Transformative Power of Artificial Intelligence and Robotics in Drug Discovery and Development: Current Landscape and Future Frontiers

Mahendra Vaniya

Abstract

The convergence of Artificial Intelligence (AI) and robotics is transforming the pharmaceutical landscape by streamlining drug discovery, development, and delivery processes. This review highlights the role of AI methodologies—including machine learning (ML), deep learning (DL), natural language processing (NLP), reinforcement learning (RL), and generative AI—in accelerating target identification, molecular design, toxicity prediction, and clinical trial optimization. Robotics further enhances these advancements through high-throughput screening (HTS), automated sample processing, and smart drug delivery systems, including nanorobotics. Tools such as AlphaFold, DiffDock, DeepPurpose, and generative platforms like INS018_055 illustrate real-world progress in designing, predicting, and validating drug candidates. Despite these technological breakthroughs, major challenges persist: limited data quality, algorithmic bias, lack of interpretability, high implementation costs, and ethical concerns surrounding data privacy and regulatory compliance. Recent developments, including large language models (LLMs), retrieval-augmented generation (RAG), state space models (SSMs), and multi-omics integration, are addressing these limitations and pushing the boundaries of precision medicine. Real-world applications demonstrate AI’s potential in repurposing existing drugs (e.g., Baricitinib for COVID-19), optimizing clinical trial recruitment and design, and enhancing personalized therapeutic strategies. The integration of AI and robotics offers a transformative framework for accelerating pharmaceutical innovation, reducing costs, and improving global healthcare outcomes. Sustained progress will require robust regulatory frameworks, ethical oversight, and interdisciplinary collaboration between academia, industry, and health authorities.

Keywords: Robotics Artificial intelligence; Intelligent pharmacy; Drug discovery and development; Machine learning

1. Introduction

Medicate revelation may be a complex, time-intensive, and expensive handle, regularly tormented by a tall rate of whittling down. On normal, creating a unused sedate can surpass $2.5 billion in taken a toll and take more than 10 to15 a long time to reach the advertise (1). Moreover, less than 10% of sedate candidates that enter clinical trials eventually get administrative endorsement, and as it were approximately 2% of all sedate disclosure ventures lead to a effectively promoted pharmaceutical item (2). These figures emphasize the critical require for imaginative and more effective techniques to make strides victory rates and decrease the in general burden of conventional medicate development.

Artificial Insights (AI) has emerged as a transformative drive, particularly within the healthcare and pharmaceutical spaces, advertising devices that can streamline forms and upgrade results. AI innovations, counting machine learning (ML), profound learning (DL), and normal dialect preparing (NLP), are presently being utilized to address basic bottlenecks over medicate revelation and improvement pipelines (3).

Traditional medicate advancement workflows are regularly ruined by a few challenges: the distinguishing proof of appropriate sedate targets, which is regularly complex and data-intensive; high-throughput screening (HTS) strategies that are expensive and require broad assets; lead compound optimization, which includes iterative blend and testing for viability, selectivity, and security; and at long last, the plan and execution of clinical trials, which confront challenges in quiet enrolment, real-time information collection, and result investigation (4). These roadblocks contribute essentially to tall disappointment rates and swelled costs within the pharmaceutical sector.

This audit points to investigate the significant part of AI in changing the scene of medicate revelation and advancement. Particularly, it analyses how present day AI techniques€ including ML, DL, NLP, Fortification Learning (RL), Chart Neural Systems (GNNs), and Generative AI (Gen-AI)€are being actualized over different stages of the sedate advancement lifecycle. These stages incorporate target distinguishing proof, lead revelation, structure-based medicate plan, poisonous quality expectation, and clinical trial optimization (5). By streamlining these stages, AI holds the potential to altogether quicken timelines, decrease improvement costs, and upgrade the victory rates of unused helpful presentations to the worldwide showcase.

2. Outline of Artificial Intelligence in Sedate Disclosure and Development

Artificial Intelligence (AI) envelops a wide run of computational procedures that empower machines to imitate human cognitive capacities such as learning, thinking, and decision-making (9). Among its key subfields, Machine Learning (ML) empowers frameworks to make strides execution over time by learning from information without being expressly modified for each particular assignment (10). Profound Learning (DL), a specialized subset of ML, utilizes multi-layered Counterfeit Neural Systems (ANNs) to recognize complex information designs and extricate significant experiences (11).

