Application of Artificial Intelligence in Drug-Drug Interactions Prediction: A Review

Traumatic brain injury: Bridging pathophysiological insights and precision treatment strategies

Blood-brain barrier disruption and the neuroinflammatory response are significant pathological features that critically influence disease progression and treatment outcomes. This review systematically analyzes the current understanding of the bidirectional relationship between blood-brain barrier disruption and neuroinflammation in traumatic brain injury, along with emerging combination therapeutic strategies. Literature review indicates that blood-brain barrier disruption and neuroinflammatory responses are key pathological features following traumatic brain injury. In the acute phase after traumatic brain injury, the pathological characteristics include primary blood-brain barrier disruption and the activation of inflammatory cascades. In the subacute phase, the pathological features are characterized by repair mechanisms and inflammatory modulation. In the chronic phase, the pathological features show persistent low-grade inflammation and incomplete recovery of the blood-brain barrier. Various physiological changes, such as structural alterations of the blood-brain barrier, inflammatory cascades, and extracellular matrix remodeling, interact with each other and are influenced by genetic, age, sex, and environmental factors. The dynamic balance between blood-brain barrier permeability and neuroinflammation is regulated by hormones, particularly sex hormones and stress-related hormones. Additionally, the role of gastrointestinal hormones is receiving increasing attention. Current treatment strategies for traumatic brain injury include various methods such as conventional drug combinations, multimodality neuromonitoring, hyperbaric oxygen therapy, and non-invasive brain stimulation. Artificial intelligence also shows potential in treatment decision-making and personalized therapy. Emerging sequential combination strategies and precision medicine approaches can help improve treatment outcomes; however, challenges remain, such as inadequate research on the mechanisms of the chronic phase traumatic brain injury and difficulties with technology integration. Future research on traumatic brain injury should focus on personalized treatment strategies, the standardization of techniques, cost-effectiveness evaluations, and addressing the needs of patients with comorbidities. A multidisciplinary approach should be used to enhance treatment and improve patient outcomes.

Stimuli-responsive hydrogel with spatiotemporal co-delivery of FGF21 and H₂S for synergistic diabetic wound repair

Chronic diabetic wounds pose significant clinical challenges due to persistent inflammation, impaired angiogenesis, and disrupted cellular homeostasis. To address these multifactorial barriers, we engineered an injectable, biodegradable, and biocompatible methylated silk fibroin (SilMA) hydrogel system co-loaded with cobalt sulfide (CoS) and fibroblast growth factor 21 (FGF21), designed for on-demand therapeutic release. In the acidic microenvironment characteristic of the inflammatory phase of diabetic wounds, the hydrogel rapidly releases hydrogen sulfide (H₂S) and Co2+ ions, mitigating inflammation and exerting antibacterial effects. Subsequently, during the proliferative and remodeling phases, sustained release of FGF21 promotes cellular proliferation, angiogenesis, and enzymatic homeostasis, thereby accelerating wound healing. Mechanistic studies reveal that the hydrogel facilitates M2 macrophage polarization and activates the JAK/STAT signaling pathway, leading to upregulation of vascular endothelial growth factor (VEGF). Additionally, it enhances antioxidant enzyme activities (superoxide dismutase, catalase, glutathione) while suppressing pro-oxidant enzymes (NADPH oxidase, lipoxygenase, cyclooxygenase). In vivo studies using a diabetic mouse model demonstrate that this dual-functional hydrogel significantly improves wound closure rates and tissue regeneration. These findings suggest that the SilMA-FGF21/CoS hydrogel represents a promising therapeutic strategy for the management of diabetic wounds.

