Integrating chemical artificial intelligence and cognitive computing for predictive analysis of biological pathways: a case for intrinsically disordered proteins

Incorporating biological molecular interactions into cognitive computing through chemical artificial intelligence (AI) presents a transformative approach with far-reaching implications for various fields, such as protein engineering, drug discovery, bioinformatics, synthetic biology, and unconventional computing. Cognitive computing, designed to emulate human thought processes and enhance decision-making, utilizes technologies, such as machine learning, natural language processing, and speech recognition for better human-system interactions. Despite advancements, the integration of biological processes with cognitive computing remains fraught with challenges, particularly due to the complexity and scale of biological data. Here, we explore the possible benefits of connecting cognitive computing with biological knowledge, including more precise models of protein interactions, gene regulation, and metabolic pathways, which could lead to personalized treatments and early disease detection. Furthermore, we discuss the intersection of cognitive computing and biophysical research techniques, examining how analogies from neuroscience-like synaptic communication and neural plasticity-can inform the development of neuromorphic chips and enhance predictive models. Additionally, the study delves into intrinsically disordered proteins (IDPs) and their crucial roles in brain function and information processing. These insights are pivotal for advancing neuroinformatics and creating more adaptive, context-aware cognitive computing algorithms. By leveraging biophysical investigations and the unique properties of IDPs, the research aims to bridge the gap between the biological processes and their computational analogs, proposing novel methods, such as chemical AI implemented in liquid solutions as promising avenues for future advancements.