具有Google抗重力的代理化学信息学
设计新药需要从图表，数字和文献中整理复杂的数据，培训预测模型，以及提出候选分子来合成——一个单一迭代的工作流程通常需要数天。
在这里，我们展示Google抗重力，装备我们新的化学信息学技能，如何加速药物发现工作流程。 使用现实世界的肺癌治疗方法，agent通过数千种化合物进行搜索，进行匹配的分子配对分析，并在短短一个小时内提出带有合成途径的新型刚性宏观循环。
化学是科学最丰富的公共记录之一，它提供了一个将物理和数学与生命和环境科学联系起来的共同代表。 分子及其转化无处不在，并塑造着日常生活。
尽管在化学信息学上取得了几十年的进步，但化学家仍然花费过多的时间对数据进行纠缠，这一过程由于分子可以从许多方面得到体现而复杂化：从文字描述到图像或数据结构。 同样的分子可以作为一个IUPAC名称出现在论文中，在专利中作为马库什结构，或者在目录中作为结构数据文件(SDF)出现。 先进的化学信息系统必须能够统一这些来源，并处理其中可能发生的许多错误。 这个任务没有误差的空间 — — 改变一个单一的原子或结合，你完全描述了一种不同的化合物，使有希望的抑制剂或催化剂惰性。
为了让科学家专注于假设而不是数据争吵，我们为Google抗重力建造了一套化学信息学技能套件，把表面变成一个合作的科学工作台，可以充分获取计算化学家和实验长凳化学家的本地工具。
这些技能还创造了一种分工，即基础语言模型处理工作流程的管弦，数据库导航和科学推理，而决定性化学工具则执行分子计算。 基于RDKit的开源化学信息学工具箱由全球贡献者团体开发，20年来与来自ChEMBL和PubChem的资源连接，技能领域最佳做法直接输入agent的工作流程。
固定软盘分子
在这个技术的走法中，我们强调一种提高药物疗效的经典药用化学技术：刚性化。
药物分子可能很复杂，常常通过可旋转的结合将多个亚结构联系起来。 这些结合使分子扭曲成许多立体形状(构型)，使其在溶液中“软”化。 然而，为了绑定目标，软盘分子必须锁定在单一的活性形状中。 失去这种行动自由就意味着要处以热带刑罚，这削弱了捆绑的亲和，降低了效力。
为了解决这个问题，药用化学家经常通过添加新的结合来僵化分子。 由单一可旋转的联结连接的两个子结构可以曲折成许多配体，但会增加第二个联结将它们捆绑在一起，从而大大限制了它们如何在三维空间中移动。 通过将这种结合放在正确的位置上，化学家可以将分子锁在绑定目标所需的精确形状中。 这种通常称为“预先支付热带费用”的战略能够大大增强效力。
在这方面，我们将集中处理一个众所周知的、成功的僵化案例。 药物crizotinib是一种第一代小分子的tyrosine kinase抑制剂(TKI)，用于治疗某些形式的非小细胞肺癌。 在Crizotinib，有五大可转债券。 Lorlatinib是第三代小分子TKI，类似于crizotinib，但在它的末端被循环，将有效可旋转的债券数降低到零，导致其他效应中的强性更高。
协作情报
我们将要求agent通过文献搜索这些药物及其类似物。 agent具有抗重力的Cheminformatics技能，能够从这些文件中提取分子，将它们与报告的数据联系起来，并为下游分析准备化合物。
为了进行更深入的探索，agent 发布是一个交互式的查看器，显示被整理的化合物，其相关数据，以及可定制的散块地块。 例如，用宏观循环状态的颜色来设计可旋转债券的威力，即时的揭示，像lorlatinib这样的宏观循环往往具有较少的可旋转债券和更高的威力。 最终，我们设计这些技能不仅是为了扩大agent的化学能力，而且也是为了让用户-agent合作无缝。
在下载和整理所有数据后，agent能够进行更先进的分析，以支持计算化学家的工作。 作为下一步，我们将进行相应的分子配对分析，这是药用化学中用来将小的，离散的改变对分子的影响隔离开来的一种技术，方法是比较只有这种变化才不同的分子配对。 agent也独立地建议进行这种分析，因为我们期望数据集包括许多信息配对。 我们还将要求agent利用它的分子机器学习能力来建立预测模型。
配对的分子配对分析(MMPA)比较了许多不同的分子配对是否被环化。 分析结果显示，循环会减少可转债券的数量，同时如预期的那样普遍提高效力。 agent还利用不同的功能集和模型架构，建造了大量的强力模型，并在一个被搁置的集合上加以验证。 这些技能为agent提供了如何最好地构建分子机器学习模型的指导——例如，agent会经常使用时间顺序的分裂，对较老的化合物进行培训以预测较新的化合物，当有了这种信息时，但可以回落到脚手架分裂来更好地估计通用性。
最后，我们请agent在其学习的基础上设计新的，改进的宏观循环，它推荐涉及不同化合物的多种反应和合成途径。 agent可以用AlphaFold或分子动力学模拟等物理技术来准备这些化合物进行进一步的分析。
完成分析后，结果可以多种形式输出，准备装入许多行业标准化学软件包。
同时，还必须知道这些工具不能做什么。 片面数据，片间化验噪声，以及狭窄的动态范围限制了即使是最强大的模型的预测精度；计算设计是可测试的假说，而不是成品药物。 借助抗重力的化学信息学技能，药物发现科学家可以更快地生成得到更有力支持的设计假设，但每个拟议设计都必须进行合成和实验验证。
研究人员作出有意义的科学决定的速度正在加快。 不久以前，在此展示的工作流程需要几天时间，需要大量的计算专门知识。 使用反重力作为科学工作台，可以使计算和实验化学家们加速工作流程，并专注于真正重要的问题。
鸣谢
化学信息学技能在今天的Google抗重力上可以得到。 下载Antigravity App并安装Science插件开始使用这些技能。 探索科学在这里使用案例。
您也可以以企业用户的身份从化学信息学开始，选择“ 使用商业账户 ”，用您的 Google Cloud 证书登录到应用程序。
为了进一步加快研究步伐，我们免费提供1万名科学家在Google反重力系统中的最高使用水平，为期6个月(滚动上船)，但需符合资格、能力和选择条件。 申请现已开放。
我们谨感谢下列小组成员的贡献：菲利普·拉德林斯基、阿尔滕·索博列夫、维多利亚·约翰斯顿、汉娜·基利-科林斯、扎卡里·吴、王朱、杰克·登格尔、阿德里亚娜·费尔南德斯·拉腊、恩加恩·瓦朗、道格·弗里茨、云汉·徐、塞巴斯蒂安·诺沃津、普希米特·科赫利。
