【muShanghai 主题活动 Themed Event】
【AI未来健康日】
AI 正在重新书写药物研发与健康管理的规则。
这一天，CarbonSilicon AI（碳硅智慧）将分享 AI 如何进入健康管理与精准干预场景，连接真实世界数据、个体化健康洞察与未来医疗体验。
XtalPi（晶泰科技）也将带来 AI 驱动的计算化学、自动化实验与智能研发平台的实践分享，展示 AI + Robotics 如何加速药物发现与生命科学创新。
从 AI 药物研发，到精准健康管理；从计算化学，到机器人实验室自动化——如果你关心 AI 如何真正改变医疗与生命科学的未来，这是你不能错过的一天。
【AI for Future Health Day】
AI is rewriting the rules of drug discovery and health management.
On this day, CarbonSilicon AI will share how AI is entering health management and precision intervention scenarios, connecting real-world data, personalized health insights, and future healthcare experiences.
XtalPi will bring hands-on perspectives on AI-driven computational chemistry, automated experimentation, and intelligent R&D platforms, showing how AI + Robotics can accelerate drug discovery and life science innovation.
From AI drug discovery to precision health management, from computational chemistry to robotic lab automation — if you care about how AI is truly changing the future of healthcare and life sciences, this is a day you should not miss.
【活动说明 Event Details】
1）持有 Full Pass 的朋友可以免费参与。Friends who hold a Full Pass can participate for free.
2）Full Pass 购票链接 Full Pass purchase links:
International: https://www.themu.simplefi.tech/auth
国内：请扫描推文中的二维码https://mp.weixin.qq.com/s/tZD-zf2JVIPRklvJ2r8gdQ
【What is muShanghai】
今年五月，我们邀请你来到上海，和来自全球各地的 Hacker、Researcher、Game Changer，共建一座持续 28 天的科技火人节。
我们把 28 天设计为四个主题周：AI、Robotics、Biotech 和 Culture。每个主题都会拆解成一系列具体场景和活动，让大家可以在不同节点随时进入、参与和共创。
This May, we invite you to Shanghai for muShanghai.
Join hackers, researchers, and game-changers from around the world to co-create a 28-day tech festival inspired by Burning Man.
We’ve designed these 28 days into four theme weeks: AI, Robotics, Biotech, and Culture. Each theme is broken down into specific scenarios and activities, allowing everyone to join, participate, and co-create at different points throughout the month.
【内容介绍】
如果未来的药物与材料研发不再主要依赖盲目“试错”，而是由 AI 预测、机器人实验、数据反馈共同驱动，科学发现会变成什么样？
晶泰科技正在回答这个问题。
这场分享将从晶泰科技的技术实践出发，讨论 AI、量子物理、超级智能体以及机器人实验平台如何重塑分子发现的流程：从靶点理解、分子生成、虚拟筛选，到自动化合成、实验验证与数据回流，研发不再只是线性的人工推进，而逐渐变成一个可以持续学习、持续迭代的闭环系统。
晶泰科技成立于 2015 年，由 MIT 背景的物理学家创立，长期聚焦以 AI 与机器人技术推动生命科学和新材料研发的智能化、数字化转型。它的技术体系把量子物理、AI、云计算和大规模自动化实验平台结合起来，服务于药物发现、新材料、化学自动化、现代化中医药、新能源、农业科学等多个方向。
当 AI 不只会“预测”，机器人不只会“执行”，而是共同构成一个可学习的研发系统时，未来分子发现的速度、尺度和成功率会如何改变？
