Artificial intelligence and machine learning have transformed computational chemistry through a data-driven approach in property prediction, kinetics, and synthetic design, offering an alternative option for addressing this challenge. This review examines the latest advancements and the potential of artificial intelligence (AI) and machine learning (ML) to overcome these limitations. We highlight groundbreaking progress across several key domains: Dataset-based machine learning and hybrid quantum mechanical/machine learning models that achieve superior accuracy in free energy and kinetics predictions with reduced computational costs. Machine learning models that enable rapid ${\text{pK}}_a$ predictions across a wide range of diverse solvents. The integration of thermodynamic principles into a machine learning model for accurate and consistent macro-micro ${\text{pK}}_a$ prediction. Graph-convolutional neural networks that demonstrate high accuracy in reaction outcome prediction with interpretable mechanisms. Neural-symbolic frameworks and Monte Carlo Tree Search (MCTS) integrated with deep neural networks that revolutionize retrosynthetic planning, generating expert-quality routes at unprecedented speeds. A machine learning model based on molecular orbital reaction theory for organic reaction outcome prediction with remarkable accuracy and generalizability. Finally, hierarchical neural networks that predict comprehensive reaction conditions interdependently with exceptional speed. These advancements in a data-driven approach enhance precision, efficiency, and scalability, but challenges such as data quality, stereochemical prediction, and explicit mechanistic incorporation persist. The convergence of these AI/ML capabilities is paving the way for fully automated chemical discovery, addressing critical global challenges in medicine, materials, and energy.^3,4