利用人工智能对抗肝炎：虚拟筛选工具识别休眠抑制剂

开发了一种新的计算方法 HBCVTr，用于使用 SMILES 符号来预测小分子对乙型肝炎病毒 (HBV) 和丙型肝炎病毒 (HCV) 的抗病毒活性。该方法将双向和自回归变压器 (BART) 架构与原子和分数标记技术相结合，以捕获 SMILES 的顺序信息和化学特征。这些模型在分别包含 1941 种和 7454 种 HBV 和 HCV 化合物的精选数据集上进行了训练和评估。与各种机器学习模型和现有方法相比，HBCVTr 模型取得了优越的性能，具有高精度和强大的预测能力。使用 HBCVTr 模型进行虚拟筛选，确定了具有良好药代动力学特性的潜在 HBV 和 HCV 抑制剂。分子对接和分子动力学模拟进一步验证了顶级候选物的结合亲和力和稳定性，提供了对其潜在作用机制的见解。该方法为发现和设计针对 HBV 和 HCV 的新型抗病毒疗法提供了宝贵的工具。

Harnessing the Power of Artificial Intelligence for Drug Discovery: A Novel Virtual Screening Tool for Identifying Potential Inhibitors of Hepatitis B and C Viruses

利用人工智能的力量进行药物发现：一种用于识别乙型和丙型肝炎病毒潜在抑制剂的新型虚拟筛选工具

Abstract

抽象的

Hepatitis B and C viruses (HBV and HCV) pose significant global health challenges, necessitating the development of novel and effective antiviral therapies. To accelerate this process, we present HBCVTr, an innovative virtual screening tool that leverages artificial intelligence (AI) to identify potential inhibitors against these viruses. Our methodology incorporates a bidirectional and auto-regressive transformer (BART) architecture, trained on a vast dataset of SMILES notations and biological activity data. The HBCVTr model demonstrates exceptional predictive performance, surpassing conventional machine learning approaches. Through virtual screening of a library of 10 million compounds, we identified promising candidates with favorable pharmacokinetic properties. Molecular docking and dynamics simulations confirmed the potential of these candidates as inhibitors of HBV and HCV. Our findings underscore the transformative potential of AI in drug discovery, offering a rapid and efficient approach to identify novel therapeutic options for combating viral infections.

乙型和丙型肝炎病毒（HBV 和 HCV）对全球健康构成重大挑战，因此需要开发新型有效的抗病毒疗法。为了加速这一过程，我们推出了 HBCVTr，这是一种创新的虚拟筛选工具，利用人工智能 (AI) 来识别针对这些病毒的潜在抑制剂。我们的方法结合了双向自回归变压器 (BART) 架构，在大量 SMILES 符号和生物活动数据数据集上进行训练。 HBCVTr 模型表现出卓越的预测性能，超越了传统的机器学习方法。通过对 1000 万种化合物的虚拟筛选，我们确定了具有良好药代动力学特性的有前途的候选药物。分子对接和动力学模拟证实了这些候选药物作为 HBV 和 HCV 抑制剂的潜力。我们的研究结果强调了人工智能在药物发现方面的变革潜力，提供了一种快速有效的方法来确定对抗病毒感染的新治疗方案。

Introduction

介绍

Hepatitis B and C viruses are prevalent pathogens that infect millions worldwide, leading to severe liver damage and potential life-threatening complications. Despite the availability of antiviral therapies, the need for new and improved treatments remains urgent, particularly in the face of emerging drug resistance. Traditional drug discovery processes are often time-consuming and expensive, prompting the exploration of alternative approaches.

乙型和丙型肝炎病毒是流行的病原体，感染全世界数百万人，导致严重的肝损伤和潜在的危及生命的并发症。尽管有抗病毒疗法，但仍然迫切需要新的和改进的治疗方法，特别是面对新出现的耐药性。传统的药物发现过程通常既耗时又昂贵，促使人们探索替代方法。

In this study, we present HBCVTr, a groundbreaking virtual screening tool that harnesses the power of AI to expedite the identification of potential HBV and HCV inhibitors. Our methodology utilizes a BART architecture, renowned for its ability to process sequential data, to predict the biological activity of small molecules using SMILES notations. This approach enables the rapid screening of vast chemical libraries, significantly accelerating the drug discovery process.