Adaptive Multi-Kernel Graph Neural Network for Drug-Drug Interaction Prediction

 Combination therapy, which synergistically enhances treatment efficacy and inhibits disease progression through the combined effects of multiple drugs, has emerged as a mainstream approach for treating complex diseases and alleviating symptoms. However, drug-drug interactions (DDIs) can sometimes lead to adverse reactions, potentially endangering lives. Therefore, developing efficient and accurate DDI prediction methods is crucial for elucidating drug mechanisms and preventing side effects. Current prediction methods often focus solely on the presence of interactions between drugs when constructing DDI graphs, neglecting the specific types of DDIs. This oversight can result in a decline in predictive performance. To address this issue, we propose an Adaptive Multi-Kernel Graph Neural Network (AMKGNN) for DDI prediction. AMKGNN differentiates DDIs into increase-type and decrease-type interactions, constructing separate increased DDI and decreased DDI graphs as convolutional kernels. AMKGNN employs a graph kernel learning mechanism that adaptively determines the optimal threshold between high-frequency and low-frequency signals in the network to capture node embeddings. Initially, AMKGNN learns drug embedding representations based on these two graph convolutional kernels and various drug features. These representations are then concatenated and input into a deep neural network to predict potential DDIs. The results show that our model achieved AUC and AUPR values above 90% across three sub-tasks on two datasets, significantly outperforming the other five comparison models. Furthermore, ablation experiments and case studies validate the superiority of AMKGNN.

Drug-drug interaction prediction of traditional Chinese medicine based on graph attention networks

Predicting drug-drug interactions (DDI) is crucial for preventing adverse reactions in patients and plays a vital role in drug design and development. However, traditional Chinese medicine (TCM) formulations, typically composed of multiple herbal ingredients with diverse bioactive compounds, present a unique challenge in comprehensively assessing potential adverse interactions among their components. To address this challenge, we propose a novel Dual Graph Attention Network (DGAT) designed to predict TCM drug-drug interactions (TCMDDI) by extracting key structural features of active molecules within the herbal ingredients. Our approach leverages graph-based representations of chemical molecules and employs attention mechanism to extract deep structural features, enabling the effective prediction of TCMDDI by capturing spatial structural relationships among different compounds. Furthermore, we construct a comprehensive dataset encompassing three different categories of herbal ingredients, informed by traditional TCM principles. Experimental results reveal that the proposed DGAT method significantly outperforms currently advanced deep learning techniques, including Graph Convolutional Networks, Weave, and Message Passing Neural Networks. Compared to traditional rule-based two-dimensional molecular descriptors, DGAT more effectively captures the spatial structural information of molecules. Notably, DGAT exhibits robust performance and strong generalizability on unseen samples, providing valuable insights for future research on TCMDDI prediction and advancing the integration of artificial intelligence in TCM studies.

SMVSNN: An Intelligent Framework for Anticancer Drug-Drug Interaction Prediction Utilizing Spiking Multi-view Siamese Neural Networks

The study of synergistic drug combinations is vital in cancer treatment, enhancing efficacy, reducing resistance, and minimizing side effects through complementary drug actions. Drug-drug interaction (DDI) analysis offers essential theoretical support, and with the rise of data science, intelligent algorithms are increasingly replacing traditional in vitro screening for predicting potential DDIs. Considering the limitations of previous computational methods, such as the application of a single view, overly direct concatenation of drug pair features, and existing data encoding that is difficult to handle, this paper proposes a novel DDI analysis and prediction framework, called the Spiking Multi-View Siamese Neural Network-based (SMVSNN) framework. First, the data of two drugs in each view are processed into fused features using a Siamese spiking convolutional network and a spiking neural perceptron. Second, the processed features from multiple views are integrated into a unified representation through a self-learning attention weight module. Finally, this unified representation is fed into a spiking multilayer perceptron network to obtain the prediction results. Compared to traditional intelligent algorithms, the spiking neurons and the siamese network in SMVSNN can more effectively extract and integrate latent information from drug pair data. Real anticancer drug data, including 904 drugs, 7730 DDI records, and 19 drug interactions, were extracted from authoritative public databases to assess the effectiveness of our framework. The 5-fold cross-validation indicates that SMVSNN outperforms previous models on the majority of metrics. SMVSNN is poised to be an effective method for inferring potential synergistic drug combinations in anticancer therapy.

AI-Assisted Hypothesis Generation to Address Challenges in Cardiotoxicity Research: Simulation Study Using ChatGPT With GPT-4o

BACKGROUND

Cardiotoxicity is a major concern in heart disease research because it can lead to severe cardiac damage, including heart failure and arrhythmias.

