基于分子骨架的GPT模型，可优化药物性能并保留原有功能
ScaffoldGPT: A Scaffold-based GPT Model for Drug Optimization
- 分三阶段优化：预训练、微调、解码控制
- 在新冠与癌症数据集上超越现有方法
- 适合药物研发人员快速生成优化分子
针对快速变异的病毒株和耐药癌细胞，药物优化愈发重要。但需在保留原药有益特性的同时提升新属性，仍具挑战。本文提出ScaffoldGPT，一种基于分子骨架的生成式预训练变换模型。包含三项核心：(1) 三阶段优化流程（预训练-微调-解码优化）；(2) 面向骨架的两阶段增量预训练策略；(3) 基于令牌级别的解码优化方法Top-N，实现受奖励引导的可控生成。在新冠与癌症基准测试中，ScaffoldGPT在保持原始骨架功能的基础上，显著优于基线模型，有效提升期望属性。
原文摘要 · Abstract (English)
Drug optimization has become increasingly crucial in light of fast-mutating virus strains and drug-resistant cancer cells. Nevertheless, it remains challenging as it necessitates retaining the beneficial properties of the original drug while simultaneously enhancing desired attributes beyond its scope. In this work, we aim to tackle this challenge by introducing ScaffoldGPT, a novel Generative Pretrained Transformer (GPT) designed for drug optimization based on molecular scaffolds. Our work comprises three key components: (1) A three-stage drug optimization approach that integrates pretraining, finetuning, and decoding optimization. (2) A novel two-phase incremental pre-training strategy for scaffold-based drug optimization. (3) A token-level decoding optimization strategy, Top-N, that enabling controlled, reward-guided generation using the pretrained or finetuned GPT. We demonstrate via a comprehensive evaluation on COVID and cancer benchmarks that ScaffoldGPT outperforms the competing baselines in drug optimization benchmarks, while excelling in preserving original functional scaffold and enhancing desired properties.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。