人工智能在制药行业的应用进展

随着人工智能（AI）技术的高速发展,AI在制药行业展现出广泛的应用潜力。总结AI在药物研发、生产、营销方面的应用情况,分析AI应用在药物靶点发现、分子设计与筛选、药物相互作用预测、生产质量监测、药品鉴别、患者画像、药物推荐等领域的效果与不足。提出应用AI技术赋能制药行业,将在缩短药品研发周期、降低药品生产与供应链成本、降低药物使用安全风险、提高药品推广效率等方面产生巨大价值,为提高药物创新水平以及健康事业带来深远的影响。     

大数据和人工智能技术用于计算机辅助药物设计的研究进展

大数据和人工智能（AI）技术不仅可以对海量的生物医学数据进行准确和综合地分析,而且可以帮助构建药物设计领域的预测模型。随着算法和统计方法的发展,大数据和AI技术已应用于计算机辅助药物设计（CADD）。CADD可被用于有效克服药物设计领域的困难,从而高效地设计和开发新药。介绍了药物设计和发现过程中数据预处理和建模步骤、药物设计和发现过程中基于AI的建模方法、大数据和AI技术在CADD中的最新应用,以期为我国药物设计和开发提供参考。     

人工智能在药物再利用治疗新型冠状病毒感染研究中的应用及问题分析

近年来,为应对新型冠状病毒感染（COVID-19）的暴发,药物再利用成为寻找COVID-19治疗药物的有效策略。人工智能（AI）能够快速计算筛选大量药物数据库以获取候选药物,在药物再利用领域得到广泛应用。根据算法设计原理,AI应用于药物再利用治疗COVID-19研究的方法可分为3类：(1)基于网络的模型,强调药物与疾病间关联性的识别,以揭示药物的潜在治疗机制;(2)基于结构的方法,通过药物和靶点间结构相互作用的分析实现精确筛选;(3)机器学习/深度学习方法,利用复杂非线性数据的多维度处理进行候选药物预测。尽管AI在药物再利用中发挥了重要作用,但数据的质量和数量对AI计算结果影响显著;实验研究无法全面模拟人体的复杂生理环境,从而可能限制候选药物在非临床研究阶段的精确验证;而且针对原始适应证的药物优化可能影响候选药物在治疗COVID-19中的有效性,治疗时机和个体差异也可能对临床效果产生影响。本文对AI在药物再利用治疗COVID-19研究中的应用和挑战进行综述,以期为将AI技术进一步应用于治疗COVID-19药物研究提供参考。     

机器学习在药物筛选中的研究进展

药物筛选是制药行业中最重要的环节之一。传统的药物随机筛选或定向筛选方法效率低、成本高、周期长。随着机器学习的出现及快速发展，药物筛选的效率得到了提升，大大减少了药物研发的时间，降低了筛选成本。该文就机器学习的基本概念、常用数据集，以及常用算法进行介绍；就机器学习在各类疾病药物筛选中的应用展开综述，为开展新药研发提供参考，并促进学科的交叉融合。     

发现毒理学和病理学在设计更安全的先导化合物中的应用

毒性是药物在开发过程中被淘汰的首要原因。大多数安全性相关的药物淘汰发生在临床前,提示在药物开发过程中早期预测药物的毒性有利于设计出上市概率更高的候选化合物。本文介绍了如何通过早期应用新的分子技术,以及传统重复给药的毒理学研究开展临床前安全性评价,确定具有预测价值的毒性事件。早期识别剂量限制性毒性既有助于化学家和毒理学家了解毒性特点、确定结构与毒性之间的关系,也利于最大限度地减少或避免安全性问题。     

机器学习在治疗药物监测与个体化用药中的应用

机器学习由于其强大的数据分析与预测能力,在医学领域的研究与应用不断深入。近年来,越来越多的研究将其应用于免疫抑制剂、抗感染药物、抗癫痫药物等的治疗药物监测与个体化用药中。相较于传统的群体药动学建模方法,基于机器学习构建的模型能更精准地预测血药浓度和给药剂量,提高临床精准用药水平,减少不良反应的发生。基于此,本文就机器学习在治疗药物监测与个体化用药中的应用予以综述,为临床精准用药提供理论依据和技术支撑。     

模型引导的药物研发在中药中的应用优势和进展

模型引导的药物研发（MIDD）是一种用于构建、验证和利用疾病模型、药物暴露反应模型和药学模型来促进药物开发的数学和统计方法。随着制药技术的发展，MIDD在中药领域应用广泛，具有较高的实用价值。本文总结了国内外相关文献，发现MIDD具有提高中药研发效率、快速明确中药的适用人群、预测药物的交互作用、优化给药剂量等优势，且在中药药效成分、组方定量设计、剂型和制剂工艺、中试放大、质量与安全性、监管决策与评价等研究方面均有应用。     

