Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

A combined in silico and MD simulation approach to discover novel LpxC inhibitors targeting multiple drug resistant Pseudomonas aeruginosa

Pseudomonas aeruginosa (P. aeruginosa), a member of the ESKAPE family, is the major cause of infections leading to increased morbidity and mortality due to multidrug resistance (MDR). One of the main proteins involved in the Raetz pathway is LpxC, which plays a significant role in anti-microbial resistance (AMR). Our study aimed to identify a novel compound to combat MDR due to the LpxC protein. It involved in silico methods comprising molecular docking, simulations, ADMET profiling, and DFT calculations. First, an ADMET and bioactivity evaluation of the 25 top-hit compounds retrieved from ligand-based virtual screening was performed, followed by molecular docking. The results revealed compound P-2 as the lead compound, which was further subjected to DFT analysis and molecular dynamics (MD) simulations. With these analyses, our in silico study identified P-2, 3-[(dimethylamino)methyl]-N-[(2 S)-1-(hydroxyamino)-1-oxobutan-2-yl]benzamide as a potential lead compound that may behave as a very potent inhibitor of LpxC for the development of targeted therapies against MDR P. aeruginosa.

The Role of Artificial Intelligence in Drug Discovery and Pharmaceutical Development: A Paradigm Shift in the History of Pharmaceutical Industries

In today's world, with an increasing patient population, the need for medications is increasing rapidly. However, the current practice of drug development is time-consuming and requires a lot of investment by the pharmaceutical industries. Currently, it takes around 8-10 years and $3 billion of investment to develop a medication. Pharmaceutical industries and regulatory authorities are continuing to adopt new technologies to improve the efficiency of the drug development process. However, over the decades the pharmaceutical industries were not able to accelerate the drug development process. The pandemic (COVID-19) has taught the pharmaceutical industries and regulatory agencies an expensive lesson showing the need for emergency preparedness by accelerating the drug development process. Over the last few years, the pharmaceutical industries have been collaborating with artificial intelligence (AI) companies to develop algorithms and models that can be implemented at various stages of the drug development process to improve efficiency and reduce the developmental timelines significantly. In recent years, AI-screened drug candidates have entered clinical testing in human subjects which shows the interest of pharmaceutical companies and regulatory agencies. End-end integration of AI within the drug development process will benefit the industries for predicting the pharmacokinetic and pharmacodynamic profiles, toxicity, acceleration of clinical trials, study design, virtual monitoring of subjects, optimization of manufacturing process, analyzing and real-time monitoring of product quality, and regulatory preparedness. This review article discusses in detail the role of AI in various avenues of the pharmaceutical drug development process, its limitations, regulatory and future perspectives.

Artificial intelligence to predict inhibitors of drug-metabolizing enzymes and transporters for safer drug design

INTRODUCTION

Drug-metabolizing enzymes (DMEs) and transporters (DTs) play integral roles in drug metabolism and drug-drug interactions (DDIs) which directly impact drug efficacy and safety. It is well-established that inhibition of DMEs and DTs often leads to adverse drug reactions (ADRs) and therapeutic failure. As such, early prediction of such inhibitors is vital in drug development. In this context, the limitations of the traditional in vitro assays and QSAR models methods have been addressed by harnessing artificial intelligence (AI) techniques.

AREAS COVERED

This narrative review presents the insights gained from the application of AI for predicting DME and DT inhibitors over the past decade. Several case studies demonstrate successful AI applications in enzyme-transporter interaction prediction, and the authors discuss workflows for integrating these predictions into drug design and regulatory frameworks.

EXPERT OPINION

The application of AI in predicting DME and DT inhibitors has demonstrated significant potential toward enhancing drug safety and effectiveness. However, critical challenges involve the data quality, biases, and model transparency. The availability of diverse, high-quality datasets alongside the integration of pharmacokinetic and genomic data are essential. Lastly, the collaboration among computational scientists, pharmacologists, and regulatory bodies is pyramidal in tailoring AI tools for personalized medicine and safer drug development.

Drug repurposing in amyotrophic lateral sclerosis (ALS)

INTRODUCTION

Identifying treatments that can alter the natural history of amyotrophic lateral sclerosis (ALS) is challenging. For years, drug discovery in ALS has relied upon traditional approaches with limited success. Drug repurposing, where clinically approved drugs are reevaluated for other indications, offers an alternative strategy that overcomes some of the challenges associated with de novo drug discovery.

AREAS COVERED

In this review, the authors discuss the challenge of drug discovery in ALS and examine the potential of drug repurposing for the identification of new effective treatments. The authors consider a range of approaches, from screening in experimental models to computational approaches, and outline some general principles for preclinical and clinical research to help bridge the translational gap. Literature was reviewed from original publications, press releases and clinical trials.

EXPERT OPINION

Despite remaining challenges, drug repurposing offers the opportunity to improve therapeutic options for ALS patients. Nevertheless, stringent preclinical research will be necessary to identify the most promising compounds together with innovative experimental medicine studies to bridge the translational gap. The authors further highlight the importance of combining expertise across academia, industry and wider stakeholders, which will be key in the successful delivery of repurposed therapies to the clinic.

Advanced Artificial Intelligence Technologies Transforming Contemporary Pharmaceutical Research

One area of study within machine learning and artificial intelligence (AI) seeks to create computer programs with intelligence that can mimic human focal processes in order to produce results. This technique includes data collection, effective data usage system development, conclusion illustration, and arrangements. Analysis algorithms that are learning to mimic human cognitive activities are the most widespread application of AI. Artificial intelligence (AI) studies have proliferated, and the field is quickly beginning to understand its potential impact on medical services and investigation. This review delves deeper into the pros and cons of AI across the healthcare and pharmaceutical research industries. Research and review articles published throughout the last few years were selected from PubMed, Google Scholar, and Science Direct, using search terms like 'artificial intelligence', 'drug discovery', 'pharmacy research', 'clinical trial', etc. This article provides a comprehensive overview of how artificial intelligence (AI) is being used to diagnose diseases, treat patients digitally, find new drugs, and predict when outbreaks or pandemics may occur. In artificial intelligence, neural networks and deep learning are some of the most popular tools; in clinical research, Bayesian non-parametric approaches hold promise for better results, while smartphones and the processing of natural languages are employed in recognizing patients and trial monitoring. Seasonal flu, Ebola, Zika, COVID-19, tuberculosis, and outbreak predictions were made using deep computation and artificial intelligence. The academic world is hopeful that AI development will lead to more efficient and less expensive medical and pharmaceutical investigations and better public services.