在这项研究中，我们推出了 HBCVTr，这是一种突破性的虚拟筛查工具，它利用人工智能的力量来加快潜在 HBV 和 HCV 抑制剂的识别。我们的方法采用 BART 架构，该架构以其处理连续数据的能力而闻名，并使用 SMILES 符号来预测小分子的生物活性。这种方法能够快速筛选大量的化学库，显着加速药物发现过程。

Methods

方法

Data Collection and Preprocessing

数据收集和预处理

To train and evaluate our HBCVTr models, we curated antiviral activity assay data for HBV and HCV from the ChEMBL database. The data underwent rigorous filtering to ensure consistency and comparability, resulting in 1941 and 7454 compounds for HBV and HCV, respectively.

为了训练和评估我们的 HBCVTr 模型，我们从 ChEMBL 数据库中收集了 HBV 和 HCV 的抗病毒活性测定数据。数据经过严格过滤以确保一致性和可比性，分别产生 1941 种 HBV 和 7454 种 HCV 化合物。

SMILES notations, representing the molecular structures of the compounds, were preprocessed to remove salts and convert them into canonical SMILES using the RDKit package. These SMILES were subsequently tokenized into atom-wise and fraction-wise tokens, capturing both individual atoms and unique functional groups.

SMILES 符号表示化合物的分子结构，经过预处理以去除盐，并使用 RDKit 包将其转换为规范的 SMILES。这些 SMILES 随后被标记为原子级和分数级标记，捕获单个原子和独特的官能团。

Model Architecture and Training

模型架构和训练

Our HBCVTr model is based on a BART architecture, which is specifically designed for sequential data processing. The model comprises two encoders: one for atom-wise tokens and the other for fraction-wise tokens. These encoders leverage multi-head attention layers to learn the contextual relationships between tokens. The outputs from the encoders are concatenated and passed through fully connected layers, culminating in a regression head that predicts the biological activity of the input SMILES.

我们的 HBCVTr 模型基于 BART 架构，专为顺序数据处理而设计。该模型包含两个编码器：一个用于原子级标记，另一个用于分数级标记。这些编码器利用多头注意力层来学习标记之间的上下文关系。编码器的输出被连接并通过完全连接的层，最终形成一个预测输入 SMILES 生物活性的回归头。

We optimized the model's hyperparameters through a comprehensive grid search, ensuring optimal performance. The model was trained on 72% of the data, while 8% and 20% were allocated for validation and independent testing, respectively.

我们通过全面的网格搜索优化了模型的超参数，确保了最佳性能。该模型使用 72% 的数据进行训练，同时分别分配 8% 和 20% 的数据用于验证和独立测试。

Evaluation Criteria

评价标准

To assess the predictive performance of HBCVTr, we employed a suite of regression evaluation metrics, including mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), R-squared, Pearson's correlation coefficient (PCC), and Spearman rank correlation (Spearman). These metrics evaluate the model's ability to accurately predict biological activity values.

为了评估 HBCVTr 的预测性能，我们采用了一套回归评估指标，包括均方误差 (MSE)、平均绝对误差 (MAE)、均方根误差 (RMSE)、R 平方、皮尔逊相关系数 (PCC) ，和斯皮尔曼等级相关（Spearman）。这些指标评估模型准确预测生物活性值的能力。

Virtual Screening and Pharmacokinetic Properties Prediction

虚拟筛选和药代动力学特性预测

The trained HBCVTr models were utilized for virtual screening of a library of 10 million compounds. The top candidates with the highest predicted biological activity were further evaluated for their pharmacokinetic properties using the SwissADME web tool. This assessment ensured the identification of compounds with desirable drug-like characteristics.

经过训练的 HBCVTr 模型用于对 1000 万种化合物的库进行虚拟筛选。使用 SwissADME 网络工具进一步评估具有最高预测生物活性的最佳候选药物的药代动力学特性。该评估确保鉴定出具有所需药物样特性的化合物。

Molecular Docking and Molecular Dynamics Simulation

分子对接与分子动力学模拟

To investigate the potential binding of the top candidates to target proteins, molecular docking was performed using Autodock Vina. The stability of the protein-ligand complexes was subsequently assessed through molecular dynamics simulations using the Desmond Molecular Dynamics System. These simulations provided insights into the interactions between the candidates and their target proteins.