Neural Systems (NNs), propelled by the structure and work of the human brain, comprise of interconnected hubs organized in layers that prepare and transmit data, empowering assignments such as classification, relapse, and forecast (12). These systems frame the foundational engineering for both ML and DL frameworks utilized broadly in pharmaceutical research.

In the setting of medicate revelation, AI procedures give a few key points of interest over ordinary strategies. Machine learning calculations, such as Back Vector Machines (SVMs) and Irregular Timberlands (RFs), are commonly utilized for drug target interaction forecast, compound screening, and biomarker revelation (13, 14). Profound learning designs, especially Convolutional Neural Systems (CNNs), are viable in de novo medicate plan, image-based phenotypic screening, and forecast of pharmacological properties, counting drug protein official liking and absorption, distribution, metabolism, Excretion,€ toxicity (ADMET) profiles (16,17).

Moreover, Chart Neural Systems (GNNs) have as of late picked up footing in modelling chemical structures and atomic intuitive by speaking to drugs and targets as charts, in this way making strides forecast exactness in assignments such as atomic property estimation and authoritative location (18). In the meantime, Generative AI (Gen-AI) models€ including vibrational auto encoders (VAEs) and generative ill-disposed systems (GANs)”are being utilized to plan novel drug-like atoms and create assorted chemical libraries for preparing AI frameworks (19).

Figure 1 outlines the integration of AI strategies over different stages of sedate disclosure, from target distinguishing proof to harmfulness appraisal. These innovations are not as it were improving forecast execution but moreover quickening timelines and diminishing costs, eventually making pharmaceutical inquire about more productive and data-driven.

Fig.1. Artificial intelligence in drug discovery and development: transforming challenges into opportunities

Overview of Artificial Intelligence and its key components. The core components of AI, including Machine learning, Deep learning, Neural networks, Natural language processing, Molecular fingerprinting, Graph neural network and Generative AI and their interconnections in AI-driven systems (26)

3. Applications of AI in Sedate Disclosure and Development

3.1 Target Recognizable proof and Validation

Artificial Intelligence (AI) has changed sedate target distinguishing proof by joining different natural datasets. By analysing genomic, proteomic, and phenotypic information, AI frameworks can distinguish novel helpful targets more productively than customary strategies (27). For occurrence, AI calculations can analyse quality expression information to distinguish disease-related hereditary varieties and pinpoint potential medicate targets (28). Essentially, protein structures and interaction systems can be mined utilizing profound learning models to uncover proteins included in neurotic pathways and evaluate their drug ability (29).

Moreover, AI models can combine numerous databases such as Medicate Bank, PubChem, Anti-microbial Combination Database (ACDB), and Anti-microbial Adjuvant Database (AADB) with clinical trial information and electronic wellbeing records to estimate restorative potential (30). Characteristic Dialect Preparing (NLP) and machine learning (ML) algorithms particularly profound learning (DL) are crucial for decoding complex natural designs which will escape human translation (31).

Supervised ML models, counting Bolster Vector Machines (SVM) and arbitrary timberland calculations, have been utilized to interface quality expression designs with infection vulnerability (32). Unsupervised learning, such as clustering and dimensionality lessening, encourages the revelation of novel biomarkers and infection subtypes (33). Repetitive Neural Systems (RNN) and Convolutional Neural Systems (CNN) have been effectively connected to foresee results in high-throughput datasets, such as SARS-CoV-2 fundamental protease (Mpro) inhibitors and hERG cardiotoxicity indicators (34).

3.2 Medicate Screening and Lead Discovery

AI-driven virtual screening has revolutionized lead compound recognizable proof. In silico models can quickly assess tremendous compound libraries, essentially quickening the screening handle whereas lessening test costs (35). ML procedures, such as QSAR modeling, anticipate organic action based on atomic descriptors and structures, making a difference prioritize compounds for union and testing (28).

These computational models, when prepared on huge chemical datasets, can recognize unobtrusive highlights connected to viability, subsequently making strides the distinguishing proof rate of strong lead compounds (36). AI applications in virtual screening in this way have the potential to boost medicate improvement victory rates by streamlining hit-to-lead pipelines.

