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Current Status of Molecular Dynamics Simulations of Membrane Permeabilization by Antimicrobial Peptides and Pore-Forming Proteins: A Review 抗菌肽和成孔蛋白渗透膜的分子动力学模拟研究进展
IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2026-02-09 DOI: 10.1021/acs.jcim.5c02731
Sofia Cresca,Jure Borišek,Alessandra Magistrato,Igor Križaj
Biological membranes are crucial for cellular integrity and function, but their selective permeability can be compromised by various peptides and proteins, such as antimicrobial peptides (AMPs) and pore-forming proteins/toxins (PFPs/PFTs). These molecules induce membrane permeabilization through diverse mechanisms, ranging from the formation of well-defined pores to more nuanced disruptions of the lipid bilayer. Understanding molecular mechanisms underlying membrane integrity disruption is vital for developing novel tools to be applied in medicine, biotechnology, and agriculture. However, due to their transient and dynamic nature, characterizing membrane-disrupting mechanisms is a significant experimental challenge. In silico methods, particularly all-atom and coarse-grained molecular dynamics (MD) simulations, are an indispensable tool to complement and enrich experimental studies, and can offer detailed insights into peptide/protein–membrane interactions, insertion, oligomerization, and pore formation. This review provides a comprehensive overview of the structural and mechanistic diversity of AMPs and PFPs, highlighting representative case studies and discussing key challenges emerging from MD simulations.
生物膜对细胞的完整性和功能至关重要,但其选择性通透性可能受到各种肽和蛋白质的影响,如抗菌肽(amp)和成孔蛋白/毒素(PFPs/PFTs)。这些分子通过不同的机制诱导膜渗透,从形成明确的孔隙到更细微的脂质双分子层破坏。了解膜完整性破坏的分子机制对于开发应用于医学、生物技术和农业的新工具至关重要。然而,由于它们的瞬态和动态性,表征膜破坏机制是一个重大的实验挑战。硅中方法,特别是全原子和粗粒度分子动力学(MD)模拟,是补充和丰富实验研究不可或缺的工具,可以提供对肽/蛋白质膜相互作用、插入、寡聚化和孔形成的详细见解。这篇综述提供了amp和pfp的结构和机制多样性的全面概述,突出了代表性的案例研究,并讨论了MD模拟中出现的关键挑战。
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引用次数: 0
BIPE: Artificial Intelligence-Driven Peptide Bitterness Intensity Prediction Engine. BIPE:人工智能驱动的肽苦味强度预测引擎。
IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2026-02-09 Epub Date: 2026-01-20 DOI: 10.1021/acs.jcim.5c02678
Jianda Yue, Hua Tan, Jiawei Xu, Tingting Li, Zihui Chen, Xie Li, Zhaoyang Tang, Songping Liang, Zhonghua Liu, Ying Wang

Bitterness, alongside sour, sweet, umami, and salty tastes, constitutes one of the five basic tastes and serves as a key dimension in shaping food flavor profiles. Food protein processing readily generates bitter peptides, whose intense bitterness often leads to consumer rejection, yet these peptides frequently carry beneficial bioactivities, necessitating a trade-off between flavor and functionality. This necessitates the quantitative assessment of bitterness intensity in the early stages of product development. However, experimental assays relying on sensory evaluation and electronic tongue instruments are complex, costly, and limited in throughput, constraining the systematic identification of bitter peptides and process optimization. Here, we present BIPE (Bitterness Intensity Prediction Engine), an end-to-end regression model that integrates ESM3 protein language model representations with a multilayer perceptron readout, performing regression of bitterness thresholds in log space to directly assess bitterness intensity from sequence alone. BIPE achieves R2 = 0.9050 under 10-fold cross-validation and R2 = 0.9449 on an independent test set. BIPE accurately reproduces trends in both electronic tongue readouts and human sensory scores, demonstrating a consistent external validity across assays. Besides, BIPE accurately differentiates the bitterness intensities of soybean protein hydrolysates produced by multiple commercial proteases. Finally, systematic scanning of the complete pentapeptide sequence space by BIPE further reveals amino acid compositional patterns associated with bitterness, providing mechanistic insights. By advancing from classification to quantitative regression, BIPE enables rational design of low-bitterness peptides, supports flavor engineering and process optimization, and establishes a reusable baseline for taste modeling.

