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DP Technology Open-sources Uni-Mol Docking V2: Powerful Docking Engine Enabled by AI

DP Technology Open-sources Uni-Mol Docking V2: Powerful Docking Engine Enabled by AI

DP Technology 開源 Uni-Mol Docking V2:由 AI 支持的強大對接引擎
PR Newswire ·  05/22 16:28

BEIJING, May 22, 2024 /PRNewswire/ -- Docking is crucial in the early stages of drug discovery as it enables the efficient screening of vast libraries of compounds, saving time and resources. In recent years, AI-based methods have emerged as promising alternatives for molecular docking, offering the potential for high accuracy without incurring prohibitive computational costs. Advancing AI for Science, DP Technology has recently open-sourced its powerful AI-based Uni-Mol Docking v2 model [1] and made it available to the science community. Uni-Mol Docking v2 is a powerful docking model released prior to AlphaFold3 [2].

北京,2024年5月22日 /PRNewswire/ — 對接在藥物發現的早期階段至關重要,因爲它可以高效篩選大量化合物庫,節省時間和資源。近年來,基於人工智能的方法已成爲有前途的分子對接替代方案,在不產生高昂的計算成本的情況下提供了實現高精度的潛力。DP Technology 最近開源了其強大的基於人工智能的 Uni-Mol Docking v2 模型,推動科學領域的人工智能發展 [1] 並將其提供給科學界。Uni-Mol Docking v2 是在 AlphaFold3 之前發佈的強大對接模型 [2]

AI assisted Drug Discovery

人工智能輔助藥物發現

Docking is a computational technique used in drug discovery to predict how "well" a small molecule (drug candidate) interacts with a target protein. Docking is crucial in the early stages of drug discovery as it enables the efficient screening of vast libraries of compounds, saving time and resources. Globally, the molecular docking market is a significant segment within the broader drug discovery informatics market, which is projected to grow from USD 3 billion in 2023 to USD 8 billion by 2032, driven by increasing investments in R&D and the adoption of advanced computational tools by pharmaceutical companies [3].

對接是一種用於藥物發現的計算技術,用於預測小分子(候選藥物)與靶蛋白相互作用的 “良好”。對接在藥物發現的早期階段至關重要,因爲它可以高效篩選大量化合物庫,從而節省時間和資源。在全球範圍內,分子對接市場是更廣泛的藥物發現信息學市場中的重要部分。受研發投資增加和製藥公司採用先進計算工具的推動,預計該市場將從2023年的30億美元增長到2032年的80億美元 [3]

Docking techniques have evolved significantly from traditional physics/scoring-based methods to advanced deep learning / AI-based approaches. Initially, docking relied on physical and chemical principles to predict interactions, using scoring functions to evaluate binding affinities based on geometric and energetic considerations. While effective, these methods were computationally intensive and limited by their reliance on predefined rules. The advent of deep learning has transformed docking by enabling models to learn complex molecular representations directly from data. These deep learning models can capture intricate patterns and interactions that were previously hard to model, leading to more accurate and efficient predictions.

對接技術已經從傳統的基於物理/評分的方法向基於深度學習/人工智能的高級方法發生了重大變化。最初,對接依靠物理和化學原理來預測相互作用,使用評分函數根據幾何和能量考慮來評估結合親和力。雖然有效,但這些方法需要大量計算,並且受到對預定義規則的依賴的限制。深度學習的出現使模型能夠直接從數據中學習複雜的分子表徵,從而改變了對接方式。這些深度學習模型可以捕獲以前難以建模的複雜模式和交互作用,從而實現更準確、更有效的預測。

Uni-Mol Docking v2, is based on pre-trained AI model developed by DP Technology. The Uni-Mol modeling series, published at ICLR 2023 [4], described the pretraining of general molecular encoders and showcased their applications in various 2D and 3D downstream tasks such as molecular conformation generation, molecular property prediction and molecular docking. Uni-Mol achieved state-of-the-art results on a wide range of tasks, indicating its competence for generalization, and making it a powerful foundational model for molecular tasks.

Uni-Mol Docking v2 基於 DP Technology 開發的預訓練人工智能模型。Uni-Mol 建模系列,在 ICLR 2023 上發佈 [4],描述了通用分子編碼器的預訓練,並展示了它們在各種二維和三維下游任務中的應用,例如分子構象生成、分子特性預測和分子對接。Uni-Mol 在各種任務上取得了最先進的結果,這表明了其推廣能力,並使其成爲分子任務的強大基礎模型。

In molecular docking, leveraging a pretrained molecular encoder, pretrained pocket encoder and joint pocket-ligand blocks, Uni-Mol Docking v2 achieved superior performance when compared to traditional docking algorithms like Autodock Vina in the CASF-2016 benchmark. Uni-Mol Docking v2 boasts an improved accuracy in predicting these binding poses with over 77% of ligands achieving an RMSD value under 2.0 Å and over 75% passing all quality checks. This marks a substantial leap from the 62% accuracy of our previous version, also eclipsing other known open-sourced methods. We've effectively tackled common challenges like chirality inversions and steric clashes, ensuring our predictions are not just accurate but also chemically viable.

