The open-source EnergyPlus engine, developed by the Department of Energy, performs the underlying calculations that predict how a design will perform. Most tools operate on its text input files, but practitioners typically work one layer up, in the OpenStudio software development kit, which provides a typed model structure and a library of reusable transformation scripts known as Measures. The new server deliberately builds on that SDK layer rather than raw input files, because the typed object structure is more composable and less error-prone to manipulate, and it connects AI agents to the broader ecosystems of OpenStudio-standards, ComStock, and the Building Component Library. Earlier protocol-based servers, such as EnergyPlus-MCP, work only at the input-file layer and defer geometry creation and HVAC loop construction to future work; OpenStudio-MCP tackles the full lifecycle from creation through simulation and evaluation.

The technical heart of the system is the Model Context Protocol, introduced by Anthropic in late 2024, which allows a language model to drive external software by calling validated, schema-typed tools instead of emitting free-form code. OpenStudio-MCP exposes 197 such tools, organized into self-contained skill modules that are discovered automatically at startup. They span model creation, geometry and thermal zoning, construction and schedule assignment, HVAC synthesis, simulation control, results extraction, quality assurance, and even large-scale parametric analysis on OpenStudio-server. A single call to add_baseline_system, for example, wires a complete air- and plant-loop topology corresponding to one of the ten baseline system types defined in ASHRAE Standard 90.1 Appendix G, a task that has historically required careful manual scripting.

Because language models can call operations out of order, select unsuitable systems, or simply invent SDK methods that do not exist, the server wraps the SDK at two levels. Low-level tools expose explicit OpenStudio operations as typed calls, while higher-level tools encode common workflows such as whole-building creation, baseline HVAC assignment, and result extraction. A separate knowledge layer serves curated workflow guides covering object dependencies, ASHRAE system-selection rules, and Measure authoring, which agents retrieve on demand. The server also guards against the finite context window of any language model: instead of dumping a 5,000-space model or a full 8,760-hour annual result into the conversation, it returns compact structured summaries, previews models without loading them, and distills entire simulations into a handful of summary numbers while the model files themselves remain server-side.

Perhaps the most striking capability is automated Measure authoring. Measures are programs, and extending analysis beyond the off-the-shelf library has traditionally required engineers to double as software developers, putting custom analysis out of reach for many architects and engineers who best understand the building. With OpenStudio-MCP, an agent scaffolds a new Measure from a plain-language request, writes its logic, runs its tests, and applies it inside a sandboxed environment that runs child processes as unprivileged users under a fail-closed Landlock filesystem policy, a seccomp filter denying outbound network access, and strict resource limits. The authors stress these are implemented controls rather than security guarantees, since the software has not undergone a formal external penetration test, but targeted adversarial probes using canary listeners and decoy secrets exercised cross-tenant file reads, environment-secret capture, filesystem escapes, and forged transfer requests.

To demonstrate the system end to end, the researchers gave an AI agent a natural-language request specifying a medium office building, its location, a baseline HVAC system, a comfort criterion, and a four-pipe active-chilled-beam retrofit, naming no tools. Working from an empty session with Claude Opus 4.8, the agent created and simulated a 27-zone, three-story, 53,600-square-foot office using the ASHRAE 90.1-2019 template with a variable-air-volume reheat system. The initial model narrowly failed the comfort criterion with 301.7 occupied unmet hours. Digging into sizing reports, the agent found that heating capacity was adequate but a 50 percent reheat-mode airflow cap was constraining morning warm-up after nighttime setback, raised the cap on all 27 terminals, and brought the model to 78.0 unmet hours at a site energy use intensity of 42.4 kBtu per square foot, squarely within the middle half of the observed U.S. office stock from the 2018 Commercial Buildings Energy Consumption Survey.

The agent then authored a Measure that replaced each terminal with a four-pipe beam connected to the existing chilled- and hot-water plants, verified SDK class names, passed its own tests, and validated 27 beam terminals with no errors, all without a human writing or reviewing code. The retrofit maintained comfort but increased site energy by 8.7 percent, driven by a 179 percent surge in fan energy, because the constant-volume beams forfeited the variable-volume air handler’s part-load fan savings and economizer hours. Crucially, the agent reported this adverse result, explained both mechanisms, and proposed remedies including a right-sized dedicated outdoor air system with energy recovery. The session used 98 calls to 36 tools, three annual simulations, and roughly 21 minutes, with no human intervention after the initial request.

