BindGPT：一个通过语言建模与强化学习实现三维分子设计的可扩展框架
BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning
AI总结：
BindGPT 提出用预训练语言模型联合生成蛋白质结合位点内的三维分子图与构象，并结合强化学习微调，在不依赖等变性假设的情况下以更低采样成本达到或超越专用扩散模型、语言模型和图神经网络的性能。
摘要：
针对给定蛋白质生成新颖活性分子，对生成模型而言是一项极具挑战性的任务，需要理解分子与其环境之间复杂的物理相互作用。本文提出了一种新颖的生成模型 BindGPT，它采用概念简单但功能强大的方法，在蛋白质结合位点内生成三维分子。该模型联合生成分子图和三维构象，无需额外的图重建步骤。我们在大规模数据集上对 BindGPT 进行预训练，并使用外部模拟软件给出的评分通过强化学习进行微调。我们展示了单一预训练语言模型如何同时充当三维分子生成模型、以分子图为条件的三维构象生成器，以及以口袋为条件的三维分子生成器。值得注意的是，该模型未对生成域作任何表示等变性假设。我们表明，这种简单的概念方法与预训练和规模化相结合，性能可与当前最优秀的专用扩散模型、语言模型和图神经网络相媲美甚至更优，同时采样成本低两个数量级。
英文摘要：
Generating novel active molecules for a given protein is an extremely challenging task for generative models that requires an understanding of the complex physical interactions between the molecule and its environment. In this paper, we present a novel generative model, BindGPT which uses a conceptually simple but powerful approach to create 3D molecules within the protein's binding site. Our model produces molecular graphs and conformations jointly, eliminating the need for an extra graph reconstruction step. We pretrain BindGPT on a large-scale dataset and fine-tune it with reinforcement learning using scores from external simulation software. We demonstrate how a single pretrained language model can serve at the same time as a 3D molecular generative model, conformer generator conditioned on the molecular graph, and a pocket-conditioned 3D molecule generator. Notably, the model does not make any representational equivariance assumptions about the domain of generation. We show how such simple conceptual approach combined with pretraining and scaling can perform on par or better than the current best specialized diffusion models, language models, and graph neural networks while being two orders of magnitude cheaper to sample.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