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
Agentic cheminformatics with Google Antigravity
Designing new medicines requires curating complex data from charts, figures and literature, training predictive models, and proposing candidate molecules to synthesize — a workflow that typically takes multiple days for a single iteration.
Here we show how Google Antigravity, equipped with our new Cheminformatics Skills, accelerates drug discovery workflows. Using real-world lung cancer treatments, an agent searches through thousands of compounds, performs matched molecular pair analysis, and proposes novel rigidified macrocycles with synthetic routes in just an hour.
Chemistry is one of science’s richest public records — spanning millions of structures across journal articles and vendor catalogs. It provides a common representation that links physics and mathematics to the life and environmental sciences. Molecules and their transformations are ubiquitous and shape the everyday world.
Despite decades of progress in cheminformatics, chemists still spend too much time wrangling data, a process complicated by the fact that molecules can be represented in many ways: from text descriptions to images or data structures. The same molecule can appear as an IUPAC name in a paper, a Markush structure in a patent, or a structure-data file (SDF) in a catalog. Advanced cheminformatics systems must be able to unify these sources and deal with the many ways errors can arise in them. This task has no margin for error – alter a single atom or bond and you have described a different compound altogether, rendering a promising inhibitor or catalyst inert.
To let scientists focus on hypotheses rather than data wrangling, we built a suite of Cheminformatics Skills for Google Antigravity, turning the surface into a collaborative science workbench with full access to local tooling for both computational chemists and experimental bench chemists.
The skills also create a division of labour where the underlying language model handles workflow orchestration, database navigation and scientific reasoning, while deterministic chemistry tools execute molecular calculations. Grounded in RDKit, the foundational open source cheminformatics toolkit developed over two decades by a global community of contributors, alongside connectors to curated resources from ChEMBL and PubChem, the skills encode domain best practices directly into the agent’s workflow.
Rigidifying a floppy molecule
In this technical walkthrough, we highlight a classic medicinal chemistry technique to improve drug potency: rigidification.