苦味与酸、甜、鲜、咸一道,构成五种基本味道之一,是塑造食物风味的关键因素。食品蛋白质加工容易产生苦味肽,其强烈的苦味经常导致消费者的排斥,但这些肽通常携带有益的生物活性,需要在风味和功能之间进行权衡。这就需要在产品开发的早期阶段对苦味强度进行定量评估。然而,依赖感官评价和电子舌仪器的实验分析复杂、昂贵且通量有限,限制了苦味肽的系统鉴定和工艺优化。在这里,我们提出了BIPE(苦味强度预测引擎),这是一个端到端回归模型,将ESM3蛋白质语言模型表示与多层感知器读出相结合,在对数空间中执行苦味阈值回归,从而直接从序列中评估苦味强度。BIPE在10倍交叉验证下R2 = 0.9050,在独立测试集上R2 = 0.9449。BIPE准确地再现了电子舌头读数和人类感官评分的趋势,在各种分析中显示出一致的外部有效性。此外,BIPE可以准确区分多种商业蛋白酶生产的大豆蛋白水解物的苦味强度。最后,通过BIPE系统扫描完整的五肽序列空间,进一步揭示了与苦味相关的氨基酸组成模式,提供了机制见解。通过从分类到定量回归,BIPE可以实现低苦味肽的合理设计,支持风味工程和工艺优化,并为味觉建模建立可重复使用的基线。
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引用次数: 0
Selector: A General Python Library for Diverse Subset Selection. 选择器:一个通用的Python库,用于不同的子集选择。
IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2026-02-09 Epub Date: 2026-01-27 DOI: 10.1021/acs.jcim.5c01499
Fanwang Meng, Marco Martínez González, Valerii Chuiko, Alireza Tehrani, Abdul Rahman Al Nabulsi, Abigail Broscius, Hasan Khaleel, Kenneth López-Pérez, Ramón Alain Miranda-Quintana, Paul W Ayers, Farnaz Heidar-Zadeh

Selector is a free, open-source Python library for selecting diverse subsets from any dataset, making it a versatile tool across a wide range of application domains. Selector implements different subset sampling algorithms based on sample distance, similarity, and spatial partitioning along with metrics to quantify subset diversity. It is flexible and integrates seamlessly with popular Python libraries such as Scikit-Learn, demonstrating the interoperability of the implemented algorithms with data analysis workflows. Selector is an operating-system-agnostic, accessible, and easily extensible package designed with modern software development practices, including version control, unit testing, and continuous integration. Interactive quick-start notebooks, which are also web-accessible, provide user-friendly tutorials for all skill levels, showcasing applications in computational chemistry, drug discovery, and chemical library design. Additionally, a web interface has been developed that allows users to easily upload datasets, configure sampling settings, and run subset selection algorithms with no programming required. This work serves as the official release note for the Selector package, offering a technical overview of its features, use cases, and development practices that ensure its quality and maintainability.

Selector是一个免费的开源Python库,用于从任何数据集中选择不同的子集,使其成为跨广泛应用领域的通用工具。Selector基于样本距离、相似性和空间划分以及量化子集多样性的指标实现不同的子集采样算法。它是灵活的,并与流行的Python库(如Scikit-Learn)无缝集成,展示了实现算法与数据分析工作流的互操作性。Selector是一个与操作系统无关的、可访问的、易于扩展的软件包,它是用现代软件开发实践设计的,包括版本控制、单元测试和持续集成。交互式快速启动笔记本,也可通过网络访问,为所有技能水平提供用户友好的教程,展示计算化学、药物发现和化学库设计方面的应用。此外,已经开发了一个web界面,允许用户轻松上传数据集,配置采样设置,并运行子集选择算法,而无需编程。这项工作作为Selector包的官方发布说明,提供了其特性、用例和开发实践的技术概述,以确保其质量和可维护性。
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引用次数: 0
DFDD: A Cloud-Ready Tool for Distance-Guided Fully Dynamic Docking in Host-Guest Complexation. DFDD:用于主客综合体中距离引导全动态对接的云就绪工具。
IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2026-02-07 DOI: 10.1021/acs.jcim.5c02852
Kowit Hengphasatporn, Lian Duan, Ryuhei Harada, Yasuteru Shigeta

