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近3年比较火热的生物科技与人工智能交叉的研究领域，if>5
在过去三年中，生物科技与人工智能之间的交叉研究领域蓬勃发展。这种跨学科合作正在推动生物医学研究和药物发现的许多方面，促进了一系列新技术和方法的出现。以下是一些近年来在这个交叉领域中尤为活跃的研究方向及其进展。
1. 药物发现与开发
人工智能（AI）技术在药物发现中引起了显著关注，尤其是使用机器学习和深度学习技术对大量化合物进行筛选和预测。例如，通过应用虚拟筛选和机器学习模型，以评估小分子药物的潜在活性，已经成功识别出一些临床上具有应用潜力的新候选药物。使用AI的方法不仅加快了药物的发现过程，而且提高了目标药物的成功率。通过深度学习算法在化合物库中快速寻找候选药物，有助于缩短研发周期和降低成本。
2. 疫病监测与应急响应
人工智能在流行病学中的应用日渐增长，特别是在疾病传播模型和治疗策略的预测方面。例如，对于猴痘（Mpox）疫情的研究已强调AI在开发新抗病毒药物中的潜力，利用AI驱动的药物靶标发现和药物重定位策略，加速抗Mpox病毒药物的开发。AI的应用不仅提升了传统流行病学监测的效率，还增强了对疫情的回应能力。
3. 早期诊断与个性化治疗
在老年痴呆症（Alzheimer's disease, AD）等神经退行性疾病的管理中，AI技术也显示出巨大的应用潜力。通过结合神经影像学、新型生物标志物检测和机器学习算法，AI能够精确识别早期认知衰退的迹象，并实时调整治疗方案，以适应患者的个体需求。这样的技术可以提高诊断的准确性，并促进个性化医学的发展，根本性改变了老年痴呆症的管理方式。
4. 代谢综合征与蛋白质组学
代谢综合征的早期识别及干预对降低全球健康负担至关重要。基于机器学习的模型已经被开发来预测个体发展代谢综合征的风险，这一领域的研究已经测量了超过400种蛋白质，并构建了一个可以在10年内预测代谢综合征风险的机器学习模型，这种模型的准确性已经获得了较高的评分。这种科研成果可以为代谢综合征的预防和治疗提供更加科学的依据和方法。
5. 免疫学研究
AI在免疫学领域的应用也在不断扩大。诸如免疫细胞的表征和疾病的解析等多个方面，AI技术被用来分析实验数据和改进实验设计，以便更好地理解免疫反应和相关疾病。 AI的引入帮助研究者更深入地理解复杂的生物系统，推动基础科学研究向临床需求的转变。
6. 合成生物学与AI
在合成生物学中，AI同样发挥着关键作用。合成生物学采取“构建以学习”（build to learn）的方法，而AI的快速进步使生物系统的建模和预测变得更加可行。例如，AI可以在基因组合成和编辑、基因电路工程等领域提高设计的准确性和可预测性，从而开辟新的研究可能性和应用前景。
7. 机器学习驱动的蛋白质设计
关于功能性蛋白质设计的研究领域也得益于AI的发展，机器学习与蛋白质序列及结构数据结合，使得在生物技术和医疗应用中设计和开发新蛋白质的能力显著增强。通过归纳较大的数据集并提出结构与功能之间的关系，新方法帮助科学家克服传统生物学方法的限制，从而加速新型酶、抗体的设计过程。
8. 数据挖掘与文献搜索
在信息获取和处理的方式上，AI也在改变生物医学领域。新的AI驱动的文献搜索工具使得研究者能够更高效地找到相关文献，从而在决策过程中减少信息过载所带来的困扰。由此可以促进基于证据的医学和精准医学的发展，为医学实践提供更为坚实的基础。
结论
综合来看，生物科技和人工智能之间的交叉研究领域正处于快速发展时期，涵盖了从基础科学研究到临床应用的方方面面。 AI的应用为多个研究领域提供了新的视角，显著提高了研究效率和成果质量。然而，尽管取得了许多进展，仍需解决诸多挑战，如数据隐私和伦理等问题。随着技术的不断进步，AI与生物科技的结合有望在未来为医学创新和公共健康带来更大的推动力。
References
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Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.
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Junjie Lu, Hui Xing, Chunhua Wang, et al.
Signal Transduct Target Ther. 2023 Dec 27;8(1):458. doi: 10.1038/s41392-023-01675-2.
In 2022, a global outbreak of Mpox (formerly monkeypox) occurred in various countries across Europe and America and rapidly spread to more than 100 countries and regions. The World Health Organization declared the outbreak to be a public health emergency of international concern due to the rapid spread of the Mpox virus. Consequently, nations intensified their efforts to explore treatment strategies aimed at combating the infection and its dissemination. Nevertheless, the available therapeutic options for Mpox virus infection remain limited. So far, only a few numbers of antiviral compounds have been approved by regulatory authorities. Given the high mutability of the Mpox virus, certain mutant strains have shown resistance to existing pharmaceutical interventions. This highlights the urgent need to develop novel antiviral drugs that can combat both drug resistance and the potential threat of bioterrorism. Currently, there is a lack of comprehensive literature on the pathophysiology and treatment of Mpox. To address this issue, we conducted a review covering the physiological and pathological processes of Mpox infection, summarizing the latest progress of anti-Mpox drugs. Our analysis encompasses approved drugs currently employed in clinical settings, as well as newly identified small-molecule compounds and antibody drugs displaying potential antiviral efficacy against Mpox. Furthermore, we have gained valuable insights from the process of Mpox drug development, including strategies for repurposing drugs, the discovery of drug targets driven by artificial intelligence, and preclinical drug development. The purpose of this review is to provide readers with a comprehensive overview of the current knowledge on Mpox.
