论文标题
学习发现药物
Learning to Discover Medicines
论文作者
论文摘要
发现新药物是人类努力过着更长更长的寿命的标志。然而，由于我们需要冒险进入更疯狂的生物医学空间，以找到与当今高标准相匹配的生物医学空间，因此发现的速度已经减慢了。通过强大的计算，大型生物医学数据库和深度学习者的突破来支持现代AI，因为AI迅速成熟，可以打破这种循环的新希望，并准备对该地区产生巨大影响。在本文中，我们回顾了旨在克服这一挑战的AI方法的最新进展。我们将AI的广泛且迅速增长的文献组织为药物发现，分为三个相对稳定的亚地区：（a）对分子序列和几何图的表示； （b）数据驱动的推理，我们可以预测分子特性及其结合，优化现有化合物，产生从头分子并计划靶分子的合成； （c）基于知识的推理，我们在其中讨论了生物医学知识图的构建和推理。我们还将确定开放的挑战，并记录未来几年可能的研究指示。
Discovering new medicines is the hallmark of human endeavor to live a better and longer life. Yet the pace of discovery has slowed down as we need to venture into more wildly unexplored biomedical space to find one that matches today's high standard. Modern AI-enabled by powerful computing, large biomedical databases, and breakthroughs in deep learning-offers a new hope to break this loop as AI is rapidly maturing, ready to make a huge impact in the area. In this paper we review recent advances in AI methodologies that aim to crack this challenge. We organize the vast and rapidly growing literature of AI for drug discovery into three relatively stable sub-areas: (a) representation learning over molecular sequences and geometric graphs; (b) data-driven reasoning where we predict molecular properties and their binding, optimize existing compounds, generate de novo molecules, and plan the synthesis of target molecules; and (c) knowledge-based reasoning where we discuss the construction and reasoning over biomedical knowledge graphs. We will also identify open challenges and chart possible research directions for the years to come.