OBJECTIVE

This study aimed to explore the ability of ChatGPT with GPT-4o to generate innovative research hypotheses to address 5 major challenges in cardiotoxicity research: the complexity of mechanisms, variability among patients, the lack of detection sensitivity, the lack of reliable biomarkers, and the limitations of animal models.

METHODS

ChatGPT with GPT-4o was used to generate multiple hypotheses for each of the 5 challenges. These hypotheses were then independently evaluated by 3 experts for novelty and feasibility. ChatGPT with GPT-4o subsequently selected the most promising hypothesis from each category and provided detailed experimental plans, including background, rationale, experimental design, expected outcomes, potential pitfalls, and alternative approaches.

RESULTS

ChatGPT with GPT-4o generated 96 hypotheses, of which 13 (14%) were rated as highly novel and 62 (65%) as moderately novel. The average group score of 3.85 indicated a strong level of innovation in these hypotheses. Literature searching identified at least 1 relevant publication for 28 (29%) of the 96 hypotheses. The selected hypotheses included using single-cell RNA sequencing to understand cellular heterogeneity, integrating artificial intelligence with genetic profiles for personalized cardiotoxicity risk prediction, applying machine learning to electrocardiogram data for enhanced detection sensitivity, using multi-omics approaches for biomarker discovery, and developing 3D bioprinted heart tissues to overcome the limitations of animal models. Our group's evaluation of the 30 dimensions of the experimental plans for the 5 hypotheses selected by ChatGPT with GPT-4o revealed consistent strengths in the background, rationale, and alternative approaches, with most of the hypotheses (20/30, 67%) receiving scores of ≥4 in these areas. While the hypotheses were generally well received, the experimental designs were often deemed overly ambitious, highlighting the need for more practical considerations.

CONCLUSIONS

Our study demonstrates that ChatGPT with GPT-4o can generate innovative and potentially impactful hypotheses for overcoming critical challenges in cardiotoxicity research. These findings suggest that artificial intelligence-assisted hypothesis generation could play a crucial role in advancing the field of cardiotoxicity, leading to more accurate predictions, earlier detection, and better patient outcomes.

MAVGAE: a multimodal framework for predicting asymmetric drug-drug interactions based on variational graph autoencoder

Drug-drug interactions refer to the phenomena wherein the potency, duration, or effectiveness of one or multiple drugs undergo alterations of varying degrees as a result of their concurrent or sequential usage. The accurate identification of potential drug interactions plays a pivotal role in mitigating the risks associated with drug administration in patients, it also helps in minimizing the likelihood of hazardous situations arising during a patient's course of treatment. However, researchers have found that there is a problem of asymmetric drug interactions, where one drug may affect another but not vice versa. This adds to the difficulty of prediction, so in polypharmacy, the order of drug administration is critical to efficacy and safety, and few current studies predict asymmetric DDIs. Aiming at the above problems, we propose a framework based on multimodal data and a variational graph autoencoder named MAVGAE for predicting asymmetric drug interactions. The framework initially encodes multimodal data into low-dimensional representations and then utilizes a variational graph autoencoder for encoding and decoding. During the model training process, supervised learning is employed for the classification task with the incorporation of heterogeneity information, ensuring accurate prediction of drug interactions. Experimental validation on a large-scale drug dataset demonstrates the framework's high accuracy and reliability in predicting non-symmetrical drug interactions, offering effective support and guidance for drug research.

Optimization and impact of an evidence-based pre-audit prescription decision system in primary healthcare settings

Objective

Analyze the operation mode of the prescription pre-audit intelligent decision system in a county-level hospital, evaluate its intervention effects on outpatient and emergency operations, thus providing references for similar hospitals to carry out pre-audit intelligent decision system and promote rational drug use.

Methods

Utilizing evidence-based approaches, system rule modifications have been refined and synergized with AI-driven decision-making analytics to examine the operational framework of pre-audit prescription decision system. Additionally, retrospectively analyze the types and levels of problems triggered by outpatient and emergency prescriptions from October 2022 to August 2023, as well as the rationality of prescriptions in the system.

