【论文速递】Automatic Generation of Novel Highly-Functional Molecular Sequences

英文标题：Automatic Generation of Novel Highly-Functional Molecular Sequences

arXiv编号：2611.03603
作者：Vincent R. Ragusa, Chrisoph Adami
来源：arXiv
原文链接：https://arxiv.org/abs/2611.03603

中文摘要：
While multiple methods now exist to generate functional sequence variants from existing annotated examples, it is unclear how well such methods can be used to generate sequences that exceed the functionality of those contained in the example set. Using two complete fitness landscapes, we quantify how effectively generative tools can search sequence space and find rare, novel, highly functional sequences, focusing on variational auto-encoders and information-theoretic methods. We find that an information-theoretic method that does not require training and runs significantly faster than standard Machine-Learning/Artificial Intelligence (ML/AI) methods also is significantly more effective in finding rare highly-functional sequences. We discuss the application of this novel method to discover rare polypeptides with anti-bacterial properties that are specific to bacterial, but not eukaryotic, cell membranes.

本文由华人空间 AI 论文助手自动抓取翻译，仅供学术交流。