标题： 生成药物发现的丛林：陷阱、宝藏和出路 显示英文标题

标题： The Jungle of Generative Drug Discovery: Traps, Treasures, and Ways Out

摘要： “如何评估生成模型提出的从头设计？” 尽管生成式深度学习在药物发现中具有变革性的潜力，但这个问题看似简单却还没有明确的答案。 缺乏标准化的指南，这对生成方法的基准测试以及前瞻性研究中分子的选择都构成了挑战。 在本工作中，我们从新的$- \textit{critical}$和$\textit{constructive} -$视角来看待从头设计的评估。 我们系统地研究了广泛使用的评估指标，并揭示了之前被忽视的关键陷阱（“陷阱”）。 此外，我们识别了工具（“宝藏”）和策略（“出路”），以应对生成药物发现的复杂“丛林”，并在这一过程中加强了分子科学和深度学习领域之间的联系。 我们的系统性和大规模结果预计将成为评估生成式深度学习方法提出的从头设计的新视角。 显示英文摘要

摘要： "How to evaluate de novo designs proposed by a generative model?" Despite the transformative potential of generative deep learning in drug discovery, this seemingly simple question has no clear answer. The absence of standardized guidelines challenges both the benchmarking of generative approaches and the selection of molecules for prospective studies. In this work, we take a fresh $- \textit{critical}$ and $\textit{constructive} -$ perspective on de novo design evaluation. We systematically investigate widely used evaluation metrics and expose key pitfalls ('traps') that were previously overlooked. In addition, we identify tools ('treasures') and strategies ('ways out') to navigate the complex 'jungle' of generative drug discovery, and strengthen the connections between the molecular and deep learning fields along the way. Our systematic and large-scale results are expected to provide a new lens for evaluating the de novo designs proposed by generative deep learning approaches.