Generative models for 3D drug design have gained prominence recently for
their potential to design ligands directly within protein pockets. Current
approaches, however, often suffer from very slow sampling times or generate
molecules with poor chemical validity. Addressing these limitations, we propose
Semla, a scalable E(3)-equivariant message passing architecture. We further
introduce a molecular generation model, MolFlow, which is trained using flow
matching along with scale optimal transport, a novel extension of equivariant
optimal transport. Our model produces state-of-the-art results on benchmark
datasets with just 100 sampling steps. Crucially, MolFlow samples high quality
molecules with as few as 20 steps, corresponding to a two order-of-magnitude
speed-up compared to state-of-the-art, without sacrificing performance.
Furthermore, we highlight limitations of current evaluation methods for 3D
generation and propose new benchmark metrics for unconditional molecular
generators. Finally, using these new metrics, we compare our model's ability to
generate high quality samples against current approaches and further
demonstrate MolFlow's strong performance.

通过引入可伸缩的 E (3)- 等变消息传递体系结构 Semla 和使用流匹配训练的分子生成模型 MolFlow，本研究解决了当前 3D 药物设计中的慢速采样和生成化学有效分子的问题，并且提出了用于无条件分子生成器的新的基准度量方法，最终实验结果表明 MolFlow 显著提升了样本质量和性能。

利用流匹配与尺度最优传输进行高效的三维分子生成

Efficient 3D Molecular Generation with Flow Matching and Scale Optimal  Transport

Dialog summarization has become increasingly important in managing and
comprehending large-scale conversations across various domains. This task
presents unique challenges in capturing the key points, context, and nuances of
multi-turn long conversations for summarization. It is worth noting that the
summarization techniques may vary based on specific requirements such as in a
shopping-chatbot scenario, the dialog summary helps to learn user preferences,
whereas in the case of a customer call center, the summary may involve the
problem attributes that a user specified, and the final resolution provided.
This work emphasizes the significance of creating coherent and contextually
rich summaries for effective communication in various applications. We explore
current state-of-the-art approaches for long dialog summarization in different
domains and benchmark metrics based evaluations show that one single model does
not perform well across various areas for distinct summarization tasks.

对于不同的领域来说，长对话摘要的一种模型无法很好地执行各种任务，因此本研究探讨了当前领域内长对话摘要的最新方法和评估指标。