Advances in AI-based strategies and tools to facilitate natural product and drug development

Natural products and their derivatives have been important for treating diseases in humans, animals, and plants. However, discovering new structures from natural sources is still challenging. In recent years, artificial intelligence (AI) has greatly aided the discovery and development of natural products and drugs. AI facilitates to: connect genetic data to chemical structures or vice-versa, repurpose known natural products, predict metabolic pathways, and design and optimize metabolites biosynthesis. More recently, the emergence and improvement in neural networks such as deep learning and ensemble automated web based bioinformatics platforms have sped up the discovery process. Meanwhile, AI also improves the identification and structure elucidation of unknown compounds from raw data like mass spectrometry and nuclear magnetic resonance. This article reviews these AI-driven methods and tools, highlighting their practical applications and guide for efficient natural product discovery and drug development.

Progress of machine learning in the application of small molecule druggability prediction

Machine learning (ML) has become an important tool for predicting the pharmaceutical properties of small molecules. Recent advancements in ML algorithms enable the rapid and accurate evaluation of solubility, activity, toxicity, pharmacokinetics, and other molecular properties through ML-based models. By conducting virtual screening of drug targets and elucidating drug-target protein interactions, researchers can conduct preliminary evaluations of the activity and safety of compounds from the ultra-large drug compound libraries, thereby accelerating the screening process for lead compounds. Moreover, ML leverages existing experimental data to train and generate new datasets, addressing the challenge of limited compounds and protein target data. This review provided a concise overview of ML applications in predicting small molecule properties, focusing on model construction principles, molecular feature selection, and other essential aspects. It also discussed the potential applications of ML in the screening of pharmaceutical small molecules.

Biological activity of natural 2-quinolinones

While natural products have undeniably played a crucial role in drug discovery, challenges such as limited availability and complex synthesis methods have hindered the identification of lead compounds. At the core of numerous natural and synthetic compounds, each displaying distinct biological behaviours, lies the foundational structure of 2-quinolinone. Compounds with this structural motif exhibit a broad range of effects in different tissues. Furthermore, specific members showcase therapeutic potential for various disorders. Despite the significance of these compounds, the current review literature has not provided a comprehensive overview, underscoring the essential contribution of this article in exploring their biological functions. This study examines the biological activity of selected 2-quinolinone alkaloids across diverse organisms, unveiling their potential as a source of innovative bioactive natural products.

Advanced AI and ML frameworks for transforming drug discovery and optimization: With innovative insights in polypharmacology, drug repurposing, combination therapy and nanomedicine

Artificial Intelligence (AI) and Machine Learning (ML) are transforming drug discovery by overcoming traditional challenges like high costs, time-consuming, and frequent failures. AI-driven approaches streamline key phases, including target identification, lead optimization, de novo drug design, and drug repurposing. Frameworks such as deep neural networks (DNNs), convolutional neural networks (CNNs), and deep reinforcement learning (DRL) models have shown promise in identifying drug targets, optimizing delivery systems, and accelerating drug repurposing. Generative adversarial networks (GANs) and variational autoencoders (VAEs) aid de novo drug design by creating novel drug-like compounds with desired properties. Case studies, such as DDR1 kinase inhibitors designed using generative models and CDK20 inhibitors developed via structure-based methods, highlight AI's ability to produce highly specific therapeutics. Models like SNF-CVAE and DeepDR further advance drug repurposing by uncovering new therapeutic applications for existing drugs. Advanced ML algorithms enhance precision in predicting drug efficacy, toxicity, and ADME-Tox properties, reducing development costs and improving drug-target interactions. AI also supports polypharmacology by optimizing multi-target drug interactions and enhances combination therapy through predictions of drug synergies and antagonisms. In nanomedicine, AI models like CURATE.AI and the Hartung algorithm optimize personalized treatments by predicting toxicological risks and real-time dosing adjustments with high accuracy. Despite its potential, challenges like data quality, model interpretability, and ethical concerns must be addressed. High-quality datasets, transparent models, and unbiased algorithms are essential for reliable AI applications. As AI continues to evolve, it is poised to revolutionize drug discovery and personalized medicine, advancing therapeutic development and patient care.

Efficient and Explainable Virtual Screening of Molecules through Fingerprint-Generating Networks Integrated with Artificial Neural Networks

A machine learning-based drug screening technique has been developed and optimized using a novel, stitched neural network architecture with trainable, graph convolution-based fingerprints as a base into an artificial neural network. The architecture is efficient, explainable, and performant as a tool for the binary classification of ligands based on a user-chosen docking score threshold. Assessment using two standardized virtual screening databases substantiated the architecture's ability to learn molecular features and substructures and predict ligand classes based on binding affinity values more effectively than similar contemporary counterparts. Furthermore, to highlight the architecture's utility to groups and laboratories with varying resources, experiments were carried out using randomly sampled small molecules from the ZINC database and their computational docking scores against six drug-design relevant proteins. This new architecture proved to be more efficient in screening molecules that less favorably bind to a specific target thereby retaining top-hit molecules. Compared to similar protocols developed using Morgan fingerprints, the neural fingerprint-based model shows superiority in retaining the best ligands while filtering molecules at a higher relative rate. Lastly, the explainability of the model was investigated; it was revealed that the model accurately emphasized important chemical substructures and atoms through the intermediate fingerprint, which, in turn, contributed heavily to the ultimate prediction of a ligand as binding tightly to a certain protein.