3.3 Medicate Optimization and Design

AI has too reshaped medicate optimization by foreseeing and improving drug-like properties such as dissolvability, steadiness, and bioavailability. ML models, prepared on thousands of chemical substances, can appraise physicochemical parameters with tall accuracy (37). For case, DL models utilizing GANs and auto encoders have been utilized to create novel particles with optimized restorative profiles (38).

Databases like UniProt and the Protein Information Bank (PDB) supply wealthy protein grouping and structure data, which bolsters preparing of AI models for protein work expectation and sedate authoritative optimization (39). These approaches assist the plan of more compelling and more secure sedate candidates with way better clinical outcomes.

3.4 Preclinical and Clinical Development

AI models contribute to preclinical stages by anticipating ADME (retention, dispersion, digestion system, excretion) properties and harmfulness profiles. ML-based re-enactments can precisely figure pharmacokinetic behavior and distinguish off-target impacts early in advancement (40). For occurrence, DL systems are proficient at modeling film porousness and foreseeing sedate dispersion elements over tissues (41).

AI has too revolutionized clinical trials. Calculations can be utilized to optimize trial plan by analyzing chronicled clinical information to choose endpoints, stratify quiet bunches, and decrease test estimate without compromising measurable control (42). AI-assisted enlistment stages make strides member coordinating and engagement through data-driven focusing on methodologies (43).

In expansion, real-time observing of clinical trial information is presently conceivable utilizing AI models, empowering early location of antagonistic occasions and trial alterations. This energetic criticism circle permits for measurements alterations and arm expansions, progressing security and quickening trial movement (46).

Fig.2. Artificial intelligence in drug discovery and development: transforming challenges into opportunities

Applications of Counterfeit Insights in Sedate Disclosure. The different AI-driven approaches in medicate revelation incorporate target distinguishing proof and approval, virtual screening, medicate optimization and plan, and preclinical development.(26)

4. AI Procedures in Sedate Discovery

4.1 Machine Learning Calculations: Directed and Unsupervised Learning

Machine learning (ML) calculations play a significant part in quickening sedate revelation. These calculations can be broadly partitioned into directed and unsupervised learning procedures. In directed learning, models are prepared on labelled datasets to anticipate particular yields. For illustration, models prepared on compound datasets with known pharmacological profiles can anticipate the viability and unfavourable impacts of modern atoms (13). Such models are moreover connected in estimating potential side impacts by leveraging verifiable antagonistic medicate response (ADR) information (14).

In differentiate, unsupervised learning works with unlabelled information to distinguish inherent structures and connections. This strategy is valuable for gathering compounds with comparative chemical characteristics or clustering qualities with related expression designs, in this manner revealing novel medicate targets. Noticeable ML calculations utilized in medicate disclosure incorporate irregular woodlands for include significance investigation, back vector machines (SVMs) for classification, and slope boosting machines for gathering learning (24). These devices empower the productive preparing of high-dimensional biomedical information and assist the distinguishing proof of promising sedate candidates.

4.2 Profound Learning Utilizing Neural Networks

Deep learning (DL) models, counting convolutional neural systems (CNNs) and repetitive neural systems (RNNs), are revolutionizing medicate revelation. CNNs are capable at analyzing atomic charts and 3D basic information, empowering precise forecast of protein-ligand authoritative affinities and sedate properties (29). RNNs, outlined for consecutive information, are especially successful for modeling protein arrangements and chemical response pathways.

DL essentially improves structure activity relationship (SAR) modeling by dispensing with the require for manual highlight choice. For occurrence, CNNs can independently extricate complex highlights from atomic structures, driving to progressed expectations of dissolvability, metabolic soundness, and authoritative potential (31). Besides, DL models can survey chemical poisonous quality by recognizing unobtrusive atomic designs inside broad datasets, supporting in early-stage end of risky candidates (32).

4.3 Characteristic Dialect Preparing (NLP) for Novel Sedate Discovery

NLP methods are instrumental in parsing unstructured printed information from logical writing, licenses, and electronic wellbeing records (EHRs). These devices can extricate profitable bits of knowledge into potential sedate targets, novel chemical substances, and clinical affiliations (34). Transformer-based models such as BERT, GPT, and T5 have revolutionized NLP by successfully capturing long-range conditions in text.