在分子對接中,與 CASF-2016 基準測試中的 Autodock Vina 等傳統對接算法相比,Uni-Mol Docking v2 利用預訓練的分子編碼器、預訓練的口袋編碼器和關節口袋配體塊,實現了卓越的性能。Uni-Mol Docking v2在預測這些結合姿勢方面的準確性有所提高,超過77%的配體的RMSD值低於2.0 A,超過75%的配體通過了所有質量檢查。這標誌着我們先前版本的62%的準確率有了長足的飛躍,也超過了其他已知的開源方法。我們有效地應對了手性倒置和空間碰撞等常見挑戰,確保我們的預測不僅準確,而且在化學上可行。

Recently, the introduction of AlphaFold3 has been widely discussed in the scientific community. In this latest development, AlphaFold3 extends its capabilities to predict protein-ligand docking poses. In the AlphaFold3 paper on Nature, Uni-Mol Docking v2 is featured as a benchmark [2].

最近,科學界廣泛討論了AlphaFold3的引入。在這項最新開發中,AlphaFold3擴展了其預測蛋白質-配體對接姿勢的能力。在 AlphaFold3 關於自然的論文中,Uni-Mol Docking v2 被列爲基準 [2]

Committing to open science, DP Technology is proud to open-source Uni-Mol Docking v2 model, code, and dataset, making them available to the science community. We will continue to work on future iterations of Uni-Mol Docking and beyond as we commit to contributing to the global scientific community.

DP Technology 致力於開放科學,很自豪能夠開源 Uni-Mol Docking v2 模型、代碼和數據集,將其提供給科學界。我們將繼續致力於Uni-Mol Docking的未來迭代及其他版本,同時我們致力於爲全球科學界做出貢獻。

DP Technology is dedicated to advancing the frontiers of science through our unwavering commitment to open science. By openly sharing the research, data, and tools, DP aims to empower the global scientific community, accelerate discovery, and drive meaningful progress across various fields. DP's commitment to transparency and collaboration is rooted in the conviction that the best solutions to the world's challenges emerge when we work together, breaking down barriers to knowledge and fostering a culture of collective advancement.

DP Technology 致力於通過我們對開放科學的堅定承諾,推動科學前沿的發展。通過公開共享研究、數據和工具,DP旨在增強全球科學界的能力,加快發現並推動各個領域取得有意義的進展。DP對透明度和協作的承諾源於這樣的信念,即當我們共同努力,打破知識壁壘,培養集體進步的文化時,應對世界挑戰的最佳解決方案就會出現。

Guided by this value, DP has been contributing actively and significantly to many open science projects in material science, drug discovery etc. They can be found at Recently, DP Technology is proud to join DeepModeling community in launching OpenLAM, towards developing the first Large Atom Model DP has collaborated with global institutions to release DPA-2, which can accurately represent a diverse range of chemical systems and materials, enabling high-quality simulations and predictions with significantly reduced efforts compared to traditional methods[5].

在這一價值觀的指導下,DP 爲材料科學、藥物發現等領域的許多開放科學項目做出了積極而顯著的貢獻。最近,DP Technology 很榮幸能加入 DeepModeling 社區推出 OpenLam,開發第一個大型原子模型 DP 與全球機構合作發佈了 DPA-2,它可以準確代表各種化學系統和材料,與傳統方法相比,能夠以顯著減少的工作量實現高質量的模擬和預測[5]

Access Uni-Mol Docking v2

訪問 Uni-Mol Docking v2

Ready-to-use Webapp:

即用型 Web 應用程序:

Github:

Github

Paper:

論文:

About DP Technology

關於 DP 科技

DP Technology is a global leader in the "AI for Science" research paradigm, where AI learns scientific principles and data, then tackles key challenges in scientific research and industrial R&D.

DP Technology 是 “人工智能促進科學” 研究模式的全球領導者,人工智能學習科學原理和數據,然後應對科學研究和工業研發中的關鍵挑戰。

DP's commitment to interdisciplinary research has led to the creation of the DP "Particle Universe", an array of pre-trained large science models designed to bridge foundational research with practical industrial applications. DP's software suite includes the Bohrium Scientific Research Space, Hermite Computational Drug Design Platform, RiDYMO Dynamics Platform, and Piloteye Battery Design Automation Platform. Together, these platforms form a robust foundation for industrial innovation and an open ecosystem for AI in science, fostering advancements in key areas such as drug discovery, energy, material science, and information technology.

DP對跨學科研究的承諾促成了DP “粒子宇宙” 的創建,這是一系列經過預訓練的大型科學模型,旨在將基礎研究與實際工業應用聯繫起來。DP的軟件套件包括Bohrium科學研究空間、Hermite計算藥物設計平台、RidyMo動力學平台和Piloteye電池設計自動化平台。這些平台共同構成了工業創新的堅實基礎和科學領域人工智能的開放生態系統,促進了藥物發現、能源、材料科學和信息技術等關鍵領域的進步。

Visit DP Technology at

訪問 DP 科技,網址爲

Reference

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參考

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SOURCE DP Technology

來源 DP 科技

譯文內容由第三人軟體翻譯。


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