Computational Design and Optimization of Multi-Compound Multivesicular Liposomes for Co-Delivery of Traditional Chinese Medicine Compounds

Study explored the synergistic anti-tumor effects of a combination of compounds from Traditional Chinese Medicine, including rosmarinic acid (RA), chlorogenic acid (CA), and scoparone (SCO), in the formulation of multivesicular liposomes (MVLs). Optimization of formulations and process parameters was essential to achieve effective liposomal encapsulation and optimal release profiles for these three compounds with diverse properties. Traditional trial-and-error approaches are inefficient for the optimization of complex multi-compound MVLs. We developed a new formulation optimization model, which could address this issue by predicting the optimal multi-compound MVLs formulation. Our machine learning model integrated support vector machine regression (SVR) algorithm and cuckoo search (CS) algorithm, resulting in three CS-SVR models to predict single-compound MVLs. The CS algorithm, with various weighting rules, was then applied to search the best formulation parameters across three CS-SVR models and to maximize the encapsulation efficiency for all three compounds. The multi-compound MLVs were subsequently prepared under the predicted conditions, achieving an optimized particle size of 15.12 µm, with encapsulation efficiencies of 82.93 ± 2.43% for CA, 82.22 ± 1.25% for RA, and 95.60 ± 0.18% for SCO. The predicted optimal multi-compound MVLs were further validated through in vitro characterization and in vivo anti-tumor experiments, showing a promising synergistic anti-tumor effect consistent with in vitro results. This model accurately predicted optimal encapsulation conditions, which were validated experimentally, demonstrating improved encapsulation efficiencies and reduced trial-and-error iterations. Collectively, our model provides a predictive pathway for multi-compound MVLs formulation, indicating the ability of this model to significantly reduce experimental burden and accelerate formulation development.

Deep learning: A game changer in drug design and development

The lengthy and costly drug discovery process is transformed by deep learning, a subfield of artificial intelligence. Deep learning technologies expedite the procedure, increasing treatment success rates and speeding life-saving procedures. Deep learning stands out in target identification and lead selection. Deep learning greatly accelerates initial stage by analyzing large datasets of biological data to identify possible therapeutic targets and rank targeted drug molecules with desired features. Predicting possible adverse effects is another significant challenge. Deep learning offers prompt and efficient assistance with toxicology prediction in a very short time, deep learning algorithms can forecast a new drug's possible harm. This enables to concentrate on safer alternatives and steer clear of late-stage failures brought on by unanticipated toxicity. Deep learning unlocks the possibility of drug repurposing; by examining currently available medications, it is possible to find whole new therapeutic uses. This method speeds up development of diseases that were previously incurable. De novo drug discovery is made possible by deep learning when combined with sophisticated computational modeling, it can create completely new medications from the ground. Deep learning can recommend and direct towards new drug candidates with high binding affinities and intended therapeutic effects by examining molecular structures of disease targets. This provides focused and personalized medication. Lastly, drug characteristics can be optimized with aid of deep learning. Researchers can create medications with higher bioavailability and fewer toxicity by forecasting drug pharmacokinetics. In conclusion, deep learning promises to accelerate drug development, reduce costs, and ultimately save lives.

Phosphodiesterase Type 5 Inhibitors in Male Reproduction: Molecular Mechanisms and Clinical Implications for Fertility Management

Phosphodiesterases, particularly the type 5 isoform (PDE5), have gained recognition as pivotal regulators of male reproductive physiology, exerting significant influence on testicular function, sperm maturation, and overall fertility potential. Over the past several decades, investigations have expanded beyond the original therapeutic intent of PDE5 inhibitors for erectile dysfunction, exploring their broader reproductive implications. This narrative review integrates current evidence from in vitro studies, animal models, and clinical research to clarify the roles of PDEs in effecting the male reproductive tract, with an emphasis on the mechanistic pathways underlying cyclic nucleotide signaling, the cellular specificity of PDE isoform expression, and the effects of PDE5 inhibitors on Leydig and Sertoli cell functions. Although certain findings suggest potential improvements in sperm motility, semen parameters, and a more favorable biochemical milieu for spermatogenesis, inconsistencies in study design, limited sample sizes, and inadequate long-term data temper definitive conclusions. Addressing these gaps through standardized protocols, larger and more diverse patient cohorts, and explorations of mechanistic biomarkers could pave the way for incorporating PDE5 inhibitors into evidence-based fertility treatment strategies. In the future, such targeted approaches may inform individualized regimens, optimize male reproductive outcomes, and refine the clinical application of PDE5 inhibitors as part of comprehensive male fertility management.

Innovative Approaches in Regulatory Affairs: Leveraging Artificial Intelligence and Machine Learning for Efficient Compliance and Decision-Making

Artificial Intelligence (AI) and AI-driven technologies are transforming industries across the board, with the pharmaceutical sector emerging as a frontrunner beneficiary. This article explores the growing impact of AI and Machine Learning (ML) within pharmaceutical Regulatory Affairs, particularly in dossier preparation, compilation, documentation, submission, review, and regulatory compliance. By automating time-intensive tasks, these technologies streamline workflows, accelerate result generation, and shorten the product approval timeline. However, despite their immense potential, AI and ML also introduce new challenges. Issues such as AI software validation, data management security and privacy, potential biases, ethical concerns, and change management requirements must be addressed. This review highlights current AI-based tools actively used by regulatory professionals such as DocShifter, Veeva Vault, RiskWatch, Freyr SubmitPro, Litera Microsystems, cortical.io etc., examines both the benefits and obstacles of integrating these advanced systems into regulatory practices. Given the rapid pace of technological innovation, the article underscores the need for proactive collaboration with regulatory bodies to manage these developments. It also stresses the importance of adapting to evolving regulatory frameworks and embracing new technologies. Although regulatory agencies like the United Sates Food and Drug Administration (USFDA), European Medicines Agency (EMA), and Medicines and Healthcare products Regulatory Agency (MHRA) are working on guidelines for AI and ML adoption, clear, standardized protocols are still in the works. While the journey ahead may be complex, the integration of AI promises to fundamentally reshape regulatory processes and accelerate the approval of safe, effective pharmaceutical products.

Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives

The applications of artificial intelligence (AI) in pharmaceutical sectors have advanced drug discovery and development methods. AI has been applied in virtual drug design, molecule synthesis, advanced research, various screening methods, and decision-making processes. In the fourth industrial revolution, when medical discoveries are happening swiftly, AI technology is essential to reduce the costs, effort, and time in the pharmaceutical industry. Further, it will aid "genome-based medicine" and "drug discovery." AI may prepare proactive databases according to diseases, disorders, and appropriate usage of drugs which will facilitate the required data for the process of drug development. The application of AI has improved clinical trials on patient selection in a population, stratification, and sample assessment such as biomarkers, effectiveness measures, dosage selection, and trial length. Various studies suggest AI could be perform better compared to conventional techniques in drug discovery. The present review focused on the positive impact of AI in drug discovery and development processes in the pharmaceutical industry and beneficial usage in health sectors as well.