Advanced Protein Dialect Models (PLMs) like Developmental Scale Modeling (ESM-2) and ProtGPT2 use transformer designs to foresee protein work, structure, and plan from amino corrosive arrangements. These models exceed expectations in assignments such as change impact forecast and de novo protein plan, regularly prepared on expansive datasets like UniProt (44). NLP applications amplify to EHR investigation, empowering the disclosure of disease-treatment relationships, biomarker recognizable proof, and personalized helpful methodologies (37).

4.4 Atomic Fingerprinting

Molecular fingerprinting changes over chemical structures into computerized representations such as bit vectors, capturing fundamental substructures and physicochemical properties. These fingerprints empower closeness looks and serve as highlights for ML models in virtual screening and poisonous quality expectation. Prevalent fingerprinting strategies incorporate Amplified Network Fingerprints (ECFPs), path-based fingerprints, and 3D atomic descriptors (35).

Unlike NLP strategies that analyze Grins as content, atomic fingerprinting employments cheminformatics calculations to encode basic data. Apparatuses such as RDKit and ChemDes encourage effective computation of atomic fingerprints (38). In spite of their utility, progressing fingerprinting strategies for assorted datasets remains a challenge.

4.5 Chart Neural Systems (GNNs)

GNNs are profound learning models custom-made for graph-structured information, making them profoundly appropriate for modeling atoms as charts of particles (hubs) and bonds (edges). Through message-passing components, GNNs learn complex hub and graph-level representations, empowering precise forecasts in virtual screening, atomic property estimation, and drug-target interaction modeling (37).

Architectures such as Chart Convolutional Systems (GCNs), Chart Consideration Systems (GATs), and Graph SAGE bolster adaptable and effective learning on expansive chemical charts. Whereas GNNs illustrate solid prescient execution, challenges like over-smoothing and interpretability proceed to be dynamic investigate regions (40).

4.6 Support Learning and Developmental Algorithms

Reinforcement Learning (RL) treats medicate plan as a successive decision-making handle. An RL operator alters atomic structures iteratively to maximize remunerate capacities based on viability, security, and pharmacokinetics. RL has been effectively connected in de novo particle era and dosing technique optimization (32).

Evolutionary calculations, motivated by natural advancement, optimize chemical structures through transformation, hybrid, and determination components. These calculations proficiently explore chemical space to distinguish particles with wanted properties like bioavailability and target specificity (36).

4.7 Generative AI

Generative AI (Gen-AI) instruments, counting generative ill-disposed systems (GANs), are instrumental in making manufactured chemical and natural information. GANs comprise of a generator and discriminator locked in in ill-disposed preparing to deliver practical yields, encouraging sedate plan, atomic optimization, and information expansion for uncommon scenarios (37).

Gen-AI has transformative potential in instruction and clinical hone, exemplified by huge dialect models (LLMs) like Chat GPT. These devices bolster errands such as medicine confirmation, ADR location, and preparing recreation. Be that as it may, issues such as demonstrate straightforwardness and approval benchmarks require continuous consideration (43).

Advanced Gen-AI systems such as Super-Resolution GANs improve therapeutic imaging and back diagnostics, whereas activities like Meta AIs ESM-2 illustrate large-scale protein structure expectation utilizing billions of parameters. The meeting of LLMs and biotechnology offers a promising wilderness in bioinformatics and healthcare (46).

Fig. 3.From: Manufactured insights in medicate disclosure and advancement: changing challenges into openings

Artificial Intelligence techniques in Drug Discovery. (26)

5. Current Challenges and Limitations

5.1 Information Quality and Accessibility A crucial challenge in AI-based medicate revelation is the constrained accessibility and variable quality of clarified datasets for preparing strong models. The heterogeneity of biomedical data ranging from chemical properties to natural measures and clinical outcomes complicates integration endeavours due to organize irregularities and lost values (47). Harmonizing such different datasets is labour- intensive and requires thorough information pre-processing methods. In addition, inborn inclinations in datasets, such as overrepresentation of particular populaces or illnesses, may lead to models with destitute generalizability and potential moral suggestions (48). Tending to these issues requests fastidious information curation, straightforwardness, and the advancement of bias-mitigation strategies.