Fully dynamic sampling of host-guest inclusion remains difficult because conventional docking and conventional molecular dynamics simulations can sample inclusion, but crystal-like binding is typically stochastic and difficult to reproduce. Here, we introduce DFDD (Distance-Guided Fully Dynamic Docking), a cloud-ready implementation of the LB-PaCS-MD framework designed to capture inclusion processes via unbiased molecular dynamics in explicit solvent. DFDD automates system setup, parameter generation, iterative short-cycle MD sampling, and trajectory analysis within a single workflow that runs on Google Colab without any installation. Progress toward complexation is guided only by the host-guest center-of-mass distance, allowing force-free exploration of insertion pathways and enabling the recovery of both stable and transient binding modes. Using β-cyclodextrin as a representative host, DFDD reproduces experimentally observed inclusion geometries within minutes and reveals intermediate states along the insertion route. Optional coupling with pKaNET-Cloud enables pH-aware, stereochemically consistent ligand protonation states prior to simulation, supporting robust host-guest modeling. This Application Note provides a transparent and accessible platform for efficient host-guest complexation studies. The DFDD framework is publicly available at https://github.com/nyelidl/DFDD.

由于传统的对接和传统的分子动力学模拟可以对包涵体进行采样,因此对主客体包涵体进行完全动态采样仍然很困难,但晶体状结合通常是随机的,难以重现。在这里,我们介绍了DFDD(距离引导全动态对接),这是LB-PaCS-MD框架的云准备实现,旨在通过显式溶剂中的无偏分子动力学捕获包合过程。DFDD自动化系统设置、参数生成、迭代短周期MD采样和轨迹分析,在谷歌Colab上运行,无需任何安装。络合的进展仅受主客体质心距离的引导,允许插入路径的无力探索,并使稳定和瞬态结合模式的恢复成为可能。DFDD以β-环糊精为主体,在几分钟内再现了实验观察到的包裹体几何形状,并揭示了沿插入路径的中间状态。与pKaNET-Cloud的可选耦合使ph感知,立体化学一致的配体质子化状态在模拟之前,支持强大的主客建模。本应用笔记为高效的主客络合研究提供了一个透明和可访问的平台。DFDD框架可以在https://github.com/nyelidl/DFDD上公开获得。
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引用次数: 0
Subtimizer: Computational Workflow for Structure-Guided Design of Potent and Selective Kinase Peptide Substrates. Subtimizer:有效和选择性激酶肽底物结构导向设计的计算工作流。
IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2026-02-07 DOI: 10.1021/acs.jcim.5c02430
Abeeb A Yekeen, Cynthia J Meyer, Melissa McCoy, Bruce Posner, Kenneth D Westover

Kinases are pivotal cell signaling regulators and prominent drug targets. Short peptide substrates are widely used in kinase activity assays essential for investigating kinase biology and drug discovery. However, designing substrates with high activity and specificity remains challenging. Here, we present Subtimizer (substrate optimizer), a streamlined computational pipeline for structure-guided kinase peptide substrate design using AlphaFold-Multimer for structure modeling, ProteinMPNN for sequence design, and AlphaFold2-based interface evaluation. Applied to five kinases, four showed substantially improved activity (up to 350%) with designed peptides. Kinetic analyses revealed >2-fold reductions in the Michaelis constant (Km), indicating improved enzyme-substrate affinity. Designed peptides for MET and ROS1 exhibited reciprocal selectivity, with 4-fold and 11-fold preferences for their intended targets, respectively. This study demonstrates AI-driven structure-guided protein design as an effective approach for developing potent and selective kinase substrates, facilitating assay development for drug discovery and functional investigation of the kinome.