Role of Artificial Intelligence in Drug Discovery to Revolutionize the Pharmaceutical Industry: Resources, Methods and Applications

Traditional drug discovery methods such as wet-lab testing, validations, and synthetic techniques are time-consuming and expensive. Artificial Intelligence (AI) approaches have progressed to the point where they can have a significant impact on the drug discovery process. Using massive volumes of open data, artificial intelligence methods are revolutionizing the pharmaceutical industry. In the last few decades, many AI-based models have been developed and implemented in many areas of the drug development process. These models have been used as a supplement to conventional research to uncover superior pharmaceuticals expeditiously. AI's involvement in the pharmaceutical industry was used mostly for reverse engineering of existing patents and the invention of new synthesis pathways. Drug research and development to repurposing and productivity benefits in the pharmaceutical business through clinical trials. AI is studied in this article for its numerous potential uses. We have discussed how AI can be put to use in the pharmaceutical sector, specifically for predicting a drug's toxicity, bioactivity, and physicochemical characteristics, among other things. In this review article, we have discussed its application to a variety of problems, including de novo drug discovery, target structure prediction, interaction prediction, and binding affinity prediction. AI for predicting drug interactions and nanomedicines were also considered.

Unleashing the future: The revolutionary role of machine learning and artificial intelligence in drug discovery

Drug discovery is a complex and multifaceted process aimed at identifying new therapeutic compounds with the potential to treat various diseases. Traditional methods of drug discovery are often time-consuming, expensive, and characterized by low success rates. Because of this, there is an urgent need to improve the drug development process using new technologies. The integration of the current state-of-art of artificial intelligence (AI) and machine learning (ML) approaches with conventional methods will enhance the efficiency and effectiveness of pharmaceutical research. This review highlights the transformative impact of AI and ML in drug discovery, discussing current applications, challenges, and future directions in harnessing these technologies to accelerate the development of innovative therapeutics. We have discussed the latest developments in AI and ML technologies to streamline several stages of drug discovery, from target identification and validation to lead optimization and preclinical studies.

Overcoming Challenges in Small-Molecule Drug Bioavailability: A Review of Key Factors and Approaches

The bioavailability of small-molecule drugs remains a critical challenge in pharmaceutical development, significantly impacting therapeutic efficacy and commercial viability. This review synthesizes recent advances in understanding and overcoming bioavailability limitations, focusing on key physicochemical and biological factors influencing drug absorption and distribution. We examine cutting-edge strategies for enhancing bioavailability, including innovative formulation approaches, rational structural modifications, and the application of artificial intelligence in drug design. The integration of nanotechnology, 3D printing, and stimuli-responsive delivery systems are highlighted as promising avenues for improving drug delivery. We discuss the importance of a holistic, multidisciplinary approach to bioavailability optimization, emphasizing early-stage consideration of ADME properties and the need for patient-centric design. This review also explores emerging technologies such as CRISPR-Cas9-mediated personalization and microbiome modulation for tailored bioavailability enhancement. Finally, we outline future research directions, including advanced predictive modeling, overcoming biological barriers, and addressing the challenges of emerging therapeutic modalities. By elucidating the complex interplay of factors affecting bioavailability, this review aims to guide future efforts in developing more effective and accessible small-molecule therapeutics.

The challenge of antimicrobial resistance (AMR): current status and future prospects

Antimicrobial resistance (AMR) represents a critical global threat, compromising the effectiveness of antibacterial drugs as bacteria adapt and survive exposure to many classes of these drugs. This phenomenon is primarily fueled by the widespread overuse and misuse of antibacterial drugs, exerting selective pressure on bacteria and promoting the emergence of multi-resistant strains. AMR poses a top-priority challenge to public health due to its widespread epidemiological and economic implications, exacerbated not only by the diminishing effectiveness of currently available antimicrobial agents but also by the limited development of genuinely effective new molecules. In addressing this issue, our research aimed to examine the scientific literature narrating the Italian situation in the common European context of combating AMR. We sought to delineate the current state of AMR and explore future prospects through an analysis of strategies to counter antibacterial drug resistance. Adopting the "One Health" model, our objective was to comprehensively engage diverse sectors, integrate various disciplines, and propose programs, policies, and regulations. This narrative review, based on PubMed research related to antibiotic resistance, emphasizes the urgent need for a coordinated and proactive approach at both national and European levels to mitigate the impact of AMR and pave the way for effective strategies to counter this global health challenge.

Advances in artificial intelligence-based technologies for increasing the quality of medical products

Artificial intelligence (AI) is a technology that combines machine learning (ML) and deep learning. It has numerous usages in the domains of medicine and other sciences. Artificial intelligence can forecast the behavior of a drug's target protein and predict its desired physicochemical qualities. AI's potential to enhance healthcare services offerings formerly unheard-of opportunities for cost reserves, enhanced overall clinical and patient outcomes. The recent development of research in the biomedical field, encompassing fields such as genomics, computational medicine, AI, and algorithms for learning, has led to the demand for novel technology, a fresh workforce, and new standards of practice set the stage for the revolution in healthcare. By connecting these health statistics with cutting-edge AI technologies, precise insights into patient treatment can be obtained. Moreover, AI can aid in the search for new drugs by foretelling the target protein's two-dimensional structure. In the current review, an overview of the latest AI-based technologies and how they may be employed to reduce product development time to market and snowballing product quality, cost-effectiveness, as well as security throughout the manufacturing process is detailed.

The role of artificial intelligence in drug screening, drug design, and clinical trials

The role of computational tools in drug discovery and development is becoming increasingly important due to the rapid development of computing power and advancements in computational chemistry and biology, improving research efficiency and reducing the costs and potential risks of preclinical and clinical trials. Machine learning, especially deep learning, a subfield of artificial intelligence (AI), has demonstrated significant advantages in drug discovery and development, including high-throughput and virtual screening, ab initio design of drug molecules, and solving difficult organic syntheses. This review summarizes AI technologies used in drug discovery and development, including their roles in drug screening, design, and solving the challenges of clinical trials. Finally, it discusses the challenges of drug discovery and development based on AI technologies, as well as potential future directions.

