Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery.

Area of Science:

• Computational chemistry
• Chemical modeling
• Predictive chemistry

Background:

• Predictive chemistry focuses on developing models for molecular interactions and reactions.
• It extends beyond traditional computer-aided retrosynthesis.
• The field aims for more ambitious goals in chemical prediction.

Purpose of the Study:

• To review areas where predictive chemistry models can accelerate organic reaction development.
• To highlight the potential of these models in advancing synthetic chemistry.
• To summarize applications in reaction deployment, development, and discovery.

Main Methods:

• Review of existing literature on predictive chemistry models.
• Analysis of applications in organic synthesis and reaction prediction.
• Synthesis of current trends and future potential in the field.

Main Results:

• Predictive chemistry models offer significant potential for accelerating chemical research.
• These models can enhance the efficiency of discovering and developing new organic reactions.
• Applications span reaction deployment, development, and discovery, advancing synthetic chemistry.

Conclusions:

• Predictive chemistry is a rapidly evolving field with broad implications for chemical sciences.
• Computational models are key to future advancements in synthetic chemistry.
• The review highlights the transformative impact of predictive chemistry on reaction discovery and development.