Mining electronic laboratory notebooks: analysis, retrosynthesis, and reaction based enumeration.

Area of Science:

• Computational Chemistry
• Cheminformatics
• Organic Synthesis

Background:

• Electronic laboratory notebooks (ELNs) contain vast amounts of chemical reaction data.
• Extracting and utilizing this knowledge for synthetic planning remains a challenge.

Purpose of the Study:

• To develop an automated system for analyzing chemical reaction knowledge within ELNs.
• To enable in silico retrosynthesis and de novo compound design based on extracted reaction rules.

Main Methods:

• Reactions were simplified to their reactive centers and converted to SMIRKS string representations.
• SMIRKS were used for reaction classification, in silico retrosynthesis, and identifying reaction rules.
• Reaction sequences were analyzed to find common combinations and diversification steps.

Main Results:

• 98% of frequently occurring SMIRKS accurately regenerated the original product.
• A virtual chemical space was explored, showing dependence on reactive center size.
• A retrosynthesis tool was built, generating feasible routes and a library of novel compounds.

Conclusions:

• Automated analysis of ELN data can effectively extract reaction rules for synthetic chemistry.
• The developed tool facilitates de novo design of novel, synthetically accessible molecules.
• This approach significantly expands the discovery of new chemical entities.