为了研究顶级候选物与目标蛋白的潜在结合，使用 Autodock Vina 进行了分子对接。随后使用德斯蒙德分子动力学系统通过分子动力学模拟评估了蛋白质-配体复合物的稳定性。这些模拟提供了对候选蛋白与其靶蛋白之间相互作用的深入了解。

Results

结果

Model Performance

模型性能

The HBCVTr models demonstrated remarkable predictive performance on both HBV and HCV datasets. They outperformed conventional machine learning approaches, consistently achieving higher R-squared and PCC values. This superior performance highlights the effectiveness of our BART-based architecture in capturing the complex relationships between molecular structures and biological activity.

HBCVTr 模型在 HBV 和 HCV 数据集上表现出卓越的预测性能。它们的性能优于传统的机器学习方法，持续实现更高的 R 平方和 PCC 值。这种卓越的性能凸显了我们基于 BART 的架构在捕获分子结构和生物活性之间的复杂关系方面的有效性。

Virtual Screening and Pharmacokinetic Properties

虚拟筛选和药代动力学特性

Virtual screening of 10 million compounds identified promising candidates with high predicted biological activity against HBV and HCV. These candidates exhibited favorable pharmacokinetic properties, including low molecular weight, good solubility, and low lipophilicity. Importantly, they demonstrated a low potential for pan-assay interference and structural alerts for potential toxicity.

对 1000 万种化合物进行虚拟筛选，确定了具有高预测抗 HBV 和 HCV 生物活性的有前途的候选化合物。这些候选药物表现出良好的药代动力学特性，包括低分子量、良好的溶解度和低亲脂性。重要的是，他们证明了泛分析干扰和潜在毒性结构警报的可能性较低。

Molecular Docking and Molecular Dynamics Simulation

分子对接与分子动力学模拟

Molecular docking and molecular dynamics simulations revealed the potential binding modes of the top candidates to target proteins. The complexes exhibited stable interactions, indicating the potential for these candidates to inhibit HBV and HCV. Further studies are warranted to validate their antiviral activity and elucidate their mechanisms of action.

分子对接和分子动力学模拟揭示了顶级候选物与靶蛋白的潜在结合模式。这些复合物表现出稳定的相互作用，表明这些候选物具有抑制 HBV 和 HCV 的潜力。需要进一步的研究来验证其抗病毒活性并阐明其作用机制。

Discussion

讨论

The HBCVTr virtual screening tool represents a significant advancement in drug discovery for HBV and HCV. Our AI-powered approach enables the rapid identification of potential inhibitors, significantly accelerating the process of developing new antiviral therapies. The integration of pharmacokinetic properties prediction and molecular docking/dynamics simulations provides valuable insights into the potential drug-like characteristics and binding mechanisms of the candidates.

HBCVTr 虚拟筛查工具代表了 HBV 和 HCV 药物发现的重大进步。我们的人工智能方法能够快速识别潜在的抑制剂，显着加快开发新抗病毒疗法的进程。药代动力学特性预测和分子对接/动力学模拟的整合为候选药物的潜在类药物特征和结合机制提供了有价值的见解。

The HBCVTr tool has broad implications for the field of drug discovery. It can be easily adapted to screen for inhibitors of other viruses, bacteria, and parasites, contributing to the development of personalized and targeted treatments for infectious diseases. Moreover, its underlying AI architecture can be leveraged to predict a wide range of biological activities, facilitating the discovery of novel drugs for various therapeutic applications.

HBCVTr 工具对药物发现领域具有广泛的影响。它可以很容易地用于筛选其他病毒、细菌和寄生虫的抑制剂，有助于开发针对传染病的个性化和有针对性的治疗方法。此外，其底层人工智能架构可用于预测广泛的生物活性，促进各种治疗应用的新药的发现。

Conclusion

结论

In conclusion, the HBCVTr virtual screening tool is a powerful AI-driven platform that transforms the drug discovery process for HBV and HCV. Its exceptional predictive performance, coupled with comprehensive pharmacokinetic and molecular docking/dynamics simulations, enables the rapid identification and characterization of promising antiviral candidates. As we continue to harness the transformative power of AI, we anticipate further advancements in drug discovery, leading to the development of effective and accessible treatments for a myriad of diseases.

总之，HBCVTr 虚拟筛查工具是一个强大的人工智能驱动平台，它改变了 HBV 和 HCV 的药物发现过程。其卓越的预测性能，加上全面的药代动力学和分子对接/动力学模拟，能够快速识别和表征有前途的抗病毒候选药物。随着我们继续利用人工智能的变革力量，我们预计药物发现将取得进一步进展，从而为多种疾病开发出有效且易于使用的治疗方法。