Background

Artificial intelligence (AI) is one of the leading and authentic methods in drug discovery, employing various AI-assisted algorithms that help to reduce time and error rates with high accuracy. Drug discovery has faced challenges due to conventional methodologies that have not effectively addressed multiple diseases at a time and were specific. The successful drug design will take an average of 15 years, as this process utilizes various technologies and expertise for therapeutic development [1, 2]. Therefore, researchers developed AI-assisted methods to revolutionize the drug discovery process. AI is one of the most useful applications and applicable in resources, such as apps ranging from recommendations to classifications and diagnostics [3]. The implementation and acclaim of AI-driven developments in computer-aided drug design (CADD) and drug discovery have been acknowledged as the most innovative approach to reshaping conventional strategies in the pharmaceutical sector. CADD is the beginning of the computer-assisted approaches for developing and identifying the most promising compound methodology [2, 4,5,6]. In the process of discovering the drug, utilizing AI-assisted techniques helps mitigate failures among preclinical lead molecules. Before finalizing the most promising one, various properties such as molecular activity, physicochemical properties, pharmacokinetics, and bioactivity are applied and screened at the in silico level. In the AI-assisted drug discovery process, high-tech computer software programs and applications are essential as they can help evaluate and reveal the most promising one using the advanced machine learning (ML) algorithm, including its various fundamental and crucial aspects such as dataset preparation, model generation, and model evaluation etc. [7,8,9,10]. Over the years, in the drug development process, a significant surge in data digitization has occurred within the domain of the development of drugs and primarily utilized in the pharmaceutical sectors the beginning with high accuracy for the novel findings, as it can easily handle bulk data information with enhanced automation [11,12,13]. For successful drug discovery, the identified drug-like compounds must have a promising profile based on the validation; the AI-assisted algorithm can quickly identify the most promising hits and help understand their activity on the drug target by optimizing the structure [1, 14,15,16]. In the present situation, researchers are utilizing several AI-based applications for the drug discovery process. The basic representation of drug discovery is shown in Fig. 1, highlighting the key steps and the integration of AI, which can aid various stages of the process. Each step involves more precise analyses aimed at supporting the development of therapeutics. As AI-assisted studies increase daily, it is essential to understand the basic mechanisms of AI’s involvement and its application in drug design, and how these applications are beneficial. So, this study aims to explore AI in drug discovery and provide a brief idea about the available resources that assist the drug discovery process. Moreover, the pharmaceutical and clinical aspects, along with the critical insight, were discussed. Additionally, the more in-depth insight via a study based on AI-assisted drug design was also discussed, along with the advancement and challenges.

Fig. 1
Fig. 1
Full size image

The representation of basic steps involved in AI-assisted drug discovery

AI-assisted application in drug discovery

In the present situation, AI is involved in several domains, including data collection, classification, dataset generation, and validation, among others, using various algorithms to maintain AI-assisted automation [15, 16]. Based on the different sets of applications, it can easily handle the bulk datasets with enhanced automation. Through consideration of drug exploration, several applications were developed to maintain accuracy and easily identify novel inhibitors for target pathogens. When it comes to drug discovery, things like ML, artificial neural networks (ANNs), deep learning (DL), random forest, support vector machines (SVMs), and deep neural networks (DNNs) are of particular interest to scientists. These algorithms aid in various tasks, such as bioactivity analysis, drug–target prediction, binding affinity calculation, and many more [1, 5]. The ML is a set of AI that can help analyze patterns in data, which is then classified to enhance the accuracy of a task. ANN is a subset of ML that can help solve biological problems regarding data and accelerate and optimize the drug discovery process [17, 18]. Deep learning is one of the most useful subsets of ML in the drug discovery field as it can help recognize the data pattern, easily handle complex data and further classification, and expedite and improve the drug development procedure [16, 19]. Random forest is also a part of ML, which can help to find and predict accurate results in drug design as it utilizes complex data and classifies it as a decision tree at training time, which is helpful to obtain the accuracy level based on the classification [20]. SVM is one of the most important ML-based applications that can help design a predictive model, as it can easily separate data points into different classes for better understanding [21]. Deep neural networks (DNNs) are one of the leading drug design applications. They can easily handle unlabeled and unstructured data based on their multiple hidden layers to represent complex datasets successfully and speed up the drug development procedure while improving the accomplishment rate [5, 22]. Apart from that, these applications have a more significant role in drug discovery, such as small molecule screening, virtual screening, model development, feature selection, quantitative structure–activity relationship (QSAR) modeling, pharmacophore modeling, predicting drug–drug interactions, absorption, distribution, metabolism, and excretion analysis (ADME/T), molecule contact within the target and ligands, the bioactivity and toxicity of analysis of the target compound and inhibitors, data integration and many [5, 16, 23,24,25]. Utilizing the different applications to deal with the drug discovery process, the researchers have applied various applications and successfully performed the study (Refer Table 2; Case study) and found that AI-assisted studies are more impactful due to advanced automation and can accelerate the method of drug development in the coming years for more effective outcomes. Moreover, Fig. 2 summarizes the stages involved, highlighting how different combinations were applied at various steps from raw data collection to final analysis to manage complexity and achieve accurate outcomes effectively.

