Artificial intelligence (AI)-driven methods can vastly improve the
historically costly drug design process, with various generative models already
in widespread use. Generative models for de novo drug design, in particular,
focus on the creation of novel biological compounds entirely from scratch,
representing a promising future direction. Rapid development in the field,
combined with the inherent complexity of the drug design process, creates a
difficult landscape for new researchers to enter. In this survey, we organize
de novo drug design into two overarching themes: small molecule and protein
generation. Within each theme, we identify a variety of subtasks and
applications, highlighting important datasets, benchmarks, and model
architectures and comparing the performance of top models. We take a broad
approach to AI-driven drug design, allowing for both micro-level comparisons of
various methods within each subtask and macro-level observations across
different fields. We discuss parallel challenges and approaches between the two
applications and highlight future directions for AI-driven de novo drug design
as a whole. An organized repository of all covered sources is available at
this https URL

人工智能驱动的方法可以大大改善历史上昂贵的药物设计过程，各种生成模型已经广泛使用。特别是对于全新药物设计的生成模型，侧重于完全从零开始创建新的生物化合物，展示了一个有前途的未来方向。通过对该领域的迅速发展以及药物设计过程的固有复杂性，为新研究者进入创造了一个困难的环境。在这项调查中，我们将全新药物设计分类为两个主要主题：小分子和蛋白质生成。在每个主题中，我们确定了各种子任务和应用，突出重要的数据集、基准和模型架构，并比较了顶级模型的性能。我们采取广泛的方法进行人工智能驱动的药物设计，允许在每个子任务中对各种方法进行微观水平的比较，并跨不同领域进行宏观观察。我们讨论了这两个应用之间的并行挑战和方法，并强调了人工智能驱动的全新药物设计的未来方向。所有涵盖的资料的有组织存储库可以在此链接中找到：https://www.example.com

基于生成的 AI 在全新药物设计中的调查：分子和蛋白质生成的新领域

A Survey of Generative AI for De Novo Drug Design: New Frontiers in  Molecule and Protein Generation

Protein language models have shown remarkable success in learning biological
information from protein sequences. However, most existing models are limited
by either autoencoding or autoregressive pre-training objectives, which makes
them struggle to handle protein understanding and generation tasks
concurrently. We propose a unified protein language model, xTrimoPGLM, to
address these two types of tasks simultaneously through an innovative
pre-training framework. Our key technical contribution is an exploration of the
compatibility and the potential for joint optimization of the two types of
objectives, which has led to a strategy for training xTrimoPGLM at an
unprecedented scale of 100 billion parameters and 1 trillion training tokens.
Our extensive experiments reveal that 1) xTrimoPGLM significantly outperforms
other advanced baselines in 18 protein understanding benchmarks across four
categories. The model also facilitates an atomic-resolution view of protein
structures, leading to an advanced 3D structural prediction model that
surpasses existing language model-based tools. 2) xTrimoPGLM not only can
generate de novo protein sequences following the principles of natural ones,
but also can perform programmable generation after supervised fine-tuning (SFT)
on curated sequences. These results highlight the substantial capability and
versatility of xTrimoPGLM in understanding and generating protein sequences,
contributing to the evolving landscape of foundation models in protein science.

通过创新的预训练框架，提出了一个统一的蛋白质语言模型 xTrimoPGLM，能够同时处理蛋白质理解和生成任务，拥有超过 1000 亿个参数和 1 万亿个训练标记的前所未有的规模。在 18 个蛋白质理解基准测试中，xTrimoPGLM 明显优于其他先进基准，能够提供蛋白质结构的原子分辨率视图，并超越现有基于语言模型的工具的 3D 结构预测能力。此外，xTrimoPGLM 不仅可以根据自然原理生成全新的蛋白质序列，还可以在有监督的精调后进行可编程的生成。这些结果凸显了 xTrimoPGLM 在理解和生成蛋白质序列方面的重大能力和多功能性，在蛋白质科学基础模型的发展中具有重要意义。

xTrimoPGLM: 语言解码的统一百亿级预训练变压器

xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering  the Language of Protein

Protein language models learn powerful representations directly from
sequences of amino acids. However, they are constrained to generate proteins
with only the set of amino acids represented in their vocabulary. In contrast,
chemical language models learn atom-level representations of smaller molecules
that include every atom, bond, and ring. In this work, we show that chemical
language models can learn atom-level representations of proteins enabling
protein generation unconstrained to the standard genetic code and far beyond
it. In doing so, we show that language models can generate entire proteins atom
by atom -- effectively learning the multiple hierarchical layers of molecular
information that define proteins from their primary sequence to their
secondary, and tertiary structure. We demonstrate language models are able to
explore beyond protein space -- generating proteins with modified sidechains
that form unnatural amino acids. Even further, we find that language models can
explore chemical space and protein space simultaneously and generate novel
examples of protein-drug conjugates. The results demonstrate the potential for
biomolecular design at the atom level using language models.

蛋白质语言模型和化学语言模型可以学习蛋白质的原子级表示，实现无限制的蛋白质生成，包括修改侧链和生成蛋白质 - 药物结合物，展示了语言模型在原子级别的生物分子设计的潜力。