  In the expansive realm of drug discovery, with approximately 15,000 known
drugs and only around 4,200 approved, the combinatorial nature of the chemical
space presents a formidable challenge. While Artificial Intelligence (AI) has
emerged as a powerful ally, traditional AI frameworks face significant hurdles.
This manuscript introduces CardiGraphormer, a groundbreaking approach that
synergizes self-supervised learning (SSL), Graph Neural Networks (GNNs), and
Cardinality Preserving Attention to revolutionize drug discovery.
CardiGraphormer, a novel combination of Graphormer and Cardinality Preserving
Attention, leverages SSL to learn potent molecular representations and employs
GNNs to extract molecular fingerprints, enhancing predictive performance and
interpretability while reducing computation time. It excels in handling complex
data like molecular structures and performs tasks associated with nodes, pairs
of nodes, subgraphs, or entire graph structures. CardiGraphormer's potential
applications in drug discovery and drug interactions are vast, from identifying
new drug targets to predicting drug-to-drug interactions and enabling novel
drug discovery. This innovative approach provides an AI-enhanced methodology in
drug development, utilizing SSL combined with GNNs to overcome existing
limitations and pave the way for a richer exploration of the vast combinatorial
chemical space in drug discovery.