Fig. 2
Fig. 2
Full size image

Overview of the involved strategy in drug design through the integration of advanced applications

Tools and databases in drug discovery

At present, scientific researchers are keenly interested in drug development using advanced approaches that are easy to understand and offer high accuracy, leading to successful therapeutic outcomes. However, to retrieve, analyze, and understand the data, various tools and databases have been developed to aid therapeutic development. These resources are freely available and can help to retrieve the chemical compound and target sequence and their essential information, as Fig. 3 illustrates the involvement of tools, databases, servers, and various applications that support therapeutics design by generating diverse outputs, which collectively aid the drug discovery process. Also, it is helpful for the different types of biological property analysis, crucial in drug design. The useful tools and databases are represented in Table 1 with their corresponding URLs and remarks.

Fig. 3
Fig. 3
Full size image

Overview of the various approaches in drug design. This figure illustrates how the raw data lead to the significant section, which is eventually helpful for the drug discovery process

Table 1 List of helpful resources in the field of drug discovery, along with the name, URLs,/GitHub, remarks, and their corresponding references
Full size table

To aid and accelerate the drug discovery process, several resources are available that are highly useful and lead to significant outcomes. The navigation, collection, analysis, and interpretation of data rely on these resources, which help researchers overcome various challenges. Advancing the approach through the integration of AI, ML, and other subsets of algorithms is a major part; however, to achieve more accurate results, researchers frequently update versions and enhance datasets to understand complex biological problems. Moreover, these resources were commonly utilized in different steps of the research, which aid the drug discovery process. Concerning drug discovery aspects, several resources are available for sequences, ligands, drug-likeness, and other areas. For example, IMPPAT 2.0, LOTOUS, DrugBank, ChEMBL, and COCONUT can be utilized for compound library generation, aiding in inhibitor design. Deep-PK and ADMETlab 2.0 can be useful for pharmacokinetic aspects, and several other tools are available for use based on the initial input data and study aims. Furthermore, concerning the molecular dynamics simulation, which is vital to analyze the stability of the complex, researchers are exploring insights by integrating ML. Brownless et al. demonstrate the implementation of ML models to MD simulation data [83], and Sodaei et al. highlight in their study the integration of MD simulations with ML [84]. Subsequently, to gain more impactful insight, enhancing resources by integrating AI will help revolutionize the discovery process.

Involvement of AI in drug development: a clinical perspective

AI has emerged as a revolutionary drug development method, streamlining every process from initial screening to experimental trials and post-market investigation. Incorporating AI into clinical settings can significantly accelerate drug development, lower costs, and enhance safety and efficacy. The clinical perspective of AI in drug development is particularly crucial as it directly impacts patient care, regulatory approvals, and the overall healthcare system. With advancements in ML, DL, and natural language processing (NLP), AI-driven approaches have been increasingly applied to enhance decision-making in clinical trials, optimize drug formulations, and personalise treatments based on patient-specific factors. Additionally, AI can integrate huge quantities of biological, pharmacological, and clinical data, enabling a more systematic drug development method [85,86,87]. AI-driven patient recruitment and stratification are transforming clinical trial design by addressing inefficiencies in traditional methods. A prominent example is IBM Watson for clinical trial matching, which leverages natural language processing (NLP) to investigate electronic health records (EHRs) and match patients to trials based on eligibility criteria [88]. This system integrates structured data (e.g., laboratory results, demographics) with genomic biomarkers, such as tumor mutation profiles, to stratify patients into molecularly defined subgroups. ML algorithms, including random forest models, predict patient eligibility and optimize cohort allocation [89]. These advancements emphasize AI’s capacity to harmonize multi-modal data streams, accelerating precision medicine initiatives.