Results

According to the clinical operation of the hospital, problems triggered by unreasonable prescriptions have been finely classified into different levels according to the severity of prescription problems. From October 2022 to August 2023, the number of prescriptions triggering issues such as indications, dosage, special populations, compatibility, administration, and contraindications showed a decreasing trend compared with October 2022 before the intervention. For example, the number of prescriptions with unreasonable routes of administration decreased from 1,745 to 20, and the number of contraindicated prescriptions decreased from 1,399 to 16. The prescriptions triggering Level 5 alerts decreased from 5.609% to 1.793% and the prescription compliance rate increased from 92.20% to 95.98%.

Conclusion

The prescription pre-audit intelligent decision system enhances patient safety and promotes rational drug use. However, the system requires fine-tuning and continuous improvement of the system rule library to effectively validate prescriptions and improve prescription accuracy. In the future, integrating big data, artificial intelligence and other technologies for secondary system development will be a model worthy of consideration. In addition, promoting this system to medical federation to establish a regional prescription review model will further promote the high-quality development of pharmaceutical services.

CA-SQBG: Cross-attention guided Siamese quantum BiGRU for drug-drug interaction extraction

Accurate and efficient drug-drug interaction extraction (DDIE) from the medical corpus is essential for pharmacovigilance, drug therapy and drug development. To solve the problems of unbalance dataset and lack of accurate manual annotations in DDIE, a cross-attention guided Siamese quantum BiGRU (CA-SQBG) is constructed to improve feature representation learning ability for DDIE. It mainly consists of two quantum BiGRUs (QBiGRUs) and a cross-attention, where two QBiGRUs are Siamese implemented in a variational quantum environment to learn the contextual semantic feature representation of drug pairs, cross-attention is employed to learn mutual information from the Siamese QBiGRUs, which in turn allows the two modules to extract DDI more collaboratively. Unlike BiGRU, Siamese QBiGRUs uses internal and external dependencies in quaternion algebra to map DDI correlations within and between multidimensional features, whereas BiGRU can only capture dependencies within sequences. CA-SQBG is evaluated on the DDIExtraction2013 dataset, and the results demonstrate that it can effectively capture the inter- and intra-dependencies within multimodal features with few parameters, using a small number of training samples, and is superior to the most advanced DDIE methods. CA-SQBG offers potential applications for quantum computing and Siamese networks in the field of DDIE. Code is available on https://github.com/xaycq/CA-SQBG.

Artificial Intelligence Models and Tools for the Assessment of Drug-Herb Interactions

Artificial intelligence (AI) has emerged as a powerful tool in medical sciences that is revolutionizing various fields of drug research. AI algorithms can analyze large-scale biological data and identify molecular targets and pathways advancing pharmacological knowledge. An especially promising area is the assessment of drug interactions. The AI analysis of large datasets, such as drugs' chemical structure, pharmacological properties, molecular pathways, and known interaction patterns, can provide mechanistic insights and identify potential associations by integrating all this complex information and returning potential risks associated with these interactions. In this context, an area where AI may prove valuable is in the assessment of the underlying mechanisms of drug interactions with natural products (i.e., herbs) that are used as dietary supplements. These products pose a challenging problem since they are complex mixtures of constituents with diverse and limited information regarding their pharmacological properties, especially their pharmacokinetic data. As the use of herbal products and supplements continues to grow, it becomes increasingly important to understand the potential interactions between them and conventional drugs and the associated adverse drug reactions. This review will discuss AI approaches and how they can be exploited in providing valuable mechanistic insights regarding the prediction of interactions between drugs and herbs, and their potential exploitation in experimental validation or clinical utilization.