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AI is rapidly becoming part of many aspects of daily life, with an impact that reaches all fields of research. We asked investigators to share their thoughts on how AI is changing immunology research, what is necessary to move forward, the potential and the pitfalls, and what will remain unchanged as the field journeys into a new era.
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Sci China Life Sci. 2023 Aug;66(8):1742-1785. doi: 10.1007/s11427-022-2214-2. Epub 2023 Feb 6.
Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.
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Pascal Notin, Nathan Rollins, Yarin Gal, et al.
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Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.
7New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease.PubMed
Rohan Gupta, Smita Kumari, Anusha Senapati, et al.
Ageing Res Rev. 2023 Sep;90:102013. doi: 10.1016/j.arr.2023.102013. Epub 2023 Jul 8.
Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.
8PubMed and beyond: biomedical literature search in the age of artificial intelligence.PubMed
Qiao Jin, Robert Leaman, Zhiyong Lu
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Biomedical research yields vast information, much of which is only accessible through the literature. Consequently, literature search is crucial for healthcare and biomedicine. Recent improvements in artificial intelligence (AI) have expanded functionality beyond keywords, but they might be unfamiliar to clinicians and researchers. In response, we present an overview of over 30 literature search tools tailored to common biomedical use cases, aiming at helping readers efficiently fulfill their information needs. We first discuss recent improvements and continued challenges of the widely used PubMed. Then, we describe AI-based literature search tools catering to five specific information needs: 1. Evidence-based medicine. 2. Precision medicine and genomics. 3. Searching by meaning, including questions. 4. Finding related articles with literature recommendation. 5. Discovering hidden associations through literature mining. Finally, we discuss the impacts of recent developments of large language models such as ChatGPT on biomedical information seeking.
9AI-driven innovations in Alzheimer's disease: Integrating early diagnosis, personalized treatment, and prognostic modelling.PubMed
Mayur Kale, Nitu Wankhede, Rupali Pawar, et al.
Ageing Res Rev. 2024 Nov;101:102497. doi: 10.1016/j.arr.2024.102497. Epub 2024 Sep 16.
Alzheimer's disease (AD) presents a significant challenge in neurodegenerative research and clinical practice due to its complex etiology and progressive nature. The integration of artificial intelligence (AI) into the diagnosis, treatment, and prognostic modelling of AD holds promising potential to transform the landscape of dementia care. This review explores recent advancements in AI applications across various stages of AD management. In early diagnosis, AI-enhanced neuroimaging techniques, including MRI, PET, and CT scans, enable precise detection of AD biomarkers. Machine learning models analyze these images to identify patterns indicative of early cognitive decline. Additionally, AI algorithms are employed to detect genetic and proteomic biomarkers, facilitating early intervention. Cognitive and behavioral assessments have also benefited from AI, with tools that enhance the accuracy of neuropsychological tests and analyze speech and language patterns for early signs of dementia. Personalized treatment strategies have been revolutionized by AI-driven approaches. In drug discovery, virtual screening and drug repurposing, guided by predictive modelling, accelerate the identification of effective treatments. AI also aids in tailoring therapeutic interventions by predicting individual responses to treatments and monitoring patient progress, allowing for dynamic adjustment of care plans. Prognostic modelling, another critical area, utilizes AI to predict disease progression through longitudinal data analysis and risk prediction models. The integration of multi-modal data, combining clinical, genetic, and imaging information, enhances the accuracy of these predictions. Deep learning techniques are particularly effective in fusing diverse data types to uncover new insights into disease mechanisms and progression. Despite these advancements, challenges remain, including ethical considerations, data privacy, and the need for seamless integration of AI tools into clinical workflows. This review underscores the transformative potential of AI in AD management while highlighting areas for future research and development. By leveraging AI, the healthcare community can improve early diagnosis, personalize treatments, and predict disease outcomes more accurately, ultimately enhancing the quality of life for individuals with AD.
10Machine learning-enabled virtual screening indicates the anti-tuberculosis activity of aldoxorubicin and quarfloxin with verification by molecular docking, molecular dynamics simulations, and biological evaluations.PubMed
Si Zheng, Yaowen Gu, Yuzhen Gu, et al.
Brief Bioinform. 2024 Nov 22;26(1). doi: 10.1093/bib/bbae696.
Drug resistance in Mycobacterium tuberculosis (Mtb) is a significant challenge in the control and treatment of tuberculosis, making efforts to combat the spread of this global health burden more difficult. To accelerate anti-tuberculosis drug discovery, repurposing clinically approved or investigational drugs for the treatment of tuberculosis by computational methods has become an attractive strategy. In this study, we developed a virtual screening workflow that combines multiple machine learning and deep learning models, and 11 576 compounds extracted from the DrugBank database were screened against Mtb. Our screening method produced satisfactory predictions on three data-splitting settings, with the top predicted bioactive compounds all known antibacterial or anti-TB drugs. To further identify and evaluate drugs with repurposing potential in TB therapy, 15 screened potential compounds were selected for subsequent computational and experimental evaluations, out of which aldoxorubicin and quarfloxin showed potent inhibition of Mtb strain H37Rv, with minimal inhibitory concentrations of 4.16 and 20.67 μM/mL, respectively. More inspiringly, these two compounds also showed antibacterial activity against multidrug-resistant TB isolates and exhibited strong antimicrobial activity against Mtb. Furthermore, molecular docking, molecular dynamics simulation, and the surface plasmon resonance experiments validated the direct binding of the two compounds to Mtb DNA gyrase. In summary, our effective comprehensive virtual screening workflow successfully repurposed two novel drugs (aldoxorubicin and quarfloxin) as promising anti-Mtb candidates. The verification results provide useful information for the further development and clinical verification of anti-TB drugs.