Clinical trial optimization

Clinical trial optimization increasingly relies on predictive analytics and imaging-driven AI tools to enhance efficiency and precision. Like random survival forests and Cox proportional hazards algorithms, ML models enable dynamic risk stratification by analyzing time-to-event data, including adverse drug reactions or disease progression [90]. Adaptive trial designs like the I-SPY 2 trial in breast cancer employ Bayesian ML models to predict treatment response, enabling real-time arm adjustments. This approach reduced required sample sizes by 50% while maintaining statistical power [91]. Similarly, reinforcement learning frameworks optimize dosing schedules by simulating patient responses [92]. Imaging analysis tools like PathAI leverage convolutional neural networks (CNNs) to quantify tumor microenvironment features (e.g., immune cell infiltration, necrosis) from histopathology slides, enabling objective assessment of treatment efficacy. PathAI predicted immunotherapy response in melanoma with 85% accuracy by analyzing spatial patterns of PD-L1 expression [93]. Similarly, QuantX, an FDA-cleared AI platform, standardizes MRI-based tumor volume measurements in glioblastoma trials, achieving 92% inter-rater reliability compared to manual radiologist assessments [94]. These tools integrate with NLP pipelines to extract latent biomarkers from radiology reports, enhancing predictive models for patient stratification. By automating endpoint quantification and reducing inter-observer variability, AI-driven imaging accelerates trial timelines while improving reproducibility.

Drug repurposing and formulation

AI significantly enhances drug repurposing efforts by systematically analyzing molecular interactions within extensive biomedical datasets. An illustration is BenevolentAI’s discovery of baricitinib, a Janus kinase-signal transducer and activator of transcription (JAK-STAT) inhibitor originally licensed for rheumatoid arthritis as a potential COVID-19 treatment. By constructing a knowledge graph that integrated SARS-CoV-2 viral entry mechanisms with host inflammatory pathways, their algorithm identified baricitinib’s dual mechanism: suppression of viral endocytosis through AP2-associated protein kinase-1 (AAK1) inhibition and attenuation of cytokine storm pathology. Subsequent clinical evaluation in the Adaptive COVID-19 Treatment Trial-2 (ACTT-2) demonstrated a 38% mortality reduction [95,96,97].

Pharmacovigilance and post-market surveillance

AI-driven pharmacovigilance utilizes natural language processing (NLP) to extract real-world evidence (RWE) from electronic health records (EHRs) and digital patient communications. The FDA Sentinel Initiative exemplifies this approach, employing transformer-based architectures such as bidirectional encoder representations from transformers (BERT) to identify adverse drug events (ADEs) from unstructured clinical documentation. This methodology has demonstrated a 40% improvement in detection rates compared with traditional manual surveillance systems [98, 99]. Social media platforms are concurrently analyzed through sentiment recognition algorithms to detect patient-reported symptoms. However, notable limitations persist: A 2021 evaluation revealed that 30% of AI-flagged Twitter posts contained non-actionable content due to sarcasm or irrelevance, underscoring the necessity for human verification [100].

Personalized medicine and biomarker discovery

AI platforms such as DeepOmics are revolutionizing personalized medicine by integrating various omics-related data (genomics, transcriptomics, proteomics, metabolomics) to identify biomarkers that can predict patient response to immunotherapy. Moreover, Fig. 4 illustrates the navigation through omics insights, which further leads to personalized plans. These platforms utilize advanced ML algorithms to investigate bulk datasets and reveal synchronicity that human researchers might miss [101,102,103]. By identifying specific biomarkers associated with treatment response, healthcare personnel can fit treatments to specific patients, increasing the likelihood of successful outcomes. Digital twins, in retrospect, play a crucial role in simulating treatment outcomes by creating virtual replicas of individual patients based on their unique biological characteristics. By simulating different scenarios and treatment strategies, digital twins enable personalized treatment plans tailored to each individual’s precise requirements and characteristics [104]. This innovative approach upholds a great possibility for refining the patient’s outcome and helping revolutionize personalized medicine.

Fig. 4
Fig. 4
Full size image

Personalized medicine and biomarker discovery. The figure illustrates the insight through the multi-omics based on their involvement in the data and biomarker finding and leads to personalized plans

Involvement of AI in drug development: a pharmaceutical sector perspective

AI is changing medicine development, which will revolutionize the pharmaceutical industry. From discovery to market approval, the normal drug development pipeline takes over 10 years and costs over $2 billion. With just 10% of new chemical entities receiving regulatory clearance, their success rate is low [105,106,107]. Drug development may be streamlined using AI to discover novel targets, enhance lead compounds, and accelerate clinical trials. AI-driven ML algorithms may identify synchronicity and linkage in large datasets that human researchers cannot, speeding up drug discovery. The effectiveness and safety of small chemicals can be predicted by reducing late-stage trial failures [107,108,109]. AI’s involvement in the development of drugs and its potential and limitations must be assessed as the pharmaceutical industry evolves. Significant limitations, including high failure rates, protracted timelines, and substantial costs, hinder the traditional drug development process [1, 86]. To address these challenges, innovative solutions are required to improve the efficiency and effectiveness of drug development. AI recently surfaced as a promising technological advancement that could revolutionize the drug development paradigm. By leveraging AI, researchers can work with bulk data, identify synchronicity, and potentially predict the outcomes, thereby speeding the target identification process, lead optimization, and clinical trial design [105, 110, 111]. Ultimately, integrating AI in drug development can facilitate the discovery, development, and delivery of novel therapeutics, improving patient outcomes and transforming the future of healthcare [110, 112]. Moreover, Fig. 5 demonstrates the insight from the process to solution, followed by the other aspect, which is essential to understand the drug development process precisely.

