Prioritizing Computational Cocrystal Prediction Methods for Experimental Researchers: A Review to Find Efficient,...

Area of Science:

• Pharmaceutical Science
• Materials Science
• Computational Chemistry

Background:

• Pharmaceutical cocrystals are key for improving drug properties, enhancing therapeutic performance and patient experience.
• Predicting cocrystal formation computationally saves time and resources by identifying stable structures before experimental synthesis.
• Understanding active pharmaceutical ingredient (API) and coformer interactions is crucial for successful cocrystal design.

Purpose of the Study:

• To provide an overview and evaluation of commonly used cocrystal prediction methods.
• To assess prediction methods based on efficiency, cost-effectiveness, and user-friendliness.
• To guide experimental researchers, especially those with limited computational expertise, in selecting initial cocrystal design tools.

Main Methods:

• Literature review of computational and theoretical cocrystal prediction techniques.
• Evaluation of methods using criteria: efficiency, cost-effectiveness, and user-friendliness.
• Comparative analysis of different prediction tools and workflows.

Main Results:

• Cocrystal prediction methods vary significantly in their efficiency, cost, and ease of use.
• No single method is universally optimal; the best choice depends on specific research needs and available resources.
• Combining multiple prediction methods can offer a more robust approach to rational cocrystal design.

Conclusions:

• Experimental researchers can benefit from a structured approach to selecting cocrystal prediction tools.
• Prioritizing user-friendly and efficient methods is recommended for initial cocrystal design workflows.
• Further research may explore synergistic benefits of integrated computational and experimental strategies in cocrystal development.