连续变分合成
Continuous Variational Synthesis
- JURA Bio, Inc.（JURA生物公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出连续变分合成方法，通过连续空间训练与训练后量化，使变分合成模型在满足严格奖励的同时保持设计多样性，并在酶、肽、抗体CDRH3及调控DNA元件上实现质量-多样性帕累托最优，且体外性能与模拟一致。
AI中文摘要：
生物机器学习长期以来受限于合成设计DNA的能力。变分合成模型通过控制化学反应来物理制造数以万亿计的设计DNA序列。然而，训练这些生成模型颇具挑战：化学合成的约束可能迫使许多参数进入离散空间，限制了预训练和微调的能力。在本文中，我们使用连续空间中的随机梯度下降训练“自由”变分合成模型，然后通过训练后量化进行离散化，以施加硬件和湿件约束。这使得变分合成模型能够满足严格的奖励标准，同时仍能合成多样化的设计，实现严格占优的质量-多样性帕累托前沿。我们通过训练酶、肽、抗体CDRH3和调控DNA元件的变分合成模型进行了演示。计算机模拟性能在体外实验中得以保持。
英文摘要：
Biological machine learning was long bottlenecked by the ability to synthesize designed DNA. Variational synthesis models control chemical reactions to physically manufacture quadrillions of designed sequences in DNA. However, training these generative models is challenging: constraints on chemical synthesis can force many parameters into a discrete space, limiting the ability to pre-train and fine-tune. In this article we train ``free'' variational synthesis models using stochastic gradient descent in continuous space, and then discretize with post-training quantization to impose hardware and wetware constraints. This enables variational synthesis models to satisfy stringent reward criteria, while still synthesizing diverse designs, achieving a strictly dominating quality-diversity Pareto frontier. We demonstrate by training variational synthesis models of enzymes, peptides, antibody CDRH3s, and regulatory DNA elements. In silico performance is maintained in vitro.