Feasibility is not reliability, so the team built a reproducible benchmark of 16 graded tasks across six families, tested with Claude Opus 4.8, Opus 4.6, Sonnet 4.6, and Haiku 4.5 alongside GPT-5.4 and GPT-5.4-mini. Each trial was graded by two deterministic checks without any AI judging: whether the agent called an acceptable tool, and whether the saved model passed physical checks such as assembly R-values, HVAC loop membership, and pinned EnergyPlus outputs. The distinction proved essential, because in 18 of 23 outcome failures an agent replaced a roof assembly with one up to 1.86 square meters kelvin per watt worse while reporting success, an error visible only in the saved artifact, not in the agent’s confident report. Under a common configuration with all tool schemas loaded, GPT-5.4 and Opus 4.8 achieved 100 percent outcome rates, with Opus 4.6 at 95.8 percent, GPT-5.4-mini at 93.8, Sonnet at 91.7, and Haiku at 85.4.

The ablation results carry a practical lesson for anyone deploying agentic AI. Loading every tool schema up front raised the weakest model’s success rate by 12.5 points but inflated costs by 36 to 68 percent for stronger Claude tiers while changing outcomes by at most two tasks, meaning deferred schema discovery saves money for capable models at no accuracy loss. The curated knowledge layer, surprisingly, changed no model’s completion rate by more than 6.3 points. The completion budget also mattered: at a 120-second limit, 17 of Opus 4.8’s 18 failures were timeouts, yet it passed every trial in four of five configurations when given 600 seconds, showing that slow but productive work was being misclassified as failure. An unscaffolded baseline without the server showed agents can handle basic OpenStudio operations through direct scripting, but both tested models failed a task requiring exact counting of warnings in an EnergyPlus error file.

The researchers frame the work as broadening access rather than replacing rigor. Engineers and architects could request, test, and run bespoke retrofit analyses without writing Ruby, while organizations could offer shared modeling capacity to design firms, classrooms, or utility programs by issuing authentication tokens instead of provisioning workstations, turning energy modeling from a per-seat desktop activity into shared infrastructure. Validation and quality-assurance tools, audit records of every tool call, and artifact-based grading make the checking explicit, but the authors are careful to note that engineering judgment is not automated: generated artifacts and conclusions require review by a qualified practitioner before use in real engineering decisions. The code, benchmark harness, and archived trial records are openly available under a BSD-3-Clause-style license, inviting the building science community to put AI-driven modeling to the test.

Subject of Research: An open-source Model Context Protocol server enabling AI agents to perform full-lifecycle building energy modeling with the OpenStudio SDK.

Article Title: OpenStudio-MCP: a model context protocol (MCP) server for AI agent-driven building energy modeling with the OpenStudio SDK

Article References: Ball, B. L., Long, N., Fleming, K., & Goldwasser, D. (2026). OpenStudio-MCP: a model context protocol (MCP) server for AI agent-driven building energy modeling with the OpenStudio SDK. SoftwareX, 36, Article 103020. https://doi.org/10.1016/j.softx.2026.103020

Image Credits: AI Generated

DOI: 10.1016/j.softx.2026.103020

Keywords: OpenStudio-MCP, building energy modeling, Model Context Protocol, large language models, AI agents, EnergyPlus, OpenStudio SDK, HVAC synthesis, Measure authoring, ASHRAE 90.1, agent benchmark, sandboxing

The journey begins in New York, where Founder and Chief Executive Officer Dr. Alex Zhavoronkov will attend the Morgan Stanley 24th Annual Global Healthcare Conference from September 14 to 16. On September 15 at 14:35, Zhavoronkov is scheduled to participate in an in-person fireside chat, engaging global investors and industry leaders on the company’s latest advances in generative AI-driven drug discovery, aging clocks and anti-aging interventions. The Morgan Stanley conference is widely regarded as one of the largest and most influential healthcare investment gatherings in the world, convening thousands of leaders each year, from multinational pharmaceutical companies and biotech innovators to medical device makers, digital health pioneers, hedge funds, long-only investors, consulting firms and regulatory bodies. Its mix of keynote addresses, fireside chats, one-on-one investor meetings and forward-looking roundtables makes it a core venue where international capital identifies healthcare opportunities and where large pharmaceutical companies scout innovative technologies and potential acquisition targets.

