Machine intelligence for chemical reaction space

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Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

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AI-Powered Chemistry: Predicting Reactions, Planning Synthesis, and Optimizing Performance

Machine intelligence is transforming chemical reaction space exploration through data-driven approaches for predicting reaction outcomes, planning syntheses, optimizing reaction performance, designing catalysts, and classifying reactions. These technologies are accelerating discovery and development in both academia and industry, enabling more efficient and sustainable chemical processes.

Possible Conflicts of Interest

One of the authors is affiliated with IBM Research Europe, and another author is affiliated with NextMove Software, which offers a commercially available chemical reaction dataset (Pistachio).

Identified Weaknesses

Benchmark dataset bias

The benchmark datasets for single-step retrosynthesis are biased toward specific reaction classes, which may not reflect real-world performance in multi-step synthesis planning.

Model evaluation challenges

Evaluating single-step retrosynthesis models remains challenging, as current metrics may not transfer well to multi-step settings and human expert evaluation or lab experiments would be ideal but expensive.

Multi-step retrosynthesis evaluation

Fair evaluation of synthesis routes requires a consistent set of commercially available molecules across different approaches and human expert input to account for unreported disconnections and functional group interconversions.

Limited generalizability of reaction performance models

Most reaction performance datasets are specific to single reactions or families of reactions, limiting the generalizability of models.

Limitations of reaction classification schemes

Reaction classification schemes often rely on predefined patterns and may not fully capture the diversity and complexity of chemical reactions.

Rating Explanation

This review provides a comprehensive overview of recent advancements in machine intelligence for chemical reaction space, covering a wide range of tasks and highlighting the potential impact on chemical synthesis and molecular discovery. The paper addresses open challenges and limitations, which are important for future research directions. However, the reliance on existing datasets, which are often biased and limited in scope, introduces a potential conflict of interest.

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