Drug molecules can be complex, often linking multiple substructures through rotatable bonds. Those bonds let the molecule twist into many 3D shapes (conformations), making it “floppy” in solution. To bind its target, however, a floppy molecule must lock into a single active shape. Losing that freedom of motion carries an entropic penalty, which weakens binding affinity and lowers potency.
To solve this problem, medicinal chemists often rigidify molecules by adding new bonds. Two substructures linked by a single rotatable bond can twist into many conformations, but adding a second bond to tie them together sharply limits how they can move in three dimensional space. By placing that bond in the right spot, chemists can lock the molecule into the exact shape needed to bind its target. This strategy, often called “paying the entropic cost in advance”, can dramatically boost potency.
Here, we will focus on a well-known case of successful rigidification. The drug crizotinib is a first-generation, small-molecule tyrosine kinase inhibitor (TKI) used to treat certain forms of non-small cell lung carcinoma. In crizotinib, there are five major rotatable bonds. Lorlatinib is a third-generation, small-molecule TKI that resembles crizotinib, but has been cyclized at its end to reduce the effective number of rotatable bonds to zero, leading to higher potency among other effects.
Collaborative Intelligence
We will ask the agent to search through literature for these drugs and their analogues. With the Cheminformatics skills in Antigravity, the agent is able to extract molecules from these documents, associate them with the data reported, and prepare the compounds for downstream analysis.
For deeper exploration, the agent launches an interactive viewer displaying the curated compounds, their associated data, and customizable scatter plots. For example, plotting potency against rotatable bonds, colored by macrocycle status, immediately reveals that macrocycles like lorlatinib tend to have fewer rotatable bonds and higher potency. Ultimately, we designed these skills to not only expand the agent’s chemistry capabilities, but also to make user-agent collaboration seamless.
After downloading and curating all the data, the agent is able to perform more advanced analysis to support the work of a computational chemist. As a next step, we will perform matched molecular pair analysis, a technique used in medicinal chemistry to isolate the effects of small, discrete changes to a molecule, by comparing pairs of molecules that only differ by that change. The agent had also independently suggested this analysis, as we expect the dataset to include many informative pairs. We will also ask the agent to build a predictive model using its molecular machine learning capabilities.
The matched molecular pair analysis (MMPA) compared many pairs of molecules that differ by whether or not they are cyclized. The results of the analysis show that cyclization reduces the number of rotatable bonds while generally increasing potency, as expected. The agent also built a large number of potency models using varying feature sets and model architectures, and validated them on a held-out set. The skills provide the agent with guidance on how to best build molecular machine learning models - for example, the agent will often use chronological splits, training on older compounds to predict newer ones, when this information is available, but can fall back to scaffold splits to better estimate generalizability.
Finally, we ask the agent to design new, improved macrocycles based on its learnings and it recommends multiple reactions and synthesis routes involving different compounds. The agent can prepare these compounds for further analysis with AlphaFold or physics-based techniques like molecular dynamics simulation.
Once we are done with our analysis, the results can be exported in various formats, ready to be loaded into many industry standard chemistry software packages.
At the same time, it is also important to know what these tools cannot do. Sparse data, inter-lab assay noise, and narrow dynamic ranges limit the predictive precision of even the most powerful models; computational designs are testable hypotheses, not finished drugs. With the Cheminformatics Skills in Antigravity, a drug discovery scientist can generate more strongly-supported design hypotheses faster, but every proposed design must be synthesized and validated experimentally.
The speed at which researchers can make meaningful scientific decisions is accelerating. Not long ago, the workflow demonstrated here would have taken multiple days and required significant computational expertise. Using Antigravity as a science workbench allows both computational and experimental chemists to accelerate their workflows and focus on the questions that really matter.
Acknowledgements
The cheminformatics skills are available on Google Antigravity today. Download the Antigravity App and install the Science plugin to start using these skills. Explore Science use cases here.
You can also get started with cheminformatics on Antigravity today as an Enterprise user, by choosing “Use Business Account” and logging into the app with your Google Cloud credentials.
To further advance the pace of research, we’re offering 10,000 scientists the highest usage tier in Google Antigravity free of charge for 6 months (onboarded on a rolling basis), subject to eligibility, capacity, and selection. Applications are now open.
We would like to acknowledge the contributions of the following team members: Filip Radlinski, Artem Sobolev, Victoria Johnston, Hannah Kiely-Collins, Zachary Wu, Jue Wang, Jack Dunger, Adriana Fernandez Lara, Ngân Vũ, Doug Fritz, Yunhan Xu, Sebastian Nowozin, Pushmeet Kohli.
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