激酶是关键的细胞信号调节因子和突出的药物靶点。短肽底物广泛用于激酶活性测定,对研究激酶生物学和药物发现至关重要。然而,设计具有高活性和特异性的底物仍然具有挑战性。在这里,我们提出了Subtimizer(底物优化器),这是一种用于结构指导的激酶肽底物设计的流线型计算管道,使用alphafold - multitimer进行结构建模,ProteinMPNN进行序列设计,以及基于alphafold2的界面评估。应用于五种激酶,其中四种表现出明显改善活性(高达350%)与设计的肽。动力学分析显示米切里斯常数(Km)降低了100倍,表明酶与底物的亲和力有所提高。为MET和ROS1设计的肽表现出相互选择性,分别对其预期目标具有4倍和11倍的偏好。该研究表明,人工智能驱动的结构引导蛋白设计是开发强效和选择性激酶底物的有效方法,促进了药物发现和激酶组功能研究的检测开发。
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引用次数: 0
Janus-QUBO: A Duality-Aware Framework for Navigating Chemical Space with a Tunable Quantum-Inspired Landscape. Janus-QUBO:用可调量子景观导航化学空间的二元感知框架。
IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2026-02-07 DOI: 10.1021/acs.jcim.5c02820
Dinghao Liu, Wenyu Zhu, Yuanpeng Fu, Xinyi Wang, Yuchen Zhou, Mengzhen Guo, Jun Liao

Discovering novel molecules within the vast chemical space is a central scientific challenge, increasingly delegated to deep generative models. However, the prevailing "black box" paradigm, built on continuous latent spaces, faces a fundamental mismatch between smooth optimization landscapes and inherently discrete molecular structures, often limiting global exploration. To overcome these limitations, we introduce Janus, a framework that recasts molecular design as a transparent, physics-inspired combinatorial optimization problem. At its core, Janus employs a Transformer-based autoencoder with a regularized binary bottleneck to map molecules into a compact discrete latent space. This representation enables the reformulation of molecular generation and optimization as a Quadratic Unconstrained Binary Optimization (QUBO) problem. This approach unlocks synergistic capabilities. For molecular generation, Janus leverages classical and quantum annealers to efficiently traverse the structured energy landscape, enabling the global discovery of diverse chemical scaffolds. Crucially, for molecular optimization, it moves beyond blind search by utilizing quantifiable feature interactions as machine-discovered SAR rules. This allows for rational, interpretable optimization─selectively modifying latent bits to enhance properties. Benchmarking against state-of-the-art methods reveals that this approach achieves superior multiobjective performance while preserving scaffold integrity, avoiding the structural fragmentation common in heuristic baselines. We validate the feasibility of the workflow on a quantum annealer and demonstrate its efficacy in drug-like property optimization. By unifying powerful combinatorial exploration with deep model interpretability, Janus establishes a robust framework for rational, quantum-assisted molecular design.

在广阔的化学空间中发现新分子是一项核心的科学挑战,越来越多地委托给深度生成模型。然而,目前流行的“黑箱”范式,建立在连续的潜在空间上,面临着平滑优化景观和固有离散分子结构之间的根本不匹配,往往限制了全局探索。为了克服这些限制,我们引入了Janus,这是一个将分子设计重塑为透明的,物理启发的组合优化问题的框架。在其核心,Janus采用了一个基于变压器的自动编码器和一个正则化的二进制瓶颈,将分子映射到一个紧凑的离散潜在空间。这种表示使分子生成和优化的重新表述为二次无约束二元优化(QUBO)问题。这种方法释放了协同能力。对于分子生成,Janus利用经典和量子退火炉来有效地遍历结构能量景观,从而在全球范围内发现各种化学支架。至关重要的是,对于分子优化,它超越了盲目搜索,利用可量化的特征相互作用作为机器发现的SAR规则。这允许合理的、可解释的优化──选择性地修改潜在比特以增强性能。对最先进的方法进行基准测试表明,该方法在保持支架完整性的同时实现了优越的多目标性能,避免了启发式基线中常见的结构碎片。我们在量子退火炉上验证了工作流程的可行性,并证明了其在类药物性能优化中的有效性。通过将强大的组合探索与深度模型可解释性统一起来,Janus为理性的量子辅助分子设计建立了一个强大的框架。
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引用次数: 0
Blind Challenges Let Us See the Path Forward for Predictive Models. 盲目的挑战让我们看到预测模型的前进道路。
IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2026-02-06 DOI: 10.1021/acs.jcim.6c00205
John D Chodera, W Patrick Walters, Sriram Kosuri, James S Fraser