For Zhavoronkov, the New York appearance is an opportunity to present Insilico’s progress to the capital markets at a moment of unusual momentum. The company has been steadily expanding its narrative beyond a single headline asset, highlighting an end-to-end autonomous laboratory roadmap that pairs its generative chemistry platforms with laboratory automation designed to compress the timelines of target identification, molecular design and preclinical validation. The aging research dimension of the company’s work, including its well-known deep learning aging clocks that estimate biological age from multimodal data, has long differentiated Insilico from AI drug discovery peers, and executives are expected to weave that longevity science perspective into their dialogue with investors who increasingly view aging biology as a fertile ground for new therapeutics.

From New York the focus shifts to the Middle East. Dr. Alex Aliper, Co-Founder and President of Insilico Medicine, has been invited to the Riyadh Global Medical Biotechnology Summit, known as RGMBS 2026, running September 14 to 16 in the Saudi capital. On September 16, from 09:00 to 12:00 at the Sofitel Riyadh Hotel and Convention Center, Aliper will lead the Insilico team in hosting a hands-on workshop titled Model Context Protocol-Empowered Protein Design: Combining AI Foundation Models and Physics-Based Molecular Modelling. The session is designed to be intensely practical. Participants will gain first-hand experience using the Model Context Protocol, or MCP, to connect AI foundation models, molecular simulation engines and chemical databases. The curriculum covers the complete workflow from protein and ligand structure preparation through physics-based validation, teaching attendees how to score and prioritize drug candidates with AlphaFold, RDKit and OpenMM, how to interpret binding modes, kinetics and free-energy calculation results, and how to run a directed MCP workflow inside Insilico’s Chemistry42 sandbox environment.

The choice of technical material is significant. The Model Context Protocol has emerged as an open standard for connecting large AI models with external tools and data sources, and Insilico’s workshop represents one of the most concrete demonstrations of how that architecture can be applied to protein engineering and small-molecule discovery. By linking generative foundation models to physics-based simulation, the workflow aims to marry the speed and creativity of deep learning with the rigor of molecular mechanics, free-energy perturbation and kinetics analysis that medicinal chemists have long demanded. Aliper, who has spent much of his career at the intersection of AI-driven drug discovery, frontier biomedical science and cross-disciplinary tool integration, will also use the summit to showcase Insilico’s role in building the Chemistry42 generative chemistry platform and the company’s broader autonomous laboratory ecosystem, positioning the workshop as a window into how modern AI-native biopharma companies orchestrate computational and experimental workflows.

RGMBS 2026 itself carries strategic weight. Co-initiated by the Saudi Ministry of Health, the Royal Commission for Riyadh City and leading biomedical authorities, the summit is one of the largest international biotechnology and medical innovation gatherings in the Middle East. It convenes scientists, research and development leaders, clinical experts, regulators and strategic investors spanning biopharmaceuticals, gene and cell therapy, medical devices, digital health and fundamental life sciences. Organizers have centered the program on frontier biotechnology, precision medicine, AI-driven drug discovery, translational medicine, health-tech investment and biomanufacturing, using keynote addresses, themed workshops, closed-door sessions, industry matchmaking and project roadshows to drive cross-regional collaboration. For global biopharma companies, the event is increasingly viewed as a gateway to the Middle East market and to Saudi Vision 2030, the kingdom’s national strategy that places biomedical capability among its economic diversification priorities.

The third stop brings Insilico to Shanghai, where Co-CEO and Chief Scientific Officer Dr. Feng Ren will attend Bio-Shanghai Week 2026, an event anchored by Zhangjiang Drug Valley, a national-level biopharmaceutical industry hub. On September 17 at 15:00, during the opening ceremony’s AI-Driven Innovation session, Ren will engage in an in-depth dialogue with Professor Michael Levitt, the 2013 Nobel Laureate in Chemistry and Stanford University structural biology professor, on the theme of AI-driven global innovation in therapeutic target discovery and treatment technologies. The conversation is expected to traverse three dimensions: foundational science breakthroughs, industrial translation pathways and global strategic coordination, examining how artificial intelligence is systematically reshaping the full chain from target discovery through molecular design to clinical development. Ren, regarded as one of the leading scientists driving AI-enabled drug research and clinical translation in China, will share Insilico’s generative AI platform, its pipeline progress and the company’s global footprint, creating what organizers describe as a high-level exchange between a leading Chinese AI-driven pharmaceutical company and a top global scientist.

