Neuroadaptive Intelligence: A Biologically-Inspired, Self-Repairing Neural Layer with Adaptive Learning for Molecular Graph Classification

This conference paper introduces Neuroadaptive Intelligence, a novel approach inspired by biological systems. It details the development of a self-repairing neural layer incorporating adaptive learning mechanisms specifically designed for the complex task of molecular graph classification. This work represents a significant contribution to biologically-inspired AI and its application in scientific domains, particularly drug discovery and molecular property prediction.

Author:

• Md Azizul Hakim (Lead Author, Bangladesh Sweden Polytechnic Institute, Bangladesh)

DOI: 10.1109/QPAIN66474.2025.11171860

Key Contributions:

• Developed self-repairing neural layer architecture with adaptive learning mechanisms
• Demonstrated neuroadaptive intelligence principles in molecular graph classification
• Advanced biologically-inspired AI methodologies for scientific computing applications
• Sole-author publication demonstrating independent research capability

Recommended citation: Hakim, M.A. (2025). "Neuroadaptive Intelligence: A Biologically-Inspired, Self-Repairing Neural Layer with Adaptive Learning for Molecular Graph Classification." IEEE Conference Proceedings. DOI: 10.1109/QPAIN66474.2025.11171860
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