Conotoxins: Classification, Prediction, and Future Directions in Bioinformatics.

Area of Science:

• Biochemistry
• Pharmacology
• Computational Biology

Background:

• Conotoxins are peptides from *Conus* species venom with specific ion channel and receptor interactions.
• Their pharmacological properties offer potential for drug development and molecular tools.
• Traditional conotoxin analysis is labor-intensive, driving the need for computational methods.

Purpose of the Study:

• To review recent advancements in applying machine learning (ML) and deep learning (DL) to conotoxin research.
• To compare databases, feature extraction, and classification models used in ML/DL for conotoxins.
• To discuss future directions for enhancing therapeutic discovery using computational approaches.

Main Methods:

• Review of current literature on ML and DL applications in conotoxin research.
• Comparison of various databases, feature extraction techniques, and classification models.
• Analysis of emerging trends and future research avenues.

Main Results:

• ML and DL have significantly improved sequence-based classification and functional prediction of conotoxins.
• Various computational approaches are being developed for de novo peptide design.
• The review highlights the effectiveness of ML/DL in accelerating conotoxin research.

Conclusions:

• Computational methods, particularly ML and DL, are essential for efficient conotoxin research.
• Integration of multimodal data and refined predictive models will further enhance therapeutic discovery.
• Future research should focus on leveraging AI for novel conotoxin-based drug development.