它不是一场泛泛而谈的 AI 科普。 这场分享会围绕真实研发场景展开，讨论 AI 与机器人如何进入药物和材料发现的核心流程。
它关心的不只是模型，而是完整闭环。 分子发现真正困难的地方，不只是生成一个候选结构，而是如何让设计、合成、测试、反馈形成可持续迭代的系统。
它连接生命科学与未来材料。 同一套“AI × Robotics”的研发范式，正在从小分子药物延展到抗体、材料、能源、化工等更广阔领域。
它来自一线产业实践。 晶泰科技已经与全球多家药企、科研机构和产业伙伴开展合作，并在自动化实验、AI 模型、分子设计与数据驱动研发方面积累了大量经验。
AI 如何进入分子发现。 从分子生成、性质预测、虚拟筛选到候选物优化，AI 如何帮助研发团队更快地探索更大的化学空间。
机器人实验室为什么重要。 自动化实验平台如何 24X7 执行高通量实验、产生标准化数据，并减少传统实验流程中的重复劳动与不可控变量。
“干实验室”与“湿实验室”如何闭环。 计算模型提出假设，机器人实验验证假设，实验数据再反哺模型，形成 Design-Make-Test-Analyze 的持续迭代。
未来分子发现的基础设施是什么。 当 AI、超级智能体、量子物理第一性原理、实验机器人、数据工程和领域专家共同工作，研发组织的能力边界会发生什么变化。
从药物到材料的跨行业想象。 分子层面的发现能力如何支持药物研发、新材料、绿色化学、新能源、农业科学和现代化中医药等方向。
王明泰
晶泰科技高级副总裁
王明泰长期参与晶泰科技在技术创新、产业合作与生态建设中的实践。他将结合晶泰科技在“AI × Robotics驱动研发范式创新”的产业实践，分享未来分子发现如何从“依靠经验的研发流程”走向“由数据驱动的工程化创新”。
晶泰科技是一家由人工智能与机器人技术驱动的创新技术平台公司，成立于 2015 年，致力于推动生命科学和新材料行业的智能化、数字化转型。
公司将量子物理、AI、云计算和大规模机器人实验平台紧密结合，为全球生物医药和新材料企业提供技术解决方案、服务与产品，帮助加速从想法到候选分子、从实验假设到可验证结果的研发过程。
晶泰科技已于 2024 年在香港联交所上市，股票代码 2228.HK。其技术和业务覆盖小分子药物发现、蛋白与抗体设计、肽类发现、固态研究、自动化实验室解决方案、未来材料和化学自动化等方向。
生命科学与药物研发从业者： 想了解 AI 与自动化实验如何改变早期发现、候选物优化和研发决策。
AI、机器人与自动化方向的研究者 / 工程师： 想看到模型、硬件、实验流程和产业场景如何结合。
材料、化学、新能源与合成生物学相关从业者： 想理解分子发现平台如何从药物研发扩展到更多材料与化学问题。
投资人、创业者与产业合作伙伴： 想观察 AI for Science 从概念走向平台化、基础设施化和商业化的路径。
对未来科学发现感兴趣的跨行业观众： 想用非专业但不浅薄的方式理解 AI × Robotics 为什么可能改变研发范式。
一个清晰框架： 如何理解 AI、量子物理、机器人实验与数据闭环在分子发现中的分工。
一个产业视角： 为什么未来的研发竞争不只是“谁的模型更强”，也包括谁能持续获得高质量实验数据、构建自动化基础设施并形成闭环能力。
一组真实问题： AI 药物研发如何避免停留在 demo？机器人实验室如何真正提升研发效率？跨学科团队如何协作？
一个未来想象： 分子发现可能从单点工具，变成一套连接科学假设、自动实验、数据生产和产业应用的新型研发操作系统。
从传统研发到智能闭环： 为什么药物与材料发现需要新的研发基础设施。
AI × Robotics 的核心逻辑： 模型预测、机器人实验、标准化数据与专家知识如何互相增强。
未来分子发现案例与场景： 从小分子、抗体，到新材料和化学自动化。
产业合作与生态建设： AI for Science 如何在药企、科研机构、材料企业和自动化平台之间形成新的协作方式。
开放 Q&A： 欢迎围绕 AI 药物研发、机器人实验室、未来材料、分子设计、产业落地和跨学科团队建设提问。
报名主要用于收集邮箱，以便后续有更新信息时及时通知大家。本活动仅限 muShanghai Pass 持有者参加。
我不是药物研发或材料科学专业背景，可以来吗？
可以。这场分享会尽量用清楚的语言解释 AI × Robotics 如何改变分子发现，同时保留足够的技术密度，适合跨行业观众理解。
这是一场销售活动吗？
不是。这是一场围绕 AI、机器人与未来分子发现的主题分享与交流，不构成采购承诺、投资建议、医疗建议或任何产品性能保证。
现场会涉及很深的专业公式或论文细节吗？
不会以公式推导为主。分享重点是研发范式、技术平台、产业实践和未来趋势。如果你来自 AI、生命科学、材料、化学、投资或创业领域，都可以找到相关视角。
为什么主题里用 “future molecule discovery”？
因为未来的分子发现不只发生在药物研发里，也会影响材料、能源、农业、环境与化学工业。AI 与机器人平台提供的是一种更通用的研发能力：更快地产生假设、更高效地验证假设、更系统地积累可复用数据。
What if future drug and materials R&D were no longer driven mainly by blind trial and error, but by AI prediction, robotic experimentation, and data feedback?
