融合VAE与自回归模型，提升分子生成的准确性与可控性。
Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation
- 设计新正则化项，结合VAE与自回归模型优势
- 在分子有效性、条件生成上显著优于基线模型
- 适合药物分子设计与风格迁移研究者参考
从头分子设计已成为热门研究方向，得益于先进生成模型的推动。尽管如此，随着研究深入至更复杂的模型与表示方式，一些根本性问题仍未解决。本文回归最简分子表示，重新审视经典生成方法（如变分自编码器VAEs和自回归模型）的被忽视局限。提出一种新型正则化机制，融合两类模型优势，显著提升分子序列的有效性、条件生成能力及风格迁移性能。同时，深入讨论了这些模型行为中常被忽略的假设。实验验证了该方法在多个基准数据集上的优越表现。
原文摘要 · Abstract (English)
De novo molecule design has become a highly active research area, advanced significantly through the use of state-of-the-art generative models. Despite these advances, several fundamental questions remain unanswered as the field increasingly focuses on more complex generative models and sophisticated molecular representations as an answer to the challenges of drug design. In this paper, we return to the simplest representation of molecules, and investigate overlooked limitations of classical generative approaches, particularly Variational Autoencoders (VAEs) and auto-regressive models. We propose a hybrid model in the form of a novel regularizer that leverages the strengths of both to improve validity, conditional generation, and style transfer of molecular sequences. Additionally, we provide an in depth discussion of overlooked assumptions of these models' behaviour.
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