Fig. 5
Fig. 5
Full size image

Traditional drug development (process, limitations, and challenges) and innovative solutions. The figure illustrates the major insight based on the individual aspects of classification of the traditional drug design

AI applications perspective

Integrating AI in drug discovery and development has transformed the pharmaceutical sector, enabling the rapid identification and creation of innovative therapeutic solutions. Among the diverse AI methodologies employed, ML is pivotal in analyzing vast datasets, recognizing outlines, and producing predictive models that accelerate drug discovery [7, 110, 113]. For example, ML algorithms can assess the binding affinities of small molecules to specific targets, thereby enabling the identification of lead compounds [114, 115]. Building on this, DL, a subdivision of ML, has been established as particularly applicable in deciphering complex biological information, like genomic and transcriptomic profiles, enhancing the precision of drug–target interactions [22, 89, 116]. Subsequently, NLP is emerging as a critical tool for extracting relevant data from extensive literature and experimental data. This capability aids in identifying novel drug targets, therapeutic applications, and potential safety concerns, thereby streamlining the drug development pipeline [117, 118]. Furthermore, computer vision has been increasingly used to analyze data from clinical imaging, such as X-rays and MRI, to identify specific biomarkers and disease characteristics. This approach supports the progress of therapies tailored to patient’s needs [119, 120]. These AI methodologies, ML, DL, NLP, and computer vision, have reportedly improved drug development’s efficacy, accuracy, and consistency. By integrating these advanced tools, the pharmaceutical industry is poised to expedite the discovery and formulation of innovative, effective, and safe treatments. These transformative potentials emphasize the profound impact of AI on advancing human health and improving quality of life.

AI-driven innovations from target identification to regulatory submission

The utilization of AI has notably transformed the medical sector, optimizing and improving many drug screening and development phases. Target identification and validation are crucial domains where AI has shown substantial influence. ML algorithms examine large datasets, encompassing genomic, proteomic, and disease pathway data, to discern prospective treatment targets and forecast their effectiveness [112, 114, 121]. AI-driven genomic data analysis has identified new targets for complex illnesses, including cancer and Alzheimer’s [96, 122]. Furthermore, AI models estimate the binding affinity of tiny molecules to various targets, hence aiding in identifying potential lead compounds [113, 114]. In lead optimization and drug design, AI has proven invaluable. Deep learning (DL) algorithms generate novel compounds with enhanced efficacy and safety profiles, significantly reducing development timelines [19, 116]. For instance, AI-optimized lead compounds have been developed for diseases such as MPOX virus and malaria, exhibiting improved potency and reduced toxicity [112, 123]. Furthermore, AI predicts these compounds’ pharmacokinetic and pharmacodynamic properties, aiding in their optimization and selection for further development. In preclinical testing, AI models analyze large datasets to predict compound toxicity, minimizing dependence on animal testing and speeding the development of safer therapeutics [96, 109, 112]. AI also has a major part in experimental trial design and patient recruitment. ML algorithms evaluate EHR and genetic data to identify suitable participants, optimize trial protocols and improve recruitment efficiency [111, 113]. Moreover, AI predicts clinical trial outcomes and patient responses, enabling personalized treatment strategies. In regulatory processes, natural language processing (NLP) tools streamline the analysis and generation of regulatory documents, reducing administrative burdens and expediting approvals [96, 117, 118]. Notable examples of AI-driven success in drug development include in silico Medicine’s identification of a novel fibrosis target, which led to the discovery of a potential therapeutic candidate [124]. Exscientia’s AI-designed molecule DSP-1181, a serotonin 5-HT1A receptor agonist for obsessive–compulsive disorder, entered clinical trials in record time [97, 125]. Additionally, recursion pharmaceuticals leveraged AI to identify drug repurposing opportunities for rare diseases [96]. These examples underscore AI’s transformative potential in accelerating drug development, improving safety, and delivering targeted therapies for unmet medical needs.