Future prospective of AI in drug discovery

Drug discovery and development is very expensive and long with an inferior success rate. It is quite inefficient and costly due to huge R&D costs and lower productivity in pharmaceutical industries, to discover effective drugs and their development. AI can revolutionize the history of drug discovery and development because it will solve all these problems. AI can identify some promising drug candidates, reduce costs, and increase precision. AI algorithms analyze large datasets, predict molecular interactions, and help optimize the design of clinical trials, making the process of drug discovery and biomedical research much more efficient. By combining cutting-edge computation with more conventional pharmaceutical strategy, AI aids in expediting the process of therapeutics development. This chapter is an investigation of the core reasons behind lower approval rates of new drugs, the potential scope of AI to improve the drug discovery and development scenario, and the practical applications in the field. This article will further explore future opportunities, key methodologies, and challenges in the implementation of AI in pharmaceutical research.

Aligning Large Language Models with Humans: A Comprehensive Survey of ChatGPT's Aptitude in Pharmacology

BACKGROUND

Due to the lack of a comprehensive pharmacology test set, evaluating the potential and value of large language models (LLMs) in pharmacology is complex and challenging.

AIMS

This study aims to provide a test set reference for assessing the application potential of both general-purpose and specialized LLMs in pharmacology.

METHODS

We constructed a pharmacology test set consisting of three tasks: drug information retrieval, lead compound structure optimization, and research trend summarization and analysis. Subsequently, we compared the performance of general-purpose LLMs GPT-3.5 and GPT-4 on this test set.

RESULTS

The results indicate that GPT-3.5 and GPT-4 can better understand instructions for information retrieval, scheme optimization, and trend summarization in pharmacology, showing significant potential in basic pharmacology tasks, especially in areas such as drug pharmacological properties, pharmacokinetics, mode of action, and toxicity prediction. These general LLMs also effectively summarize the current challenges and future trends in this field, proving their valuable resource for interdisciplinary pharmacology researchers. However, the limitations of ChatGPT become evident when handling tasks such as drug identification queries, drug interaction information retrieval, and drug structure simulation optimization. It struggles to provide accurate interaction information for individual or specific drugs and cannot optimize specific drugs. This lack of depth in knowledge integration and analysis limits its application in scientific research and clinical exploration.

CONCLUSION

Therefore, exploring retrieval-augmented generation (RAG) or integrating proprietary knowledge bases and knowledge graphs into pharmacology-oriented ChatGPT systems would yield favorable results. This integration will further optimize the potential of LLMs in pharmacology.

Impact of Food Physical Properties on Oral Drug Absorption: A Comprehensive Review

Food-Drug Interaction (FDI) refers to the phenomenon where food affects the pharmacokinetic or pharmacodynamic characteristics of a drug, significantly altering the drug's absorption rate or absorption extent. These Interactions are considered as a primary determinant in influencing the bioavailability of orally administered drugs within the gastrointestinal tract. The impact of food on drug absorption is complex and multifaceted, potentially involving alterations in gastrointestinal physiology, increases in splanchnic blood flow rates, and shifts in the gut microbiota's composition. Up to now, extensive research has focused on the interactions between food composition (such as proteins, fats, and vitamins) and drug absorption. In contrast, the impact of food physical properties (such as viscosity, volume, and pH) has received less attention in drug development. This article reviewed the impact of food properties on oral drug absorption based on a comprehensive literature search, focusing on the influence of food volume and food viscosity. From the perspective of pharmacokinetics, we examined interaction trends between food properties and drugs across different classification based on the Biopharmaceutics Classification System (BCS). In addition, we introduced the practical application of physiologically based pharmacokinetic (PBPK) modeling in predicting oral drug absorption under the influence of food Properties.

Machine learning to predict adverse drug events based on electronic health records: a systematic review and meta-analysis

OBJECTIVE

This systematic review aimed to provide a comprehensive overview of the application of machine learning (ML) in predicting multiple adverse drug events (ADEs) using electronic health record (EHR) data.

METHODS

Systematic searches were conducted using PubMed, Web of Science, Embase, and IEEE Xplore from database inception until 21 November 2023. Studies that developed ML models for predicting multiple ADEs based on EHR data were included.