XtalPi is answering this question.
This session will start from XtalPi’s technology practice and discuss how AI, quantum physics, super agents, and robotic experimentation platforms are reshaping the process of molecule discovery: from target understanding, molecule generation, and virtual screening to automated synthesis, experimental validation, and data feedback. R&D is no longer only a linear, human-driven process, but is gradually becoming a closed-loop system that can keep learning and iterating.
Founded in 2015 by MIT-trained physicists, XtalPi has long focused on using AI and robotics to drive the intelligent and digital transformation of life sciences and new materials R&D. Its technology system combines quantum physics, AI, cloud computing, and large-scale automated experimentation platforms, serving drug discovery, new materials, chemical automation, modernized traditional Chinese medicine, renewable energy, agricultural science, and other directions.
When AI does not only “predict” and robots do not only “execute,” but together form a learning R&D system, how will the speed, scale, and success rate of future molecule discovery change?
It is not a generic AI popular science talk. This session will focus on real R&D scenarios and discuss how AI and robotics enter the core processes of drug and materials discovery.
It cares not only about models, but about the full closed loop. The real difficulty of molecule discovery is not only generating a candidate structure, but how to make design, synthesis, testing, and feedback form a continuously iterative system.
It connects life sciences and future materials. The same “AI × Robotics” R&D paradigm is extending from small-molecule drugs to antibodies, materials, energy, chemical engineering, and broader fields.
It comes from frontline industry practice. XtalPi has already collaborated with many global pharmaceutical companies, research institutions, and industry partners, and has accumulated extensive experience in automated experimentation, AI models, molecular design, and data-driven R&D.
How AI enters molecule discovery. From molecule generation, property prediction, and virtual screening to candidate optimization, how AI helps R&D teams explore larger chemical spaces faster.
Why robotic laboratories matter. How automated experimentation platforms can run high-throughput experiments 24X7, generate standardized data, and reduce repetitive labor and uncontrolled variables in traditional experimental workflows.
How “dry labs” and “wet labs” form a closed loop. Computational models propose hypotheses, robotic experiments validate hypotheses, and experimental data feeds back into the models, forming continuous Design-Make-Test-Analyze iteration.
What the infrastructure of future molecule discovery is. When AI, super agents, first principles of quantum physics, experimental robots, data engineering, and domain experts work together, how will the capability boundaries of R&D organizations change?
Cross-industry imagination from drugs to materials. How molecule-level discovery capabilities can support drug R&D, new materials, green chemistry, renewable energy, agricultural science, and modernized traditional Chinese medicine.
Wang Mingtai
Senior Vice President, XtalPi
Wang Mingtai has long participated in XtalPi’s practice in technology innovation, industry collaboration, and ecosystem building. He will draw on XtalPi’s industry practice in “AI × Robotics-driven R&D paradigm innovation” to share how future molecule discovery is moving from “experience-based R&D processes” toward “data-driven engineering innovation.”
XtalPi is an innovative technology platform company driven by artificial intelligence and robotics. Founded in 2015, it is committed to driving the intelligent and digital transformation of the life sciences and new materials industries.
The company tightly combines quantum physics, AI, cloud computing, and large-scale robotic experimentation platforms to provide technology solutions, services, and products for biopharmaceutical and new materials companies worldwide, helping accelerate the R&D process from ideas to candidate molecules and from experimental hypotheses to verifiable results.
XtalPi was listed on the Hong Kong Stock Exchange in 2024 under the stock code 2228.HK. Its technology and business cover small-molecule drug discovery, protein and antibody design, peptide discovery, solid-state research, automated laboratory solutions, future materials, and chemical automation.
Life sciences and drug R&D professionals: Those who want to understand how AI and automated experimentation are changing early discovery, candidate optimization, and R&D decision-making.
AI, robotics, and automation researchers / engineers: Those who want to see how models, hardware, experimental workflows, and industry scenarios come together.
Materials, chemistry, renewable energy, and synthetic biology practitioners: Those who want to understand how molecule discovery platforms can expand from drug R&D to more materials and chemistry problems.
