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.

翻译：在药物发现的广阔领域中，已知药物约有15,000种，而获批药物仅约4,200种，化学空间的组合性质构成了巨大挑战。尽管人工智能已成为强大的盟友，但传统AI框架仍面临重大障碍。本文介绍了CardGraphormer，这是一种开创性的方法，将自监督学习、图神经网络和基数保持注意力相结合，以革新药物发现。CardGraphormer是Graphormer与基数保持注意力的新颖组合，利用自监督学习学习强大的分子表征，并采用图神经网络提取分子指纹，从而在减少计算时间的同时提升预测性能和可解释性。它擅长处理分子结构等复杂数据，并执行与节点、节点对、子图或整个图结构相关的任务。CardGraphormer在药物发现和药物相互作用方面的潜在应用十分广泛，从识别新药靶点到预测药物间相互作用，再到实现新药发现。这种创新方法在药物开发中提供了一种增强的AI技术，利用自监督学习与图神经网络相结合来克服现有局限，为更深入地探索药物发现中广袤的组合化学空间铺平了道路。