Pharmaceutical sector perspective: benefits, challenges, and future scope

Adopting AI in the pharmaceutical sector offers transformative benefits but presents significant challenges (Fig. 6), highlighting the potential impact. AI boosts efficiency and productivity by automating tasks like data analysis, target identification, and compound screening [5, 112, 113]. AI platforms like BenevolentAI have cut drug discovery time from years to just months. AI models improve decision-making and risk assessment by analyzing complex data to predict drug effectiveness, toxicity, and clinical trial results, helping to reduce expensive late-stage failures [112, 123, 126]. AI speeds up finding and creating new treatments by spotting new drug targets and improving lead compounds. A great example is Exscientia’s AI-designed molecule DSP-1181, which moved into clinical trials faster than ever. AI makes healthcare more personal by assessing an individual’s data to customize therapy, as shown by platforms like recursion pharmaceuticals [125, 127]. However, incorporating AI in the medical sector faces several obstacles (Fig. 6). Notably, the scarcity of high-quality and large-scale data, which are often fragmented or proprietary, poses a significant barrier to AI model development [107, 108]. Furthermore, the swift evolution of AI technologies outperforms the progress of homogenous regulatory outlines and validation guidelines, creating additional challenges for AI adoption in drug development. Intellectual property, data security, and talent acquisition/retention concerns impede AI integration [107, 112, 128]. To overcome these hurdles, industry leaders emphasize the importance of cross-industry collaboration and strategic investment in AI capabilities. Partnerships between pharmaceutical companies and technology giants (e.g., Microsoft) demonstrate the value of collaborative efforts in integrating AI into drug discovery processes. Similarly, investments in AI-driven platforms (Pfizer, Chemistry42, PandaOmics) highlight the potential of AI as an enhancer in investigational trials and patient enrollment. Ultimately, while AI adoption in the pharmaceutical sector presents challenges, its transformative potential for drug development and patient care necessitates continued focus and investment [96, 101, 102, 129].

Fig. 6
Fig. 6
Full size image

AI adoption in the pharmaceutical sector. The figure illustrates how the involvement of AI has enhanced the pharmaceutical sector and has limitations

Case studies: AI involvement stories in pharmaceutical companies

Prominent case studies, such as Exscientia and BenevolentAI, illustrate the effective incorporation of AI in drug development. Exscientia, an innovative pharmaceutical firm, has utilized ML and DL algorithms to improve drug candidate selection and refine drug development. Exscientia significantly reduced the duration of preclinical drug discovery from years to months by employing AI-driven platforms [96, 101, 102, 130]. The company’s partnership with Sumitomo Dainippon Pharma led to the inaugural AI-designed drug candidate commencing clinical trials after 12 months, illustrating AI’s capacity to expedite drug development schedules. BenevolentAI has effectively incorporated AI into its drug development pipeline via approaches such as NLP and ML. The company’s technology evaluates extensive biological data, encompassing scholarly literature and clinical trial information, to discover new drug candidates and repurpose current medications for alternative uses [112, 123, 126]. In an exemplary situation, baricitinib is recognized as a prospective treatment for severe COVID-19, according to its capacity to obstruct viral entrance into host cells [95]. This finding resulted in clinical trials and eventual approval by regulatory bodies, underscoring the significance of AI in expediting medication repurposing initiatives. The insights derived from these case studies highlight the necessity of amalgamating domain expertise with AI technology, promoting multidisciplinary cooperation, and guaranteeing rigorous validation of AI-generated conclusions. Pharmaceutical businesses must define explicit objectives for AI deployment, invest in robust data infrastructure, and provide openness in algorithmic decision-making processes [130]. By using these best practices, organizations may reduce the risks linked to AI adoption and leverage its revolutionary potential. AI-driven methodologies have effectively improved drug development efficiency, precision, and creativity, presenting a hopeful outlook for the medical division. As the industry progresses, AI is anticipated to have a more substantial role in influencing the impending drug development process. The strategic amalgamation of AI can transform the healthcare division, facilitating the fast creation of innovative, effective, and safe treatments that enhance human health and quality of life. AI and future technologies can improve drug development. AI and blockchain technology can improve clinical trial data quality and traceability, enabling secure and transparent sharing of sensitive data [107, 131]. The Internet of Things (IoT) allows real-time patient health nursing, improving AI models and tailoring treatment techniques. Oncology and uncommon disorders are promising areas for AI use. AI can analyze complex information and find subtle patterns to speed up the development of new therapies for these difficult ailments [96, 126]. In drug development, explainable and transparent AI models that build pharmaceutical industry trust are needed. The lack of interpretability in modern black-box AI systems inhibits regulatory and healthcare practitioner acceptability [97, 108, 132]. Thus, interpretable AI models like symbolic reasoning or rule-based systems are essential for accountability and human supervision. AI can expedite pharmaceutical manufacturing and supply chain management, cut costs, and improve product quality. Automation and predictive analytics improve procedures, reduce waste, and deliver drugs on time. To use AI’s disruptive potential in drug discovery, pharmaceutical companies should invest in robust data infrastructure and diverse knowledge and collaborate with technological and academic institutions (Fig. 7). AI-driven innovations should be integrated into regulatory frameworks, stressing openness and ethics in algorithmic decision-making. Researchers should address data privacy, bias reduction, and model validation to make AI systems reliable and fair. By working together, pharmaceutical ecosystem stakeholders may improve medicine research, development, and delivery for global patients. AI, combined with new technology, might revolutionize the pharmaceutical industry by enabling the quick creation of creative, effective, and safe medications that improve human health and quality of life. AI is expected to become more important in drug development, necessitating stakeholder engagement to harness its promise and overcome new difficulties.