The rapid proliferation of AI/ML models in drug discovery heralds an era of extraordinary progress but also raises urgent questions about whether the true predictive performance is as good as advertised. On-target prediction models often benefit from high-resolution structural or atomistic representations that capture the subtleties of binding affinity and pose. In contrast, off-target and ADMET liabilities have typically relied on more implicit representations of molecular interactions. Retrospective benchmarks often provide a misleading picture of how successful these diverse representations are at predicting properties, and the community lacks standardized, prospective comparisons. Blind challenges, such as the OpenADMET × ASAP × PolarisHub Challenge featured in this issue, are crucial for realistically evaluating progress, encouraging iterations, and directing collective efforts toward major accuracy barriers. With ongoing investment in large-scale, open data creation, and community-led challenges, predictive modeling is poised to rapidly transform drug discovery by enabling accurate, multiparameter optimization.

人工智能/机器学习模型在药物发现领域的迅速普及预示着一个非凡进步的时代,但也提出了一个紧迫的问题,即真正的预测性能是否像宣传的那样好。目标预测模型通常受益于高分辨率的结构或原子表示,这些表示捕获了结合亲和力和姿态的微妙之处。相比之下,脱靶和ADMET责任通常依赖于分子相互作用的更隐式表示。回顾性基准通常会误导人们对这些不同表示在预测房产方面的成功程度,而且社区缺乏标准化的、前瞻性的比较。盲挑战,如本期介绍的OpenADMET × ASAP × PolarisHub挑战,对于现实地评估进展、鼓励迭代和指导集体努力解决主要精度障碍至关重要。随着对大规模开放数据创建和社区主导挑战的持续投资,预测建模有望通过实现准确的多参数优化来快速改变药物发现。
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引用次数: 0
xTB-Based High-Throughput Screening of TADF Emitters: 747-Molecule Benchmark. 基于xtb的TADF发射体高通量筛选:747-分子基准。
IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2026-02-06 DOI: 10.1021/acs.jcim.5c02978
Jean-Pierre Tchapet Njafa, Elvira Vanelle Kameni Tcheuffa, Aissatou Maghame Foumkpou, Serge Guy Nana Engo

We validate semiempirical sTDA-xTB and sTD-DFT-xTB methods for high-throughput screening of thermally activated delayed fluorescence (TADF) emitters using 747 experimentally characterized molecules─the largest such benchmark to date. Our framework achieves >99% computational cost reduction versus TD-DFT while maintaining strong internal consistency (Pearson r ≈ 0.82) and reasonable agreement with 312 experimental singlet-triplet gaps (MAE ≈ 0.17 eV). Large-scale analysis statistically validates key design principles: D-A-D architectures outperform other motifs, and optimal torsional angles of 50°-90° maximize TADF efficiency, while PCA confirms a low-dimensional property space. This work establishes xTB methods as cost-effective tools for accelerating TADF discovery.

我们验证了半经验的sTDA-xTB和sTD-DFT-xTB方法,使用747个实验表征的分子来高通量筛选热激活延迟荧光(TADF)发射器,这是迄今为止最大的此类基准。与TD-DFT相比,我们的框架在保持强内部一致性(Pearson r ≈ 0.82)和312个实验单重态-三重态间隙(MAE≈0.17 eV)的合理一致性的同时,计算成本降低了bb0 99%。大规模分析统计验证了关键的设计原则:D-A-D架构优于其他图案,最佳扭转角度为50°-90°,最大限度地提高了TADF效率,而PCA确认了低维属性空间。这项工作确立了xTB方法作为加速TADF发现的具有成本效益的工具。
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引用次数: 0
How Minor Sequence Changes Enable Mechanistic Diversity in MFS Transporters? An Atomic-Level Rationale for Symport Emergence in NarU. 微小的序列改变如何使MFS转运蛋白的机制多样性?NarU中同体出现的原子级理论基础。
IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2026-02-06 DOI: 10.1021/acs.jcim.5c02971
Tanner J Dean, Jiangyan Feng, Diwakar Shukla