Investors, entrepreneurs, and industry partners: Those who want to observe how AI for Science moves from concept toward platformization, infrastructure, and commercialization.
Cross-industry audiences interested in future scientific discovery: Those who want to understand in a non-professional but not shallow way why AI × Robotics may change the R&D paradigm.
A clear framework: How to understand the division of labor among AI, quantum physics, robotic experimentation, and data closed loops in molecule discovery.
An industry perspective: Why future R&D competition is not only about “whose model is stronger,” but also about who can continuously obtain high-quality experimental data, build automated infrastructure, and form closed-loop capabilities.
A set of real questions: How can AI drug R&D avoid staying at the demo stage? How can robotic laboratories truly improve R&D efficiency? How can interdisciplinary teams collaborate?
A future imagination: Molecule discovery may move from point tools into a new R&D operating system that connects scientific hypotheses, automated experiments, data production, and industrial applications.
From traditional R&D to intelligent closed loops: Why drug and materials discovery need new R&D infrastructure.
The core logic of AI × Robotics: How model prediction, robotic experimentation, standardized data, and expert knowledge reinforce one another.
Future molecule discovery cases and scenarios: From small molecules and antibodies to new materials and chemical automation.
Industry collaboration and ecosystem building: How AI for Science forms new ways of collaboration among pharmaceutical companies, research institutions, materials companies, and automation platforms.
Open Q&A: Questions are welcome around AI drug R&D, robotic laboratories, future materials, molecular design, industrial implementation, and interdisciplinary team building.
Registration is mainly used to collect email addresses so that we can notify everyone in time if there are follow-up updates. This event is for muShanghai Pass holders only.
Can I come if I do not have a drug R&D or materials science background?
Yes. This session will try to explain how AI × Robotics changes molecule discovery in clear language while retaining enough technical density, making it suitable for cross-industry audiences to understand.
Is this a sales event?
No. This is a thematic sharing and exchange around AI, robotics, and future molecule discovery. It does not constitute a procurement commitment, investment advice, medical advice, or any guarantee of product performance.
Will the session involve very deep professional formulas or paper-level details?
It will not focus on formula derivation. The focus of the sharing is the R&D paradigm, technology platform, industry practice, and future trends. If you come from AI, life sciences, materials, chemistry, investment, or entrepreneurship, you can find relevant perspectives.
Why use “future molecule discovery” in the theme?
Because future molecule discovery does not only happen in drug R&D. It will also affect materials, energy, agriculture, the environment, and the chemical industry. AI and robotics platforms provide a more general R&D capability: generating hypotheses faster, validating hypotheses more efficiently, and systematically accumulating reusable data.