5.2 Interpretability and Straightforwardness: The murkiness of complex AI models, particularly profound neural systems (DNNs), presents noteworthy obstructions to selection. These models frequently work as “black boxes, making it troublesome for clinicians and administrative bodies to get it or legitimize choices (49). Interpretability is significant in healthcare and pharmaceutical settings, where understanding a model thinking is vital for guaranteeing security and responsibility. Later propels, such as consideration instruments, reasonable AI (XAI), and surrogate models, offer fractional arrangements but require advance advancement to meet administrative desires and construct partner believe (50).

5.3 Integration into Existing Medicate Advancement Forms Consolidating: AI innovations into set up pharmaceutical workflows is complex and resource-intensive. Conventional pipelines are established in bequest frameworks and preservationist administrative benchmarks, which can stand up to quick advanced change (43). Viable integration of AI requires upgrades in foundation, upskilling of work force, and the advancement of modern approval systems for AI-generated comes about. Extra concerns incorporate mental property rights, information protection, and business displacement challenges that require multidisciplinary procedures and solid regulation back (51).

5.4 Moral Challenges of Utilizing: AI The moral suggestions of AI integration in pharmaceuticals are multifaceted. A ponder among drug store experts within the MENA locale highlighted beat concerns counting information protection (58.9%), cybersecurity dangers (58.9%), work misfortune (62.9%), and need of lawful controls (67.0%) (52). Moral utilize of AI requests standards of educated assent, value, straightforwardness, and advantage. Clinical trials require thorough oversight to guarantee members are completely educated and not subjected to undue hazard, particularly in defenceless populaces (52). Administrative bodies are effectively creating arrangements to address algorithmic predisposition, information abuse, and liability but advance remains divided. Adjusting advancement with moral administration will be urgent to maintainable AI selection in sedate revelation and healthcare (53).

6. AI in Real-World Applications

6.1 AI in Early Medicate Revelation: AI has revolutionized early-stage medicate revelation by empowering de novo atom era, structure expectation, and virtual screening. Generative models such as RNNs, auto encoders, GANs, and fortification learning (RL) are utilized to plan novel compounds with optimized pharmacological profiles (55). Instruments like Diff Dock (54), Molecule Gen, and ESM-2 contribute to protein ligand docking and atomic era. Companies like Atom wise and Benevolent AI use DL and ML calculations to recognize reasonable targets and sedate candidates. Atom wise utilizes structure-based screening over enormous compound libraries to quicken hit recognizable proof, whereas Benevolent AI utilizes information charts and NLP to extricate restorative experiences from logical and clinical information (56). These stages have encouraged revelations for high-priority targets such as those included in Ebola and COVID-19, illustrating AIs transformative potential in biomedical advancement (57).

6.2 AI in Medicate Repurposing: AI is additionally being utilized to repurpose existing drugs for modern indication a methodology that brings down costs and abbreviates timelines by leveraging earlier security and adequacy information 58). Systems like Deep Purpose utilize DL structures to demonstrate drug-target intelligent and rank candidates based on anticipated adequacy (59). Multi DCP coordinating information sorts such as atomic structures and quality expression profiles utilizing multi-view learning, making strides precision in foreseeing novel drug-disease connections. IBM Watson and Benevolent AI have effectively actualized these models in clinical settings. One outstanding victory is the AI-assisted repurposing of Baricitinib, initially endorsed for rheumatoid joint pain, for treating COVID-19 (60). All things considered, repurposing faces mental property and administrative obstacles, as method-of-use licenses are weaker and administrative pathways may require full-scale trials to approve modern signs (61).

6.3 AI in Clinical Trials: AI is rethinking clinical trial plan through versatile strategies, prescient modeling, and understanding stratification. Real-time information investigation permits AI to powerfully alter trial conventions, making strides proficiency and measurable control (62). AI helps persistent enlistment by mining EHRs and genomic information to distinguish reasonable candidates and guarantees way better coordinating to consideration criteria utilizing NLP calculations. Developing instruments such as computerized twins recreate trial results for virtual populaces, upgrading exactness pharmaceutical approaches. New companies like DeepDrug offer stages for particle union and expectation assignments (e.g., eMolFrag, eSynth, eToxPred), whereas Exscientia has entered clinical trials with six AI-designed particles focusing on oncology and neurology (https://www.cio.inc/how-exscientia-reduces-drug-discovery-time-gen-ai-a-23015). Whereas these propels quicken timelines and decrease costs, concerns stay with respect to information protection, educated assent, and algorithmic predisposition (63). Moral systems and compliance guidelines must advance to secure trial members and guarantee impartial outcomes.