Recent progress in artificial intelligence and machine learning for novel diabetes mellitus medications development

Diabetes mellitus, stemming from either insulin resistance or inadequate insulin secretion, represents a complex ailment that results in prolonged hyperglycemia and severe complications. Patients endure severe ramifications such as kidney disease, vision impairment, cardiovascular disorders, and susceptibility to infections, leading to significant physical suffering and imposing substantial socio-economic burdens. This condition has evolved into an increasingly severe health crisis. There is an urgent need to develop new treatments with improved efficacy and fewer adverse effects to meet clinical demands. However, novel drug development is costly, time-consuming, and often associated with side effects and suboptimal efficacy, making it a major challenge. Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized drug development across its comprehensive lifecycle, spanning drug discovery, preclinical studies, clinical trials, and post-market surveillance. These technologies have significantly accelerated the identification of promising therapeutic candidates, optimized trial designs, and enhanced post-approval safety monitoring. Recent advances in AI, including data augmentation, interpretable AI, and integration of AI with traditional experimental methods, offer promising strategies for overcoming the challenges inherent in AI-based drug discovery. Despite these advancements, there exists a notable gap in comprehensive reviews detailing AI and ML applications throughout the entirety of developing medications for diabetes mellitus. This review aims to fill this gap by evaluating the impact and potential of AI and ML technologies at various stages of diabetes mellitus drug development. It does that by synthesizing current research findings and technological advances so as to effectively control diabetes mellitus and mitigate its far-reaching social and economic impacts. The integration of AI and ML promises to revolutionize diabetes mellitus treatment strategies, offering hope for improved patient outcomes and reduced healthcare burdens worldwide.

A Chronicle Review of In-Silico Approaches for Discovering Novel Antimicrobial Agents to Combat Antimicrobial Resistance

Antimicrobial resistance (AMR) poses a foremost threat to global health, necessitating innovative strategies for discovering antimicrobial agents. This review explores the role and recent advances of in-silico techniques in identifying novel antimicrobial agents and combating AMR giving few briefings of recent case studies of AMR. In-silico techniques, such as homology modeling, virtual screening, molecular docking, pharmacophore modeling, molecular dynamics simulation, density functional theory, integrated machine learning, and artificial intelligence, are systematically reviewed for their utility in discovering antimicrobial agents. These computational methods enable the rapid screening of large compound libraries, prediction of drug-target interactions, and optimization of drug candidates. The review discusses integrating in-silico approaches with traditional experimental methods and highlights their potential to accelerate the discovery of new antimicrobial agents. Furthermore, it emphasizes the significance of interdisciplinary collaboration and data-sharing initiatives in advancing antimicrobial research. Through a comprehensive discussion of the latest developments in in-silico techniques, this review provides valuable insights into the future of antimicrobial research and the fight against AMR.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s12088-024-01355-x.

Bibliometric analysis of artificial intelligence in healthcare research: Trends and future directions

Objective

The presence of artificial intelligence (AI) in healthcare is a powerful and game-changing force that is completely transforming the industry as a whole. Using sophisticated algorithms and data analytics, AI has unparalleled prospects for improving patient care, streamlining operational efficiency, and fostering innovation across the healthcare ecosystem. This study conducts a comprehensive bibliometric analysis of research on AI in healthcare, utilising the SCOPUS database as the primary data source.

Methods

Preliminary findings from 2013 identified 153 publications on AI and healthcare. Between 2019 and 2023, the number of publications increased exponentially, indicating significant growth and development in the field. The analysis employs various bibliometric indicators to assess research production performance, science mapping techniques, and thematic mapping analysis.

Results

The study reveals insights into research hotspots, thematic focus, and emerging trends in AI and healthcare research. Based on an extensive examination of the Scopus database provides a brief overview and suggests potential avenues for further investigation.

Conclusion

This article provides valuable contributions to understanding the current landscape of AI in healthcare, offering insights for future research directions and informing strategic decision making in the field.

Artificial Intelligence, the Digital Surgeon: Unravelling Its Emerging Footprint in Healthcare - The Narrative Review

Background

Artificial Intelligence (AI) holds transformative potential for the healthcare industry, offering innovative solutions for diagnosis, treatment planning, and improving patient outcomes. As AI continues to be integrated into healthcare systems, it promises advancements across various domains. This review explores the diverse applications of AI in healthcare, along with the challenges and limitations that need to be addressed. The aim is to provide a comprehensive overview of AI's impact on healthcare and to identify areas for further development and focus.

Main Applications

The review discusses the broad range of AI applications in healthcare. In medical imaging and diagnostics, AI enhances the accuracy and efficiency of diagnostic processes, aiding in early disease detection. AI-powered clinical decision support systems assist healthcare professionals in patient management and decision-making. Predictive analytics using AI enables the prediction of patient outcomes and identification of potential health risks. AI-driven robotic systems have revolutionized surgical procedures, improving precision and outcomes. Virtual assistants and chatbots enhance patient interaction and support, providing timely information and assistance. In the pharmaceutical industry, AI accelerates drug discovery and development by identifying potential drug candidates and predicting their efficacy. Additionally, AI improves administrative efficiency and operational workflows in healthcare, streamlining processes and reducing costs. AI-powered remote monitoring and telehealth solutions expand access to healthcare, particularly in underserved areas.

Challenges and Limitations

Despite the significant promise of AI in healthcare, several challenges persist. Ensuring the reliability and consistency of AI-driven outcomes is crucial. Privacy and security concerns must be navigated carefully, particularly in handling sensitive patient data. Ethical considerations, including bias and fairness in AI algorithms, need to be addressed to prevent unintended consequences. Overcoming these challenges is critical for the ethical and successful integration of AI in healthcare.

Conclusion

The integration of AI into healthcare is advancing rapidly, offering substantial benefits in improving patient care and operational efficiency. However, addressing the associated challenges is essential to fully realize the transformative potential of AI in healthcare. Future efforts should focus on enhancing the reliability, transparency, and ethical standards of AI technologies to ensure they contribute positively to global health outcomes.