Fig. 7
Fig. 7
Full size image

Recommendations for pharmaceutical companies, regulatory agencies, and researchers to harness the potential of AI in drug development

AI-assisted case study in drug discovery

Due to advanced automation based on computer-assisted drug design, worldwide researchers are keenly interested in drug discovery. Several steps, such as data retrieval, dataset generation, descriptor calculation model development, optimization, validation, and finding a potential hit through the virtual screening and molecular docking analysis to comprehend the molecular activity with the target and identified molecules, are the essential steps that are most employed in the process to discover potential key molecules[133,134,135]. Also, to confirm whether the identified molecules have promising profiles, the researchers are performing the drug-likeliness analysis, so it will be helpful to determine whether the identified compound has a significant function and can inhibit the corresponding target [5, 136]. This section will help us understand how worldwide researchers are utilizing different algorithms to successfully identify potential compounds that can be helpful for therapeutic development, as shown in Table 2.

Table 2 List of selected case studies that employed an advanced AI approach
Full size table

Exploring and utilizing the advanced computational steps, along with the integration of AI, is revolutionizing the drug discovery process. Subsequently, the researchers are employing these approaches, yielding significant outcomes in different emerging, re-emerging, and ongoing pathogens and associated complications.

Zhang et al. employed various ML methods to formulate a classification models that assist in acetyl-CoA carboxylase inhibitor (ACC) identification, and based on the subsequent steps and overall findings, suggest that the employed strategy is significant based on the identification of promising hits [137]. Similarly, Che et al. conducted a study on Interleukin-1 receptor-associated kinase-1 (IRAK1), which is crucial in inflammation and autoimmune diseases, to identify novel inhibitors. Moreover, with the integration of AI and other applications, this study aims to improve the IRAK1-VS protocol, and based on the overall findings, it suggests that the involvement of AI can yield in the VS strategy [23].

Subsequently, the African swine fever virus (ASFV) attacks domestic pigs of all ages and causes a fatal viral disease. No vaccines or antiviral medications are available, and there have been no reports of human damage. Concerning this, Choi et al. employed docking and ML models, along with subsequent steps, to find ASFV inhibitors and also conducted in vitro investigations on the final top complex. The overall employed strategy in this study can be a promising step toward the ASFV antiviral agent identification [138]. Based on recent emerging and re-emerging viral infections, researchers found that repurposing approved drugs can lead to therapeutic development and help reduce infection. Concerning this, Delijewski et al. employed AI-based drug discovery and identified the best promising repurposing candidates by examining FDA-approved compounds. Based on the employed subsequent steps and validation, the study suggests zafirlukast as a promising candidate [24].