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一场真正会“跑起来”的 AI 药物发现现场实验：我们打开一篇公开论文，把里面的分子结构图自动抽出来，丢进蛋白口袋里做对接、打分、ADMET 过筛，再让模型生成几个还不存在的新分子。全程使用公开数据，没有 PPT 催眠——而且就算你听到 docking 只想到划船，也完全可以跟得下来。
如果说过去的研发更像“先懂很多，再慢慢试”，AI 制造则让一个明确的想法更快变成可验证的原型：先定义目标，再调用已有模型、算法和工具，把论文里的线索转化为可筛选、可优化、可迭代的候选分子。这场活动想展示的，不是一个完整药物如何诞生，而是一个想法如何借助 AI 制造能力，快速走到“可以被看见、被评估、被继续推进”的下一步。
关于专业名词的友好声明
这场不是考试，是低门槛的第一排。屏幕上会全程挂一份“人话翻译”，把 docking、ADMET、骨架跃迁、从头设计 这些词都讲清楚。听到陌生词请直接举手，我们随时停下来解释。重要的不是你已经懂这些名词，而是看见它们怎么串成一条完整的流水线。
📖术语小词典
AIDD（AI 药物发现） — 用人工智能来设计、筛选潜在药物，把过去靠经验试错的流程换成“先在电脑里跑一遍”。
分子对接（Docking） — 把一个小分子虚拟地塞进蛋白的“口袋”里，看它们贴不贴合。像拿一串钥匙试一把锁，只是用的是物理 + 机器学习。
ADMET — 吸收 分布 / 代谢 / 排泄 毒性。一句话翻译：“这玩意儿真被人吃下去会发生什么？”
虚拟筛选（Virtual Screening） — 在海量化合物库里先用电脑过一遍，挑出少数值得进一步验证的候选。
骨架跃迁 从头设计（Scaffold-hopping / De novo Design）* — 让模型要么把已知分子的“骨架”换一种结构，要么干脆从零生成一个全新的分子。 <*aside>
碳硅智慧是谁
碳硅智慧（CarbonSilicon AI，杭州，2021 年成立）是国内 AIDD 圈相当扎实的一家公司，团队脱胎自浙江大学侯廷军教授的 CADD 课题组。公司已经上线 DrugFlow、BioFlow、Inno-FEP、SciGPT 等平台；核心算法与模型包括 FragGPT、Delete、RapiDock、CarsiDock、KarmaDock、RTMScore、BioScore、ADMET 等，背后都有同行评审论文支撑，不是只能做 demo 的那种。
为什么这场会好玩
Speedrun 模式。 我们会现场跑一个简化版流程，让你看到候选分子如何从公开论文一路进入筛选漏斗。
造一个还不存在的分子。 分子工厂支持 R-group Linker / 骨架跃迁 全新生成，模型真的会当场“画”出新分子。
像化学家一样读论文。 Structure Extraction 几秒钟就能把 PDF 里的化学结构图变成可编辑、可对接的分子（包括那些画得有点歪的）。
整条 AIDD 流水线一气呵成。 Inno-Docking（CarsiDock KarmaDock RTMScore）→ Inno-ADMET → 分子工厂，环环相扣。
顺便瞄一眼 Agent 层。 Agent 会把研究问题拆成子任务、调用合适的工具、自己跑起来，出错了还能自己救回来。
适合谁来
做工程的、做科研的、读书的、做产品的、对 AI 好奇的——不管你是 PyMOL 老玩家，还是连 RDKit 都没装过。“AI for biology” 既好玩又有点劝退？这场就是给你准备的低门槛第一排。不需要化学背景，带着好奇心就够了。
重点平台
DrugFlow — 完整小分子流水线。模块包括 Inno-Docking、Inno-ADMET、分子工厂、虚拟筛选、结构提取。
BioFlow — 大分子设计平台，覆盖多肽、抗体、蛋白互作等方向，包含 Inno-PepDocking、Inno-ProtDocking、Inno-StructGen 等能力。
Inno-FEP — 高性能自由能计算平台，帮助快速完成 FEP 计算流程。
SciGPT — 生医药研发副驾驶，跑在知识图谱、临床、监管与专利数据之上。
现场会发生什么
开场。 碳硅智慧是谁，以及为什么 AIDD 最近又突然“上头”了。
平台轮廓。 DrugFlow BioFlow / Inno-FEP SciGPT 各自是干嘛的，讲清楚。
现场 speedrun。 从一篇公开论文到候选分子排序：结构提取 → Inno-Docking → Inno-ADMET → 分子工厂，每一步都讲解。
自由问答与交流。 带着你的问题、半成形的想法，以及“这真的能跑吗”的怀疑都来。
主持人
谢昌谕 — 碳硅智慧 CTO，浙大求是工程教授
施慧 — 碳硅智慧联合创始人兼 COO
贾皓文 — 碳硅智慧 AI 工程化负责人
王志远 — 碳硅智慧产品经理
参与说明
这次活动的报名主要是为了收集大家的邮箱，以便后续有更新信息时能及时通知到大家。本活动对所有持有日票或月票的朋友开放。
希望大家带上电脑，方便现场跟着 demo 看窗口、记笔记，或在合适环节同步操作。
全程只使用公开数据。我们不会使用、上传或处理任何私人数据、未公开靶点、未公开结构或未公开化合物。
如果你有自己的研究问题，欢迎带来讨论；但请不要在现场提交任何敏感或未公开材料。
A real AI drug-discovery stack, running live in front of you. We open a public research paper, pull the molecules out of its figures, dock them into a protein, score them, filter for drug-likeness, and ask the model to invent new molecules that do not exist yet. Public data only, no PowerPoint coma — and yes, you can absolutely follow along even if docking still sounds like something boats do.
AI manufacturing changes the shape of early R&D: a clear idea can move much faster toward a testable prototype. Define the target, call the right models, algorithms, and tools, then turn signals from a paper into candidate molecules that can be screened, optimized, and iterated on. This session is not about claiming that a full drug appears in one afternoon — it is about showing how an idea can use AI manufacturing capabilities to reach the next visible, evaluable step.