Development and validation of machine learning models for the prediction of SH-2 containing protein tyrosine phosphatase 2 inhibitors

Discovery and development of a new drug to the market is a highly challenging and resource consuming process. Although, modern drug discovery technologies have enabled the rapid identification of lead compounds, translation of the lead compounds into successful clinical candidates remains a big challenge. In recent years, the availability of massive structural and biological data of diverse small molecules and macromolecules has helped the researchers to deep mine the multidimensional data with the help of artificial intelligence-based predictive tools to draw useful insights on the structural features of biological or therapeutic significance. The aim of this study was to utilize the available data on small molecule (SH2)-containing protein tyrosine phosphatase 2 (SHP2) inhibitors to build and develop machine learning (ML) models that can predict the SHP2 inhibitory potential of new compounds. The dataset contained 2739 unique small molecule SHP2 inhibitors obtained from the BindingDB, ChEMBL and recent literature. After curation of the data, the predictive models such as XGBoost, K nearest neighbours, neural networks were developed and validated through a tenfold cross-validation testing procedure. Out of the seven models developed, the XGBoost model showed an excellent performance with ROC AUC score of 0.96 and accuracy of 0.97 on the test data. Moreover, the Shapley Additive Explanations method was applied to assess a more in-depth understanding of the influence of variables on the model's predictions. In summary, the XGBoost model developed in this study can be useful in the identification of novel SHP2 inhibitors and therefore, can accelerate the discovery of novel therapeutics for cancer therapy.

Bioinformatics and molecular biology tools for diagnosis, prevention, treatment and prognosis of COVID-19

Since December 2019, a new form of Severe Acute Respiratory Syndrome (SARS) has emerged worldwide, caused by SARS coronavirus 2 (SARS-CoV-2). This disease was called COVID-19 and was declared a pandemic by the World Health Organization in March 2020. Symptoms can vary from a common cold to severe pneumonia, hypoxemia, respiratory distress, and death. During this period of world stress, the medical and scientific community were able to acquire information and generate scientific data at unprecedented speed, to better understand the disease and facilitate vaccines and therapeutics development. Notably, bioinformatics tools were instrumental in decoding the viral genome and identifying critical targets for COVID-19 diagnosis and therapeutics. Through the integration of omics data, bioinformatics has also improved our understanding of disease pathogenesis and virus-host interactions, facilitating the development of targeted treatments and vaccines. Furthermore, molecular biology techniques have accelerated the design of sensitive diagnostic tests and the characterization of immune responses, paving the way for precision medicine approaches in treating COVID-19. Our analysis highlights the indispensable contributions of bioinformatics and molecular biology to the global effort against COVID-19. In this review, we aim to revise the COVID-19 features, diagnostic, prevention, treatment options, and how molecular biology, modern bioinformatic tools, and collaborations have helped combat this pandemic. An integrative literature review was performed, searching articles on several sites, including PUBMED and Google Scholar indexed in referenced databases, prioritizing articles from the last 3 years. The lessons learned from this COVID-19 pandemic will place the world in a much better position to respond to future pandemics.

Antitumoral activity of different Amaryllidaceae alkaloids: In vitro and in silico assays

ETHNOPHARMACOLOGY RELEVANCE

The plants of Amaryllidaceae family, such as Amaryllis belladonna L., have been used as herbal remedies for thousands of years to address various disorders, including diseases that might today be identified as cancer.

AIM OF THE STUDY

The objective of this work was to evaluate the potential of three Amaryllidaceae alkaloids against four cancer cell lines.

MATERIAL AND METHODS

The alkaloids lycorine, 1-O-acetylcaranine, and montanine were evaluated in vitro against colon adenocarcinoma cell line (HCT-116) and breast carcinoma cell lines (MCF-7, MDAMB231, and Hs578T). Computational experiments (target prediction and molecular docking) were conducted to gain a deeper comprehension of possible interactions between these alkaloids and potential targets associated with these tumor cells.

RESULTS

Montanine presented the best results against HCT-116, MDAMB231, and Hs578T cell lines, while lycorine was the most active against MCF-7. In alignment with the target prediction outcomes and existing literature, four potential targets were chosen for the molecular docking analysis: CDK8, EGFR, ER-alpha, and dCK. The docking scores revealed two potential targets for the alkaloids with scores similar to co-crystallized inhibitors and substrates: CDK8 and dCK. A visual analysis of the optimal docked configurations indicates that the alkaloids may interact with some key residues in contrast to the other docked compounds. This observation implies their potential to bind effectively to both targets.

CONCLUSIONS

In vitro and in silico results corroborate with data literature suggesting the Amaryllidaceae alkaloids as interesting molecules with antitumoral properties, especially montanine, which showed the best in vitro results against colorectal and breast carcinoma. More studies are necessary to confirm the targets and pharmaceutical potential of montanine against these cancer cell lines.

The Role of AI in Drug Discovery

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

Biosimilars in the Era of Artificial Intelligence-International Regulations and the Use in Oncological Treatments

This research is based on three fundamental aspects of successful biosimilar development in the challenging biopharmaceutical market. First, biosimilar regulations in eight selected countries: Japan, South Korea, the United States, Canada, Brazil, Argentina, Australia, and South Africa, represent the four continents. The regulatory aspects of the countries studied are analyzed, highlighting the challenges facing biosimilars, including their complex approval processes and the need for standardized regulatory guidelines. There is an inconsistency depending on whether the biosimilar is used in a developed or developing country. In the countries observed, biosimilars are considered excellent alternatives to patent-protected biological products for the treatment of chronic diseases. In the second aspect addressed, various analytical AI modeling methods (such as machine learning tools, reinforcement learning, supervised, unsupervised, and deep learning tools) were analyzed to observe patterns that lead to the prevalence of biosimilars used in cancer to model the behaviors of the most prominent active compounds with spectroscopy. Finally, an analysis of the use of active compounds of biosimilars used in cancer and approved by the FDA and EMA was proposed.

A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

Comprehensive Review on the Virulence Factors and Therapeutic Strategies with the Aid of Artificial Intelligence against Mucormycosis

Mucormycosis, a rare but deadly fungal infection, was an epidemic during the COVID-19 pandemic. The rise in cases (COVID-19-associated mucormycosis, CAM) is attributed to excessive steroid and antibiotic use, poor hospital hygiene, and crowded settings. Major contributing factors include diabetes and weakened immune systems. The main manifesting forms of CAM─cutaneous, pulmonary, and the deadliest, rhinocerebral─and disseminated infections elevated mortality rates to 85%. Recent focus lies on small-molecule inhibitors due to their advantages over standard treatments like surgery and liposomal amphotericin B (which carry several long-term adverse effects), offering potential central nervous system penetration, diverse targets, and simpler dosing owing to their small size, rendering the ability to traverse the blood-brain barrier via passive diffusion facilitated by the phospholipid membrane. Adaptation and versatility in mucormycosis are facilitated by a multitude of virulence factors, enabling the pathogen to dynamically respond to various environmental stressors. A comprehensive understanding of these virulence mechanisms is imperative for devising effective therapeutic interventions against this highly opportunistic pathogen that thrives in immunocompromised individuals through its angio-invasive nature. Hence, this Review delineates the principal virulence factors of mucormycosis, the mechanisms it employs to persist in challenging host environments, and the current progress in developing small-molecule inhibitors against them.

