The generation of molecules with desired properties has become increasingly popular, revolutionizing the way scientists design molecular structures and providing valuable support for chemical and drug design. However, despite the potential of language models in molecule generation, they face challenges such as generating syntactically or chemically flawed molecules, having narrow domain focus, and struggling to create diverse and feasible molecules due to limited annotated data or external molecular databases. To tackle these challenges, we introduce MolGen, a pre-trained molecular language model tailored specifically for molecule generation. Through the reconstruction of over 100 million molecular SELFIES, MolGen internalizes structural and grammatical insights. This is further enhanced by domain-agnostic molecular prefix tuning, fostering robust knowledge transfer across diverse domains. Importantly, our chemical feedback paradigm steers the model away from molecular hallucinations, ensuring alignment between the model's estimated probabilities and real-world chemical preferences. Extensive experiments on well-known benchmarks underscore MolGen's optimization capabilities in properties such as penalized logP, QED, and molecular docking. Additional analyses confirm its proficiency in accurately capturing molecule distributions, discerning intricate structural patterns, and efficiently exploring the chemical space. Code is available at https://github.com/zjunlp/MolGen.

翻译：具有目标属性的分子生成方法日益普及，彻底改变了科学家设计分子结构的方式，并为化学与药物设计提供了重要支持。然而，尽管语言模型在分子生成方面具有潜力，但它们仍面临诸多挑战：生成存在语法或化学缺陷的分子、领域聚焦狭窄、因标注数据或外部分子数据库有限而难以生成多样且可行的分子。为应对这些挑战，我们提出了MolGen——一个专为分子生成任务预训练的分子语言模型。通过重构超过1亿条分子SELFIES序列，MolGen内化了结构性和语法性知识。领域无关的分子前缀微调进一步增强了这种能力，促进了跨不同领域的鲁棒知识迁移。值得注意的是，我们的化学反馈范式引导模型远离分子幻觉，确保模型估计概率与现实化学偏好保持一致。在知名基准测试上的大量实验，凸显了MolGen在惩罚logP、QED和分子对接等属性上的优化能力。进一步分析证实了其在准确捕获分子分布、识别复杂结构模式以及高效探索化学空间方面的卓越表现。代码已开源至https://github.com/zjunlp/MolGen。