Bio-Shanghai Week ranks among the largest and most internationally influential biopharmaceutical industry events in Shanghai, drawing leading scientists, clinical experts, multinational pharmaceutical and biotech companies, innovative drug and device developers, investors, regulators and industry service platforms. Its agenda spans AI-driven innovation, gene and cell therapy, antibodies and antibody-drug conjugates, rare diseases, neuroscience, global market access, clinical translation and the broader industry ecosystem. The event serves as a vital window into the frontier of China’s biopharmaceutical industry and the wider Yangtze River Delta innovation ecosystem, a region that has become one of the world’s densest concentrations of drug discovery talent and capital.

The final leg of the tour takes Zhavoronkov to Boston on September 18 for the Harvard IvyTech Discussion, co-initiated by Harvard University and other Ivy League academic institutions. From 10:50 to 12:00, he will deliver a keynote address in a forum titled A Geo-Economic Shift: China’s Rise as an Innovation Powerhouse in Biotech. Sharing the stage with leading scientists, industry strategists and policy researchers from North America’s top institutions, Zhavoronkov will discuss the leapfrog transformation of China’s biopharmaceutical industry from generic manufacturing to first-in-class innovation, and the corresponding evolution of the global biopharma value chain and capital landscape. He is also expected to present Insilico’s strategic blueprint as what the company calls a bridge enterprise connecting Eastern and Western innovation ecosystems, spanning Chinese foundational research, the company’s AI platform technology, its global research and development pipeline, and its international capital and industry partnerships. The IvyTech platform, which focuses on frontier technology, industrial transformation and geo-economic topics, brings together scientists, technology executives, entrepreneurs, policymakers and institutional investors for dialogue across biomedical innovation, artificial intelligence, advanced manufacturing, the energy transition and cross-border innovation ecosystems.

The speaking tour arrives at a pivotal financial and scientific moment for Insilico Medicine. The company recently reported total revenue of approximately 106 million US dollars in the first half of 2026, a 287 percent year-over-year increase, and achieved its first profitable half-year since listing, with adjusted net profit exceeding 51 million dollars. The milestone was driven by a series of out-licensing, co-development and research collaborations with global partners including Eli Lilly, Servier, Takeda, SK Biopharmaceuticals, Qilu Pharmaceutical, Hygtia Therapeutics, CMS and Tenacia. As of the latest practicable date, the total contract value of transactions announced by the company in 2026 reached approximately 7.3 billion dollars, pushing the cumulative contract value of its major collaborations since 2021 to roughly 11 billion dollars. On the research front, Insilico nominated nine development candidates within the first nine months of 2026 as of late August, a company record for annual pipeline productivity, and achieved eight clinical milestones across its proprietary and co-developed programs. Leading that progress is rentosertib, also known as ISM001-055, the world’s first drug candidate discovered and developed using generative AI, which has advanced into a Phase III trial evaluating treatment for idiopathic pulmonary fibrosis, a progressive and often fatal scarring lung disease with few therapeutic options. Listed on the Main Board of the Hong Kong Stock Exchange on December 30, 2025 under the stock code 03696.HK, Insilico continues to apply its Pharma.AI platform to fibrosis, oncology, immunology, pain, obesity and metabolic disorders, while extending the technology into advanced materials, agriculture, nutritional products and veterinary medicine. The four-city executive tour, spanning capital markets in New York, biotechnology diplomacy in Riyadh, scientific dialogue in Shanghai and academic strategy in Boston, functions as a compressed portrait of the company’s thesis: that generative AI, rigorous physics-based validation and global collaboration can deliver better drugs faster, and that the companies able to bridge the world’s major innovation hubs will define the next decade of biopharmaceutical progress.

Subject of Research: Insilico Medicine executive participation in four global healthcare and AI summits showcasing generative AI drug discovery

Article Title: Across four global innovation hubs: Insilico Medicine executive team to speak at premier healthcare and AI Summits

Article References: Across four global innovation hubs: Insilico Medicine executive team to speak at premier healthcare and AI Summits. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Insilico Medicine, generative AI, drug discovery, rentosertib, AlphaFold, Model Context Protocol, aging clocks, biotechnology, idiopathic pulmonary fibrosis, AI summits, autonomous laboratory, biopharmaceutical innovation