Unlike traditional molecular dynamics (MD) simulations, which rely on physics-based calculations to simulate atomic interactions, BioEmu-1 uses a data-driven approach to predict how proteins shift between different conformational states.
To achieve this, BioEmu-1 has been trained using a combination of three key data sources:
Static Protein Structures – The model was pretrained on publicly available protein structures, similar to how AlphaFold was trained.這些提供了對蛋白質如何基於氨基酸序列折疊的基本理解。 分子動力學仿真數據 -與Alphafold不同，Alphafold主要依靠靜態結構，BioEMU-1結合了基於軌蹟的分子模擬，這些分子模擬捕獲蛋白質隨時間的移動。這些仿真通常在超級計算機上運行，提供了有關不同時間尺度原子相互作用的高分辨率信息。 實驗穩定性數據 -使用實驗驗證的蛋白質穩定性測量值對AI進行了微調，以確保其預測與現實世界實驗室的結果保持一致。 BioeMu-1預測LAPD蛋白的多種結構（來源:Microsoft）
BioeMu-1背後的核心機制是一個擴散模型，它與現代AI圖像生成工具的功能相似。
的運作生物EMU-1的關鍵輸出是平衡合奏的預測。蛋白質不是剛性的；它們基於溫度，pH或配體結合等環境因素在不同的結構狀態之間波動。
傳統的結構預測模型輸出了單個靜態模型，但是BioEMU-1產生結構的分佈，顯示蛋白質可能會採用的蛋白質範圍。
另一個關鍵方面是自由的能量預測，以估計蛋白質的轉變，以估計蛋白質的轉變。 Microsoft researchers validated BioEmu-1 by comparing its predictions to millisecond-scale molecular dynamics simulations and experimental free energy measurements.
The AI was able to achieve a free energy error margin within 1 kcal/mol, which is comparable to conventional MD simulations but with significantly lower computational costs.
By producing thousands of protein structure samples per hour, BioEmu-1 offers a scalable alternative to molecular dynamics simulations that typically take weeks.
The model’s ability to capture structural transitions makes it particularly useful for drug discovery, where small conformational changes can determine whether a drug binds effectively to a target protein.
Microsoft has not disclosed whether BioEmu-1 will be released as an open-source model, similar to DeepMind’s AlphaFold.
However, given its focus on dynamic protein modeling, it could serve as a complementary tool to existing AI-based structure prediction models, bridging the gap between static structure prediction and time-dependent protein behavior modeling.
The Growing Competition in AI-Powered Biology
BioEmu-1’s release highlights the increasing reliance on artificial intelligence to tackle complex biological questions.
AI models are no longer limited to analyzing existing data—they are now simulating molecular behavior at a scale that was previously impractical.
Microsoft’s entry into AI-driven structural biology follows major advancements from competitors, particularly Google DeepMind, which has pioneered AI applications in protein modeling.
Beyond AlphaFold, Google has also been exploring AI’s potential in scientific discovery.該公司的AI共同科學家證明了在研究人員正式發布之前預測生物學發現的能力
Google將Alphafold定位為蛋白質結構預測的基礎工具，而Microsoft的BioeMu-1方法旨在解決關鍵限制:建模結構過渡，而不僅僅是預測最終構象>製藥公司和生物技術公司越來越多地尋找減少藥物開發時間表的工具。
傳統的分子模擬需要廣泛的計算資源，但是像生物EMU-1這樣的模型可以使大型蛋白質行為預測更易於訪問
生物EMU-1對AI在科學中的未來意味著什麼
BioEMU-1的發射信號信號是AI用於建模生物系統的轉變。 AI現在能夠模擬分子結構的動態表現。
這種能力可能具有深遠的應用，從開發基於酶的材料到研究人員設計藥物候選者U-1被廣泛採用的AlphaFold仍然有待觀察，但其引入證實，AI將結構預測超越了實時分子模擬，這是計算生物學中最複雜的挑戰之一。
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