药物筛选技术的研究进展

药物筛选是药物发现的主要手段之一，近年来在药物筛选的技术方面发展迅速。本文综述了药物筛选中药靶的发现、化合物样品的选择、超高通量药物筛选技术、高信息量筛选、药代动力学和毒性的高通量筛选、数据分析等方面的进展。     

虚拟筛选辅助新药发现方法研究进展

在新药发现过程中, 虚拟筛选的应用可以富集活性化合物, 降低筛选成本, 提高药物筛选的可行性, 因此已成为新药发现的重要方法。虚拟筛选与生物活性筛选的结合, 可以优势互补, 有效地促进新药的发现。本文介绍了非类药化合物排除、假阳性化合物排除、药效团搜索、分子对接计算以及分子相似性分析等几种方法在药物发现中的应用及其发展趋势, 以期更好地应用虚拟筛选方法, 促进新药的快速发现。     

Domainex扩大与ICR的合作

英国药物发现公司Domainex扩大与从伦敦大学学院分出的癌研究院（ICR）的合作。后者正利用它的LeadBuilder病毒筛选技术设计和选择化学化合物以帮助该研究院开发乳腺癌的新治疗。它也将应用它的药物化学经验帮助ICR将候选药物推进进入临床试验。LeadBuilder是一种筛选方法，可使Domainex公司客户的成本降低到仅为高通量筛选法总花费的10％～20％。     

Explainability and white box in drug discovery

Recently, artificial intelligence (AI) techniques have been increasingly used to overcome the challenges in drug discovery. Although traditional AI techniques generally have high accuracy rates, there may be difficulties in explaining the decision process and patterns. This can create difficulties in understanding and making sense of the outputs of algorithms used in drug discovery. Therefore, using explainable AI (XAI) techniques, the causes and consequences of the decision process are better understood. This can help further improve the drug discovery process and make the right decisions. To address this issue, Explainable Artificial Intelligence (XAI) emerged as a process and method that securely captures the results and outputs of machine learning (ML) and deep learning (DL) algorithms. Using techniques such as SHAP (SHApley Additive ExPlanations) and LIME (Locally Interpretable Model-Independent Explanations) has made the drug targeting phase clearer and more understandable. XAI methods are expected to reduce time and cost in future computational drug discovery studies. This review provides a comprehensive overview of XAI-based drug discovery and development prediction. XAI mechanisms to increase confidence in AI and modeling methods. The limitations and future directions of XAI in drug discovery are also discussed.     

High-throughput drug screens for amyotrophic lateral sclerosis drug discovery

ABSTRACT

Introduction: Amyotrophic lateral sclerosis (ALS) is a rapid adult-onset neurodegenerative disorder characterised by the progressive loss of upper and lower motor neurons. Current treatment options are limited for ALS, with very modest effects on survival. Therefore, there is a unmet need for novel therapeutics to treat ALS.

Areas covered: This review highlights the many diverse high-throughput screening platforms that have been implemented in ALS drug discovery. The authors discuss cell free assays including in silico and protein interaction models. The review also covers classical in vitro cell studies and new cell technologies, such as patient derived cell lines. Finally, the review looks at novel in vivo models and their use in high-throughput ALS drug discovery

Expert opinion: Greater use of patient-derived in vitro cell models and development of better animal models of ALS will improve translation of lead compounds into clinic. Furthermore, AI technology is being developed to digest and interpret obtained data and to make ‘hidden knowledge’ usable to researchers. As a result, AI will improve target selection for high-throughput drug screening (HTDS) and aid lead compound optimisation. Furthermore, with greater genetic characterisation of ALS patients recruited to clinical trials, AI may help identify responsive genetic subtypes of patients from clinical trials.     

Artificial intelligence in virtual screening: Models versus experiments

A typical drug discovery project involves identifying active compounds with significant binding potential for selected disease-specific targets. Experimental high-throughput screening (HTS) is a traditional approach to drug discovery, but is expensive and time-consuming when dealing with huge chemical libraries with billions of compounds. The search space can be narrowed down with the use of reliable computational screening approaches. In this review, we focus on various machine-learning (ML) and deep-learning (DL)-based scoring functions developed for solving classification and ranking problems in drug discovery. We highlight studies in which ML and DL models were successfully deployed to identify lead compounds for which the experimental validations are available from bioassay studies.     

Using artificial intelligence methods to speed up drug discovery

ABSTRACT

Introduction: Drug discovery is the process through which potential new compounds are identified by means of biology, chemistry, and pharmacology. Due to the high complexity of genomic data, AI techniques are increasingly needed to help reduce this and aid the adoption of optimal decisions. Phenotypic prediction is of particular use to drug discovery and precision medicine where sets of genes that predict a given phenotype are determined. Phenotypic prediction is an undetermined problem given that the number of monitored genetic probes markedly exceeds the number of collected samples (from patients). This imbalance creates ambiguity in the characterization of the biological pathways that are responsible for disease development.