Moreover, an unbalanced expression may lead to severe complications; therefore, Narendra et al. targeted Aldehyde dehydrogenases (ALDHs) by employing different ML models and subsequently took a step to find the selective ALDH1A1 inhibitors. Further study suggests that the identified diverse scaffolds-based inhibitors are promising and can serve as novel scaffolds for ALDH1A1 inhibitors [139]. Similarly, Tang B et al. employed a deep Q-learning network with a fragment-based pharmacological design approach to detect innovative covalent inhibitors targeting SARS-CoV-2, considering the other essential steps, and also suggested that the ADQN–FBDD and related pipeline can be utilized for other emerging diseases through structure-based drug discoveries [140]. Focusing on the different ML models concept algorithm, Raju et al. performed a study to identify selective CYP1B1 inhibitors via different ML models, along with other subsequent steps [14]. Similarly, another study by Nguyen et al. employed an ML-based model to analyze the effects of O. basilicum essential oils on the breast cancer cell line MCF-7, assessing their inhibitory properties. Based on the employed different steps and validation, the author suggests that the designed model can potentially screen the bioactive compounds, and the identified compounds can lead to breast cancer treatment [141]. A prevalent metabolic disorder among people who have both type 1 and 2 diabetes, diabetic neuropathy affects approximately 463 million adults in 2019, and the cases were increasing day by day; to overcome this, Kashyap et al. built the 3D CNN prediction models to evaluate the Aldose reductase (ALR2) inhibitors, considering the various steps [142]. Moreover, Patel et al. employed an AI-based DeepRepurpose framework to identify potential inhibitors of the Monkeypox virus (MPXV), which is a recently emerged viral infection. Based on the subsequent steps employed, the overall finding shows that Elvitegravir is a promising inhibitor; however, experimental validation is needed to ensure efficacy [143]. Following the recent trend, Chikhale et al. focused on generative AI and subsequent steps to explore new EthR ligands. In this study, the authors utilized the REINVENT4, which is a GenAI tool; moreover, the authors suggest that the designed framework can be applied to other drug resistance-based complications [135]. Similarly, concerning the drug resistance mechanism, Garg et al. employed the AI-ML along with an in silico approach to find anti-TB compounds. In this study, the authors initially employed various in silico steps and further integrated the AI-ML to validate the findings. Moreover, the authors suggest that further experimental validations are required [144]. Another study by Suliman et al. [145] focused on the derivative insights of pyrazolone, employing AI-generated derivatives to overcome fungal infections. Based on various validation steps, the study suggested a therapeutic candidate that could be used to address the infection, and also emphasized the need for future experimental validation to confirm the outcomes [145]. Colorectal cancer (CRC) is one of the ongoing complications in infected individuals, and to make an effort toward the therapeutic design, Ali et al. targeted RPS20 to identify the plant-derived compound as a promising inhibitor. In this study, the author initially identified Indirubin as a promising candidate and, further, through AI integration, enhanced its activity in 13 different ligands and further evaluated its activity with the docking and subsequent steps. The overall study shows the AI-enhanced Indirubin derivative has superior binding affinity and also suggests that further experimental evaluation is required to ensure efficacy and safety [146]. As researchers in different therapeutic design steps are utilizing the integration of AI, Vaidya et al. have taken a further extension and framed an AI/ML-based predictive algorithm “Anti-EBV” to identify promising antivirals against the Epstein-Barr virus (EBV). In this investigation, the author designed the “Anti-EBV” web server, which is the first platform dedicated to identifying antivirals against EBV [147].

At present, the different algorithms of AI, ML, and DL integrating with the in silico approach are being utilized by researchers worldwide to address ongoing severe complications and improve therapeutic efficacy and novel drug identification. The employed different algorithms in successful design studies suggest and revealed the other significant roles of AI, such as to deal with the bulk dataset, design pipeline and framework to screen the promising compounds, design models to evaluate the efficacy, using framework for antiviral against the viral infection, employing combined algorithms that can be helpful to validate the compounds which may assist in the more accurate finding and many more. Although the use of AI is beneficial, it still has certain limitations. Further improvements are needed in model accuracy, streamlined steps and pipelines for handling bulk datasets, accurate compound prioritization, and the generation of more meaningful hypotheses, ultimately enhancing the drug development process.

Advancement and challenges

Compared to the conventional method, advancements in drug design using AI and algorithms are being utilized by researchers worldwide to make outcomes more accurate. The developed drug must be specific and bind to the target to successfully treat the infection. With the advancement of AI, researchers can easily find potential drugs among the bulk dataset, which can easily bind to the target proteins and genes associated with specific diseases. Based on that information, the researchers can quickly begin the drug discovery, leading to successful inhibitor identification [2, 6, 148]. Although complications from many diseases and pathogens continue to emerge, significant advancements have been made in therapeutics, with several drugs already available and others currently undergoing clinical trials. Using the advanced AI drug repurposing approach, researchers can easily find the existing approved drugs that may be useful for treating emerging and re-emerging diseases [143, 149, 150]. To transform and accelerate the drug discovery process, numerous databases containing chemical compounds, natural products, and approved drugs comprising millions of entries are freely available. By integrating AI-assisted models, researchers can rapidly identify the most promising candidates from these large datasets and analyze their biological activities using various computational models and pipelines. Compared to conventional methods, AI-assisted drug design facilitates the identification of the most promising inhibitors through high-throughput virtual screening, allowing researchers to rapidly prioritize candidates based on generated molecular attributes and key biological information [10, 17, 151]. Furthermore, the introduction of different AI-based algorithms and pipelines has made structure modeling easier, thereby facilitating de novo structure design. This has specifically aided the field of personalized medicine, where drugs can be tailored with preferred characteristics [152, 153]. To manage vast datasets, various cloud-based storage systems have also been introduced, which not only facilitate data storage but also support efficient data analysis [87, 154]. Subsequently, the introduction of AI has made drug discovery more efficient as it can integrate the data from different fields, enabling comprehensive analysis of complex biological data and improving the accuracy of predictions, thereby enhancing the efficacy of the drugs [155].