A friendly note on jargon
This is meant to be a low-stakes front row, not a final exam. We’ll keep a running plain-English gloss on screen for terms like docking, ADMET, scaffold-hopping, and de novo design. If a word makes you twitch, raise your hand — we’ll pause and explain. The fun is in seeing how the pieces snap together, not in already knowing them.
📖Quick glossary
AIDD (AI Drug Discovery) — using AI to design and screen potential drugs, instead of (or alongside) trial-and-error in the wet lab.
Docking (分子对接) — virtually fitting a small molecule into a protein’s binding pocket to predict whether they stick. Like trying keys in a lock, but with physics and ML.
ADMET — Absorption, Distribution, Metabolism, Excretion, Toxicity. In one sentence: if a human actually swallowed this, what would happen?
Virtual screening (虚拟筛选) — searching large candidate libraries in silico to find the few worth investigating further.
Scaffold-hopping De novo design (骨架跃迁 / 从头设计)* — asking the model to either swap out the “skeleton” of a known molecule, or invent a brand-new one from scratch. <*aside>
Who is CarbonSilicon AI?
CarbonSilicon AI (碳硅智慧, founded in Hangzhou in 2021) is one of the more substantive AI-for-drug-discovery teams in China, spun out of Prof. Tingjun Hou’s CADD lab at Zhejiang University. The team has shipped platforms including DrugFlow, BioFlow, Inno-FEP, and SciGPT, with core methods such as FragGPT, Delete, RapiDock, CarsiDock, KarmaDock, RTMScore, BioScore, and ADMET backed by peer-reviewed papers.
Why this will be fun
Speedrun mode. We’ll run a simplified live workflow so you can watch a candidate set move through the screening funnel.
Generate molecules that do not exist yet. The Molecular Factory supports R-group edits, linker design, scaffold-hopping, and full de novo generation — the model literally draws new chemistry in front of you.
Read a paper like a chemist. Structure Extraction turns the molecule pictures inside a PDF into editable, dockable structures in seconds, including the ones drawn a little crooked.
One continuous AIDD pipeline. Inno-Docking (CarsiDock KarmaDock RTMScore) → Inno-ADMET → Molecular Factory, each step feeding the next.
A peek at the agent layer. The agent decomposes a research question into subtasks, calls the right tool for each, reviews its own work, and recovers when a step fails.
Who this is for
Builders, researchers, students, founders, and the AI-curious — whether you live in PyMOL or you have never opened RDKit. If “AI for biology” sounds equally fascinating and intimidating, this is your low-friction front row. No prior chemistry required; curiosity is the only prerequisite.
Featured platforms
DrugFlow — full small-molecule pipeline, including Inno-Docking, Inno-ADMET, Molecular Factory, Virtual Screening, and Structure Extraction.
BioFlow — large-molecule design for peptides, antibodies, and protein–protein interactions, with capabilities such as Inno-PepDocking, Inno-ProtDocking, and Inno-StructGen.
Inno-FEP — high-performance free-energy calculation platform for faster FEP workflows.
SciGPT — biomedical R&D copilot built over knowledge graphs, clinical-trial, regulatory, and patent data.
What will happen live
Opening. Who CarbonSilicon AI is, and why AIDD has gotten suddenly, almost suspiciously good.
Platform tour. A grounded sketch of DrugFlow BioFlow / Inno-FEP SciGPT — what each one is actually for.
Live speedrun. From a public paper to a ranked candidate set: Structure Extraction → Inno-Docking → Inno-ADMET → Molecular Factory, narrated every step of the way.
Open Q&A and hangout. Bring your questions, half-formed ideas, and “wait, is this real?” skepticism.
Hosts
Xie Changyu — CTO, CarbonSilicon AI; Qiushi Engineering Professor, Zhejiang University
Shi Hui — Co-founder & COO, CarbonSilicon AI
Jia Haowen — Head of AI Engineering, CarbonSilicon AI
Wang Zhiyuan — Product Manager, CarbonSilicon AI
Participation notes
Registration is mainly to collect email addresses so we can notify everyone promptly if there are follow-up updates. This event is open to all day-pass and month-pass holders.
Please bring a laptop so you can follow the demo windows, take notes, or participate hands-on where appropriate.
We will use public data only. We will not use, upload, or process any private data, unpublished targets, unpublished structures, or unpublished compounds.
If you have your own research questions, feel free to bring them for discussion — just please do not submit sensitive or unpublished materials during the session.