Virtual Screening of Molecules via Neural Fingerprint-based Deep Learning Technique

A machine learning-based drug screening technique has been developed and optimized using convolutional neural network-derived fingerprints. The optimization of weights in the neural network-based fingerprinting technique was compared with fixed Morgan fingerprints in regard to binary classification on drug-target binding affinity. The assessment was carried out using six different target proteins using randomly chosen small molecules from the ZINC15 database for training. This new architecture proved to be more efficient in screening molecules that less favorably bind to specific targets and retaining molecules that favorably bind to it.

Scientific contribution

We have developed a new neural fingerprint-based screening model that has a significant ability to capture hits. Despite using a smaller dataset, this model is capable of mapping chemical space similar to other contemporary algorithms designed for molecular screening. The novelty of the present algorithm lies in the speed with which the models are trained and tuned before testing its predictive capabilities and hence is a significant step forward in the field of machine learning-embedded computational drug discovery.

Integrating clinical pharmacology and artificial intelligence: potential benefits, challenges, and role of clinical pharmacologists

INTRODUCTION

The integration of artificial intelligence (AI) into clinical pharmacology could be a potential approach for accelerating drug discovery and development, improving patient care, and streamlining medical research processes.

AREAS COVERED

We reviewed the current state of AI applications in clinical pharmacology, focusing on drug discovery and development, precision medicine, pharmacovigilance, and other ventures. Key AI applications in clinical pharmacology are examined, including machine learning, natural language processing, deep learning, and reinforcement learning etc. Additionally, the evolving role of clinical pharmacologists, ethical considerations, and challenges in implementing AI in clinical pharmacology are discussed.

EXPERT OPINION

The AI could be instrumental in accelerating drug discovery, predicting drug safety and efficacy, and optimizing clinical trial designs. It can play a vital role in precision medicine by helping in personalized drug dosing, treatment selection, and predicting drug response based on genetic, clinical, and environmental factors. The role of AI in pharmacovigilance, such as signal detection and adverse event prediction, is also promising. The collaboration between clinical pharmacologists and AI experts also poses certain ethical and practical challenges. Clinical pharmacologists can be instrumental in shaping the future of AI-driven clinical pharmacology and contribute to the improvement of healthcare systems.

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

5-chloro-3-(2-(2,4-dinitrophenyl) hydrazono)indolin-2-one: synthesis, characterization, biochemical and computational screening against SARS-CoV-2

Chemical prototypes with broad-spectrum antiviral activity are important toward developing new therapies that can act on both existing and emerging viruses. Binding of the SARS-CoV-2 spike protein to the host angiotensin-converting enzyme 2 (ACE2) receptor is required for cellular entry of SARS-CoV-2. Toward identifying new chemical leads that can disrupt this interaction, including in the presence of SARS-CoV-2 adaptive mutations found in variants like omicron that can circumvent vaccine, immune, and therapeutic antibody responses, we synthesized 5-chloro-3-(2-(2,4-dinitrophenyl)hydrazono)indolin-2-one (H2L) from the condensation reaction of 5-chloroisatin and 2,4-dinitrophenylhydrazine in good yield. H2L was characterised by elemental and spectral (IR, electronic, Mass) analyses. The NMR spectrum of H2L indicated a keto-enol tautomerism, with the keto form being more abundant in solution. H2L was found to selectively interfere with binding of the SARS-CoV-2 spike receptor-binding domain (RBD) to the host angiotensin-converting enzyme 2 receptor with a 50% inhibitory concentration (IC50) of 0.26 μM, compared to an unrelated PD-1/PD-L1 ligand-receptor-binding pair with an IC50 of 2.06 μM in vitro (Selectivity index = 7.9). Molecular docking studies revealed that the synthesized ligand preferentially binds within the ACE2 receptor-binding site in a region distinct from where spike mutations in SARS-CoV-2 variants occur. Consistent with these models, H2L was able to disrupt ACE2 interactions with the RBDs from beta, delta, lambda, and omicron variants with similar activities. These studies indicate that H2L-derived compounds are potential inhibitors of multiple SARS-CoV-2 variants, including those capable of circumventing vaccine and immune responses.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11696-023-03274-5.

Recent advances in multitarget-directed ligands via in silico drug discovery

To combat multifactorial refractory diseases, such as cancer, cardiovascular, and neurodegenerative diseases, multitarget drugs have become an emerging area of research aimed at 'synthetic lethality' (SL) relationships associated with drug-resistance mechanisms. In this review, we discuss the in silico design of dual and triple-targeted ligands, strategies by which specific 'warhead' groups are incorporated into a parent compound or scaffold with primary inhibitory activity against one target to develop one small molecule that inhibits two or three molecular targets in an effort to increase potency against multifactorial diseases. We also discuss the analytical exploration of structure-activity relationships (SARs), physicochemical properties, polypharmacology, scaffold feature extraction of US Food and Drug Administration (FDA)-approved multikinase inhibitors (MKIs), and updates regarding the clinical status of dual-targeted chemotypes.

Approaches, Strategies and Procedures for Identifying Anti-Inflammatory Drug Lead Molecules from Natural Products

Natural products (NPs) have played a vital role in human survival for millennia, particularly for their medicinal properties. Many traditional medicine practices continue to utilise crude plants and animal products for treating various diseases, including inflammation. In contrast, contemporary medicine focuses more on isolating drug-lead compounds from NPs to develop new and better treatment drugs for treating inflammatory disorders such as inflammatory bowel diseases. There is an ongoing search for new drug leads as there is still no cure for many inflammatory conditions. Various approaches and technologies are used in drug discoveries from NPs. This review comprehensively focuses on anti-inflammatory small molecules and describes the key strategies in identifying, extracting, fractionating and isolating small-molecule drug leads. This review also discusses the (i) most used approaches and recently available techniques, including artificial intelligence (AI), (ii) machine learning, and computational approaches in drug discovery; (iii) provides various animal models and cell lines used in in-vitro and in-vivo assessment of the anti-inflammatory potential of NPs.

