An overview of reaction outcome prediction with physics-based and data-driven methods

Area of Science:

• Chemistry
• Computational Chemistry
• Chemical Reactivity

Background:

• Predicting reaction outcomes is a fundamental challenge in chemistry.
• Accurate prediction is vital for synthetic planning and in silico experiments.
• Understanding chemical reactivity is reflected in our ability to predict reaction products.

Purpose of the Study:

• To review diverse methodologies for predicting chemical reaction outcomes.
• To categorize prediction approaches into single-step and two-part strategies.
• To discuss both data-driven and physics-based methods.

Main Methods:

• Examined data-driven approaches like graph-based and sequence-generation models.
• Reviewed physics-based methods including potential energy surface exploration and reactive molecular dynamics.
• Discussed methods for predicting reaction selectivity, regioselectivity, stereoselectivity, and yield.

Main Results:

• Reaction outcome prediction methods can be single-step or involve candidate enumeration followed by likelihood prediction.
• Both data-driven and physics-based computational approaches are employed.
• Quantitative predictions of selectivity and yield are also addressed.

Conclusions:

• The field of reaction outcome prediction is advancing with diverse methodologies.
• Future directions involve further integration of computational and data-driven techniques.
• Improved prediction capabilities enhance our understanding of chemical reactivity.