Areas covered: In this paper, the authors present AI methodologies that perform a robust deep sampling of altered genetic pathways to locate new therapeutic targets, assist in drug repurposing and speed up and optimize the drug selection process.

Expert opinion: AI is a potential solution to a number of drug discovery problems, though one should, bear in mind that the quality of data predicts the overall quality of the prediction, as in any modeling task in data science. The use of transparent methodologies is crucial, particularly in drug repositioning/repurposing in rare diseases.     

Machine-learning methods for ligand–protein molecular docking

Artificial intelligence (AI) is often presented as a new Industrial Revolution. Many domains use AI, including molecular simulation for drug discovery. In this review, we provide an overview of ligand–protein molecular docking and how machine learning (ML), especially deep learning (DL), a subset of ML, is transforming the field by tackling the associated challenges.     

Targeting in silico GPCR conformations with ultra-large library screening for hit discovery

The use of deep machine learning (ML) in protein structure prediction has made it possible to easily access a large number of annotated conformations that can potentially compensate for missing experimental structures in structure-based drug discovery (SBDD). However, it is still unclear whether the accuracy of these predicted conformations is sufficient for screening chemical compounds that will effectively interact with a protein target for pharmacological purposes. In this opinion article, we examine the potential benefits and limitations of using state-annotated conformations for ultra-large library screening (ULLS) in light of the growing size of ultra-large libraries (ULLs). We believe that targeting different conformational states of common drug targets like G-protein-coupled receptors (GPCRs), which can regulate human physiology by switching between different conformations, can offer multiple advantages.     

高内涵分析在新药发现毒理学中的应用进展

在新药发现早期开展发现毒理学研究是提高新药研发效率的重要策略之一。高内涵分析(HCA)是基于高效新药筛选需求发展起来的一项新技术,其主要特点是基于活细胞、多参数、实时、高通量,能够实现化合物多种生物活性、毒性的早期、快速地检测,为发现毒理学研究提供了高效的技术手段。目前,HCA已用于多种靶器官细胞毒性、遗传毒性、神经毒性、血管毒性、生殖毒性等检测以及毒理学分子机制的研究,本文就HCA在新药发现毒理学方面的应用进展进行综述。     

Predicting liposome formulations by the integrated machine learning and molecular modeling approaches

Liposome is one of the most widely used carriers for drug delivery because of the great biocompatibility and biodegradability. Due to the complex formulation components and preparation process, formulation screening mostly relies on trial-and-error process with low efficiency. Here liposome formulation prediction models have been built by machine learning (ML) approaches. The important parameters of liposomes, including size, polydispersity index (PDI), zeta potential and encapsulation, are predicted individually by optimal ML algorithm, while the formulation features are also ranked to provide important guidance for formulation design. The analysis of key parameter reveals that drug molecules with logS [-3, -6], molecular complexity [500, 1000] and XLogP3 (≥2) are priority for preparing liposome with higher encapsulation. In addition, naproxen (NAP) and palmatine HCl (PAL) represented the insoluble and water-soluble molecules are prepared as liposome formulations to validate prediction ability. The consistency between predicted and experimental value verifies the satisfied accuracy of ML models. As the drug properties are critical for liposome particles, the molecular interactions and dynamics of NAP and PAL liposome are further investigated by coarse-grained molecular dynamics simulations. The modeling structure reveals that NAP molecules could distribute into lipid layer, while most PAL molecules aggregate in the inner aqueous phase of liposome. The completely different physical state of NAP and PAL confirms the importance of drug properties for liposome formulations. In summary, the general prediction models are built to predict liposome formulations, and the impacts of key factors are analyzed by combing ML with molecular modeling. The availability and rationality of these intelligent prediction systems have been proved in this study, which could be applied for liposome formulation development in the future.     

Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era

Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional machine learning (ML) algorithms, DL methods still have a long way to go to achieve recognition in small molecular drug discovery and development. And there is still lots of work to do for the popularization and application of DL for research purpose, e.g., for small molecule drug research and development. In this review, we mainly discussed several most powerful and mainstream architectures, including the convolutional neural network (CNN), recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning; summarized most of the representative applications in small molecule drug design; and briefly introduced how DL methods were used in those applications. The discussion for the pros and cons of DL methods as well as the main challenges we need to tackle were also emphasized.     

Machine Learning guided early drug discovery of small molecules

Machine learning (ML) approaches have been widely adopted within the early stages of the drug discovery process, particularly within the context of small-molecule drug candidates. Despite this, the use of ML is still limited in the pharmacokinetic/pharmacodynamic (PK/PD) application space. Here, we describe recent progress and the role of ML used in preclinical drug discovery. We summarize the advances and current strategies used to predict ADME (absorption, distribution, metabolism and, excretion) properties of small molecules based on their structures, and predict structures based on the desired properties for molecular screening and optimization. Finally, we discuss the use of ML to predict PK to rank the ability of drug candidates to achieve appropriate exposures and hence provide important insights into safety and efficacy.