RESULTS

Ten studies met the inclusion criteria. Twenty ML methods were reported, most commonly random forest (RF, n = 9), followed by AdaBoost (n = 4), eXtreme Gradient Boosting (n = 3), and support vector machine (n = 3). The mean area under the summary receiver operator characteristics curve (AUC) was 0.76 (95% confidence interval [CI] = 0.26-0.95). RF combined with resampling-based approaches achieved high AUCs (0.9448-0.9457). The common risk factors of ADEs included the length of hospital stay, number of prescribed drugs, and admission type. The pooled estimated AUC was 0.72 (95% CI = 0.68-0.75).

CONCLUSIONS

Future studies should adhere to more rigorous reporting standards and consider new ML methods to facilitate the application of ML models in clinical practice.

Artificial intelligence, medications, pharmacogenomics, and ethics

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing various scientific and clinical disciplines including pharmacogenomics (PGx) by enabling the analysis of complex datasets and the development of predictive models. The integration of AI and ML with PGx has the potential to provide more precise, data-driven insights into new drug targets, drug efficacy, drug selection, and risk of adverse events. While significant effort to develop and validate these tools remain, ongoing advancements in AI technologies, coupled with improvements in data quality and depth is anticipated to drive the transition of these tools into clinical practice and delivery of individualized treatments and improved patient outcomes. The successful development and integration of AI-assisted PGx tools will require careful consideration of ethical, legal, and social issues (ELSI) in research and clinical practice. This paper explores the intersection of PGx with AI, highlighting current research and potential clinical applications, and ELSI including privacy, oversight, patient and provider knowledge and acceptance, and the impact on patient-provider relationship and new roles.

A comprehensive review of artificial intelligence for pharmacology research

With the innovation and advancement of artificial intelligence, more and more artificial intelligence techniques are employed in drug research, biomedical frontier research, and clinical medicine practice, especially, in the field of pharmacology research. Thus, this review focuses on the applications of artificial intelligence in drug discovery, compound pharmacokinetic prediction, and clinical pharmacology. We briefly introduced the basic knowledge and development of artificial intelligence, presented a comprehensive review, and then summarized the latest studies and discussed the strengths and limitations of artificial intelligence models. Additionally, we highlighted several important studies and pointed out possible research directions.

Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic

Enzyme-mediated pharmacokinetic drug-drug interactions can be caused by altered activity of drug metabolizing enzymes in the presence of a perpetrator drug, mostly via inhibition or induction. We identified a gap in the literature for a state-of-the art detailed overview assessing this type of DDI risk in the context of drug development. This manuscript discusses in vitro and in vivo methodologies employed during the drug discovery and development process to predict clinical enzyme-mediated DDIs, including the determination of clearance pathways, metabolic enzyme contribution, and the mechanisms and kinetics of enzyme inhibition and induction. We discuss regulatory guidance and highlight the utility of in silico physiologically-based pharmacokinetic modeling, an approach that continues to gain application and traction in support of regulatory filings. Looking to the future, we consider DDI risk assessment for targeted protein degraders, an emerging small molecule modality, which does not have recommended guidelines for DDI evaluation. Our goal in writing this report was to provide early-career researchers with a comprehensive view of the enzyme-mediated pharmacokinetic DDI landscape to aid their drug development efforts.

Stereoisomers Are Not Machine Learning's Best Friends

This study addresses the challenge of accurately identifying stereoisomers in cheminformatics, which originates from our objective to apply machine learning to predict the association constant between cyclodextrin and a guest. Identifying stereoisomers is indeed crucial for machine learning applications. Current tools offer various molecular descriptors, including their textual representation as Isomeric SMILES that can distinguish stereoisomers. However, such representation is text-based and does not have a fixed size, so a conversion is needed to make it usable to machine learning approaches. Word embedding techniques can be used to solve this problem. Mol2vec, a word embedding approach for molecules, offers such a conversion. Unfortunately, it cannot distinguish between stereoisomers due to its inability to capture the spatial configuration of molecular structures. This study proposes several approaches that use word embedding techniques to handle molecular discrimination using stereochemical information on molecules or considering Isomeric SMILES notation as a text in Natural Language Processing. Our aim is to generate a distinct vector for each unique molecule, correctly identifying stereoisomer information in cheminformatics. The proposed approaches are then compared to our original machine learning task: predicting the association constant between cyclodextrin and a guest molecule.