Although various algorithm-based advancements have significantly aided drug discovery, several challenges remain that need to be addressed to further improve therapeutic development. An AI-assisted study requires the most advanced high-tech computer system as it needs to deal with the bulk dataset, which generates the need for more money for the successful development of drugs; also, at the same time, it requires a person who is skilled with the generated data for further leads [7, 116]. However, the promising and successful treatments of the infected patient’s dataset generate concerns about the privacy aspects of the patients. Also, AI-assisted drug development requires a massive amount of data, but the retrieved data is often incomplete, which poses challenges for successful model development. The transparency of the model is another major challenge, as most AI models lack interpretability, making it difficult to understand the reasoning behind the generated results. Various models have also been shown to be biased and known to produce incorrect predictions [156, 157]. Subsequently, there remains a gap between in silico and in vivo studies, which may not align with computational findings [158]. Additionally, the models are often limited to specific datasets, and when provided with unfamiliar data, they may fail to generate accurate results, thereby hindering analysis [133, 159]. Lastly, ethical and regulatory concerns must also be addressed, as most models do not clearly define ownership and accountability for their applications [134]. Despite the challenges, the integration of AI in drug discovery continues to advance rapidly. Researchers worldwide are enhancing interpretation, validation, and the quality and quantity aspects, as these are essential for successful therapeutics development.

Critical analysis of challenges and limitations in AI-assisted drug discovery

AI, along with its subsets ML and DL, provides substantial efficiency in drug discovery. However, the broader implementation and successful translation of these technologies face multiple interrelated challenges that require thorough analysis.

Data scarcity, quality, and generalizability

The efficacy of any deep learning or machine learning model is fundamentally contingent upon the quality, quantity, and consistency of the training data. Biomedical data are often fragmented, proprietary, or inconsistent among various laboratories and institutions. This constraint frequently compels models to be trained on limited datasets, resulting in high effectiveness for specific tasks (e.g., predicting toxicity for a restricted chemical class) but increasing the likelihood of failure in generalization when applied to new chemical spaces or complex, polygenic diseases. The absence of standardized data protocols undermines the robustness and reproducibility of insights derived from AI. Addressing this necessitates a collective commitment across the industry to ensure high-quality data curation, promote open-science initiatives, and establish standardized metadata practices.

The interpretability and “Black-Box” dilemma

When very complicated models like deep neural networks (DNNs) are used successfully, they often provide “black-box” outputs that are hard to understand. Although these models attain considerable predictive accuracy, researchers encounter substantial challenges in data interpretation due to the complexity of the generated outputs, which complicates the identification of the mechanical explanation underlying a prediction. This absence of explainable AI (XAI) constitutes a significant impediment, not only for scientists requiring mechanistic reasons to inform synthesis and optimization but also for regulatory entities, like the Food and Drug Administration (FDA). Regulators usually want explicit, mechanistic proof before they approve a drug. This makes it hard for clinical and market translation to use AI models that can’t be understood.

Economic and regulatory hurdles

As the current publication emphasizes, AI-assisted research necessitates sophisticated high-tech computer systems and proficient individuals to manage extensive datasets, hence increasing the overall cost demands for medication development. Also, the rapid growth of AI technology is now exceeding the growth of unified regulatory frameworks. This delay in regulation makes it hard for businesses to invest in the industry. In addition to concerns about patient data privacy and the challenges of owning intellectual property (IP) for newly created compounds, these issues require immediate global action to establish clear ethical, legal, and operational standards for fully harnessing AI in the process.

Conclusion

Integrating AI in drug discovery is revolutionizing the process in terms of cost and time. As several emerging and re-emerging infections occur daily, enormous progress has been made employing AI-assisted automation in the drug development method to find potential leads. Also, due to their accuracy, several tools and databases play a vital role in drug discovery, from data retrieval to optimization. Advancements in these tools and databases can significantly reduce time and cost, as manual methods are tedious and time-consuming. Moreover, incorporating AI in clinical processes leads to improved accuracy through the various stages. However, the AI in drug design still has some limitations and challenges, such as data privacy and ethical concerns; advancements in drug discovery can address these issues effectively. This study compiles numerous details of AI in drug discovery. It offers understandings into its key steps, real-time applications through case studies, involvement in clinical trials, pharmaceutical aspects, and associated limitations. This comprehensive approach aims to help researchers understand AI’s critical stages and contributions to drug development.