Closely related membrane transporters can diverge sharply in their modes of transport despite minimal sequence differences, underscoring how minor structural features can alter the transport function. This divergence is exemplified in nitrate and nitrite transport across bacterial membranes, which supports anaerobic respiration and involves the major facilitator superfamily (MFS) transporters NarK and NarU. NarK operates as a nitrate/nitrite antiporter, whereas NarU's mechanism remains unresolved, with evidence suggesting potential symport activity. Using extensive adaptive molecular dynamics simulations and Markov State Modeling, we mapped NarU's conformational free-energy landscape and assessed how its behavior contrasts with mechanistic principles established for NarK. NarU follows a similar gating pathway but displays pronounced asymmetry favoring the outward-facing state and stabilizes an apo-occluded intermediate inaccessible to antiporters. This state arises from rotation of an arginine gating pair and a hinged glycine substitution that enhances gate flexibility. These sequence-dependent adaptations alter gating energetics and reprogram the scaffold to permit coupled cotransport. Our results show that the presence of a few strategic residue substitutions in the binding pocket and translocation pathway could alter the transport mechanism of transporters with high sequence and structural similarity.

密切相关的膜转运蛋白可以在其运输方式上急剧分化,尽管最小的序列差异,强调微小的结构特征如何改变运输功能。这种差异体现在硝酸盐和亚硝酸盐跨细菌膜的运输中,这支持厌氧呼吸,并涉及主要促进剂超家族(MFS)转运体NarK和NarU。NarK作为硝酸盐/亚硝酸盐的反向转运蛋白起作用,而NarU的机制尚不清楚,有证据表明其可能具有同义转运活性。利用广泛的自适应分子动力学模拟和马尔可夫状态模型,我们绘制了NarU的构象自由能图,并评估了其行为与为NarK建立的机制原理的对比。NarU遵循类似的门控途径,但表现出明显的不对称性,有利于向外的状态,并稳定了反转运蛋白无法进入的载脂蛋白闭塞中间体。这种状态是由精氨酸门控对的旋转和铰链甘氨酸取代引起的,后者增强了门控的灵活性。这些序列依赖的适应性改变了门控能量并重新编程支架以允许耦合共运输。我们的研究结果表明,在结合袋和转运途径中存在少量战略性残基取代可以改变具有高序列和结构相似性的转运蛋白的转运机制。
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引用次数: 0
More Accurate Binding Affinity Prediction Using Protein Homology and Ligand-Based Transfer Learning. 利用蛋白质同源性和基于配体的转移学习进行更准确的结合亲和力预测。
IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL Pub Date : 2026-02-06 DOI: 10.1021/acs.jcim.5c02334
Justin Purnomo, Caitlin Kim, Kunyang Sun, Yingze Wang, Teresa Head-Gordon

Accurate and rapid prediction of protein-ligand binding affinities is critical for drug discovery, particularly when evaluating large chemical libraries or new drug molecules from high-throughput generative models. We present UCBbind, a hybrid framework that combines a similarity-based transfer module with a deep-learning-based prediction module, to efficiently estimate binding affinities of small molecules to target proteins. For each query protein-ligand pair, UCBbind transfers experimental data from highly similar reference pairs when available and applies the prediction module when no sufficiently similar reference exists. We benchmarked UCBbind on multiple datasets, including the CASF-2016 set, the HiQBind dataset post 2020, and the COVID Moonshot database. Our results show that UCBbind achieves state-of-the-art predictive performance, particularly for test entries with high similarity to well-characterized reference proteins and ligands, and can support downstream tasks such as binding site prediction and binder/nonbinder classification.

准确和快速地预测蛋白质与配体的结合亲和力对于药物发现至关重要,特别是在评估大型化学文库或来自高通量生成模型的新药物分子时。我们提出了UCBbind,这是一个混合框架,结合了基于相似性的转移模块和基于深度学习的预测模块,以有效地估计小分子与目标蛋白质的结合亲和力。对于每个查询蛋白配体对,UCBbind在有高度相似的参考对时转移实验数据,在没有足够相似的参考对时应用预测模块。我们在多个数据集上对ucbinding进行了基准测试,包括CASF-2016集、HiQBind 2020年后数据集和COVID Moonshot数据库。我们的研究结果表明,UCBbind实现了最先进的预测性能,特别是对于与特征良好的参考蛋白和配体高度相似的测试条目,并且可以支持下游任务,如结合位点预测和结合剂/非结合剂分类。
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引用次数: 0
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Journal of Chemical Information and Modeling
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