Through a computer monitor darkly: artificial intelligence in absorption, distribution, metabolism and excretion science

Artificial Intelligence (AI) is poised or has already begun to influence absorption, distribution, metabolism and excretion (ADME) science. It is not in the area expected - that of superior modelling of ADME data to increase its predictive power. It is influencing traditional exhaustive and careful literature research by providing almost perfect summaries of existing information. This will highly influence how people study, graduate and progress in the ADME sciences. The literature contains many flaws (protein binding influence on unbound drug concentration is one of the examples cited) and without direction AI may help to popularise them.ADME science has a relatively small number of key assays and values, but these are produced under widely varying conditions so large data sets, the best substrate for artificial intelligence, are not readily available to produce new more predictive systems. The use of AI to enrich the databases may be a near term goal.AI is already contributing in other areas such as technical skill assimilation, maintenance of complex instruments (combined with virtual reality) and the processing of pharmacovigilance.

A low-cost machine learning framework for predicting drug-drug interactions based on fusion of multiple features and a parameter self-tuning strategy

Poly-drug therapy is now recognized as a crucial treatment, and the analysis of drug-drug interactions (DDIs) offers substantial theoretical support and guidance for its implementation. Predicting potential DDIs using intelligent algorithms is an emerging approach in pharmacological research. However, the existing supervised models and deep learning-based techniques still have several limitations. This paper proposes a novel DDI analysis and prediction framework called the Multi-View Semi-supervised Graph-based (MVSG) framework, which provides a comprehensive judgment by integrating multiple DDI features and functions without any time-consuming training process. Unlike conventional approaches, MVSG can search for the most suitable similarity (or distance) measurement among DDI data and construct graph structures for each feature. By employing a parameter self-tuning strategy, MVSG fuses multiple graphs according to the contributions of features' information. The actual anticancer drug data are extracted from the authoritative public database for evaluating the effectiveness of our framework, including 904 drugs, 7730 DDI records and 19 types of drug interactions. Validation results indicate that the prediction is more accurate when multiple features are adopted by our framework. In comparison to conventional machine learning techniques, MVSG can achieve higher performance even with less labeled data and without a training process. Finally, MVSG is employed to narrow down the search for potential valuable combinations.

Advancements and Applications of Artificial Intelligence in Pharmaceutical Sciences: A Comprehensive Review

Artificial intelligence (AI) has revolutionized the pharmaceutical industry, improving drug discovery, development, and personalized patient care. Through machine learning (ML), deep learning, natural language processing (NLP), and robotic automation, AI has enhanced efficiency, accuracy, and innovation in the field. The purpose of this review is to shed light on the practical applications and potential of AI in various pharmaceutical fields. These fields include medicinal chemistry, pharmaceutics, pharmacology and toxicology, clinical pharmacy, pharmaceutical biotechnology, pharmaceutical nanotechnology, pharmacognosy, and pharmaceutical management and economics. By leveraging AI technologies such as ML, deep learning, NLP, and robotic automation, this review delves into the role of AI in enhancing drug discovery, development processes, and personalized patient care. It analyzes AI's impact in specific areas such as drug synthesis planning, formulation development, toxicology predictions, pharmacy automation, and market analysis. Artificial intelligence integration into pharmaceutical sciences has significantly improved medicinal chemistry, drug discovery, and synthesis planning. In pharmaceutics, AI has advanced personalized medicine and formulation development. In pharmacology and toxicology, AI offers predictive capabilities for drug mechanisms and toxic effects. In clinical pharmacy, AI has facilitated automation and enhanced patient care. Additionally, AI has contributed to protein engineering, gene therapy, nanocarrier design, discovery of natural product therapeutics, and pharmaceutical management and economics, including marketing research and clinical trials management. Artificial intelligence has transformed pharmaceuticals, improving efficiency, accuracy, and innovation. This review highlights AI's role in drug development and personalized care, serving as a reference for professionals. The future promises a revolutionized field with AI-driven methodologies.