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI's role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.

Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics

Over the period of the preceding decade, artificial intelligence (AI) has proved an outstanding performance in entire dimensions of science including pharmaceutical sciences. AI uses the concept of machine learning (ML), deep learning (DL), and neural networks (NNs) approaches for novel algorithm and hypothesis development by training the machines in multiple ways. AI-based drug development from molecule identification to clinical approval tremendously reduces the cost of development and the time over conventional methods. The COVID-19 vaccine development and approval by regulatory agencies within 1-2 years is the finest example of drug development. Hence, AI is fast becoming a boon for scientific researchers to streamline their advanced discoveries. AI-based FDA-approved nanomedicines perform well as target selective, synergistic therapies, recolonize the theragnostic pharmaceutical stream, and significantly improve drug research outcomes. This comprehensive review delves into the fundamental aspects of AI along with its applications in the realm of pharmaceutical life sciences. It explores AI's role in crucial areas such as drug designing, drug discovery and development, traditional Chinese medicine, integration of multi-omics data, as well as investigations into drug repurposing and polypharmacology studies.

Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors

Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to accelerate and improve the process of discovering and developing new drugs. The use of AI in drug discovery is still in its early stages, but it has the potential to revolutionize the way new drugs are discovered and developed. As AI technology continues to evolve, it is likely that AI will play an even greater role in the future of drug discovery. AI is used to identify new drug targets, design new molecules, and predict the efficacy and safety of potential drugs. The inclusion of AI in drug discovery can screen millions of compounds in a matter of hours, identifying potential drug candidates that would have taken years to find using traditional methods. AI is highly utilized in the pharmaceutical industry by optimizing processes, reducing waste, and ensuring quality control. This review covers much-needed topics, including the different types of machine-learning techniques, their applications in drug discovery, and the challenges and limitations of using machine learning in this field. The state-of-the-art of AI-assisted pharmaceutical discovery is described, covering applications in structure and ligand-based virtual screening, de novo drug creation, prediction of physicochemical and pharmacokinetic properties, drug repurposing, and related topics. Finally, many obstacles and limits of present approaches are outlined, with an eye on potential future avenues for AI-assisted drug discovery and design.

Physiologically Based Pharmacokinetic Modeling of Extracellular Vesicles

Extracellular vesicles (EVs) are lipid membrane bound-cell-derived structures that are a key player in intercellular communication and facilitate numerous cellular functions such as tumor growth, metastasis, immunosuppression, and angiogenesis. They can be used as a drug delivery platform because they can protect drugs from degradation and target specific cells or tissues. With the advancement in the technologies and methods in EV research, EV-therapeutics are one of the fast-growing domains in the human health sector. Therapeutic translation of EVs in clinics requires assessing the quality, safety, and efficacy of the EVs, in which pharmacokinetics is very crucial. We report here the application of physiologically based pharmacokinetic (PBPK) modeling as a principal tool for the prediction of absorption, distribution, metabolism, and excretion of EVs. To create a PBPK model of EVs, researchers would need to gather data on the size, shape, and composition of the EVs, as well as the physiological processes that affect their behavior in the body. The PBPK model would then be used to predict the pharmacokinetics of drugs delivered via EVs, such as the rate at which the drug is absorbed and distributed throughout the body, the rate at which it is metabolized and eliminated, and the maximum concentration of the drug in the body. This information can be used to optimize the design of EV-based drug delivery systems, including the size and composition of the EVs, the route of administration, and the dose of the drug. There has not been any dedicated review article that describes the PBPK modeling of EV. This review provides an overview of the absorption, distribution, metabolism, and excretion (ADME) phenomena of EVs. In addition, we will briefly describe the different computer-based modeling approaches that may help in the future of EV-based therapeutic research.

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

Computational Modeling on Drugs Effects for Left Ventricle in Cardiomyopathy Disease

Cardiomyopathy is associated with structural and functional abnormalities of the ventricular myocardium and can be classified in two major groups: hypertrophic (HCM) and dilated (DCM) cardiomyopathy. Computational modeling and drug design approaches can speed up the drug discovery and significantly reduce expenses aiming to improve the treatment of cardiomyopathy. In the SILICOFCM project, a multiscale platform is developed using coupled macro- and microsimulation through finite element (FE) modeling of fluid-structure interactions (FSI) and molecular drug interactions with the cardiac cells. FSI was used for modeling the left ventricle (LV) with a nonlinear material model of the heart wall. Simulations of the drugs' influence on the electro-mechanics LV coupling were separated in two scenarios, defined by the principal action of specific drugs. We examined the effects of Disopyramide and Dygoxin which modulate Ca2+ transients (first scenario), and Mavacamten and 2-deoxy adenosine triphosphate (dATP) which affect changes of kinetic parameters (second scenario). Changes of pressures, displacements, and velocity distributions, as well as pressure-volume (P-V) loops in the LV models of HCM and DCM patients were presented. Additionally, the results obtained from the SILICOFCM Risk Stratification Tool and PAK software for high-risk HCM patients closely followed the clinical observations. This approach can give much more information on risk prediction of cardiac disease to specific patients and better insight into estimated effects of drug therapy, leading to improved patient monitoring and treatment.

Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade

Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.

Role of Metal Cations of Copper, Iron, and Aluminum and Multifunctional Ligands in Alzheimer's Disease: Experimental and Computational Insights

Alzheimer's disease (AD) is the most common form of dementia, affecting millions of people around the world. Even though the causes of AD are not completely understood due to its multifactorial nature, some neuropathological hallmarks of its development have been related to the high concentration of some metal cations. These roles include the participation of these metal cations in the production of reactive oxygen species, which have been involved in neuronal damage. In order to avoid the increment in the oxidative stress, multifunctional ligands used to coordinate these metal cations have been proposed as a possible treatment to AD. In this review, we present the recent advances in experimental and computational works aiming to understand the role of two redox active and essential transition-metal cations (Cu and Fe) and one nonbiological metal (Al) and the recent proposals on the development of multifunctional ligands to stop or revert the damaging effects promoted by these metal cations.

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as "message-passing paradigms", "spatial-symmetry-preserving networks", "hybrid de novo design", and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.