Explorations into Chemical Reactions and Biochemical Pathways.

Area of Science:

• Computational chemistry
• Chemoinformatics
• Bioinformatics

Background:

• Chemical reaction data analysis is crucial for understanding chemical processes.
• Physicochemical effects at reaction sites can be quantified.
• Biochemical reactions offer complex challenges and opportunities for scientific discovery.

Purpose of the Study:

• To present methods for extracting knowledge from chemical reaction data.
• To demonstrate the utility of physicochemical effects in predicting reaction data and classifying reactions.
• To explore applications in biochemical reactions for biological and medicinal insights.

Main Methods:

• Development of methods to calculate physicochemical effects at reaction sites.
• Derivation of equations for predicting gas-phase and solution-phase reaction data.
• Application of physicochemical effects for reaction classification.
• Utilizing the BioPath.Database for biochemical reaction analysis.
• Employing 3D models of reaction intermediates for inhibitor discovery.
• Combining chemoinformatics and bioinformatics for pathway analysis.

Main Results:

• Physicochemical effects enable accurate prediction of reaction data, including aqueous acidity and amide hydrolysis.
• Reactions can be effectively classified into meaningful chemical knowledge categories.
• Biochemical reactions are successfully classified using physicochemical effects, correlating with enzyme classification (EC numbers).
• 3D models of reaction intermediates facilitate the search for novel enzyme inhibitors.
• Combined chemoinformatics and bioinformatics approaches uncover essential disease pathways and bacterial flavor-forming pathways.

Conclusions:

• Physicochemical effect calculations provide a powerful tool for chemical reaction data analysis and prediction.
• This methodology extends to the classification and understanding of complex biochemical processes.
• The integration of chemoinformatics and bioinformatics opens new avenues for drug discovery and understanding biological systems.