Fig. 4.From: Counterfeit insights in medicate revelation and improvement: changing challenges into openings

Artificial Intelligence in real world applications and their providers. (26)

Fig.5. Applications of various AI techniques across different stages of drug discovery

Here's a graph showing the applications of various AI techniques across different stages of drug discovery, such as target identification, lead optimization, clinical trials, and drug repurposing.

Table.1. Applications of Different AI Techniques Across Drug Discovery and Development Stages

AI Technique Target Identification (%) Lead Optimization (%) Clinical Trials (%) Drug Repurposing (%)

Machine Learning (ML) 80 70 40 60

Deep Learning (DL) 70 85 50 70

Natural Language Processing (NLP) 65 40 75 80

Graph Neural Networks (GNN) 75 80 35 50

Reinforcement Learning (RL) 60 65 55 65

Generative AI (Gen-AI) 50 60 70 85

(64)

AI’s role across various drug discovery stages:

1. Deep Generative Model Applications in the Drug Discovery Pipeline – This figure highlights how deep learning architectures like vibrational auto encoders and GANs are employed for molecule generation, optimization, and activity prediction. (75)

Fig.6. Deep Generative Model Applications in the Drug Discovery Pipeline (75)

2. AI in the Drug Development Funnel – A depiction of artificial intelligence integrated at each phase—from discovery to clinical development—illustrating where ML, NLP, GNNs, and DL contribute effectively. (76)

Fig.7. AI in the Drug Development Funnel (76)

3. AI-Driven Pipeline for COVID‑19 Drug & Vaccine Development – A case study of AI tools applied in urgent pandemic response, highlighting structure prediction, virtual screening, and candidate prioritization. (77)

Fig.8. AI-Driven Pipeline for COVID 19 Drug & Vaccine Development (77)

7. Future Bearings and Developing Trends

Artificial Intelligence (AI) is balanced to revolutionize numerous perspectives of pharmaceutical inquire about, especially in personalized pharmaceutical, where it encourages the integration of genomic, clinical, and natural information to tailor individualized medicate treatments. This accuracy approach maximizes restorative adequacy whereas minimizing unfavourable responses (43). Additionally, the joining of AI with block chain and the Web of Things (IoT) has the potential to convert clinical investigate and persistent checking. Block chain guarantees information keenness and traceability, whereas IoT gadgets empower real-time following of quiet wellbeing measurements. Together, these innovations can streamline clinical trials and guarantee secure, decentralized information sharing over partners (64).

To fully harness AIs potential, there's a basic require for versatile administrative systems that can suit the interesting highlights of AI-based instruments. Administrative organizations must set up clear measures for AI demonstrates improvement, approval, interpretability, and responsibility to guarantee security, decency, and straightforwardness in medicate advancement and clinical decision-making (65).

AI too holds guarantee in tending to worldwide wellbeing incongruities by quickening the improvement of treatments for uncommon illnesses, dismissed tropical ailments, and rising irresistible maladies. Through quick examination of pathogen genomes and medicate repurposing stages, AI can encourage quick helpful reactions to pandemics and back worldwide wellbeing value (66).

Large Dialect Models (LLMs) such as GPT, BERT, and their domain-specific partners are rising as transformative instruments in bio manufacturing and engineered science. By consolidating methods such as retrieval-augmented era (Cloth) and information graph-based thinking, these models can computerize writing audit, experimental planning, and protein or quality plan within the design-build-test-learn (DBTL) cycle (67). This robotization altogether quickens theory era and test optimization.

Recent headways in State Space Models (SSMs)€including designs such as Hyena, Mamba, and Evoprovide promising options to conventional transformer-based models. SSMs exceed expectations in taking care of long-context information with decreased memory necessities, making them perfect for organic arrangement investigation and time-series applications in medicate disclosure (68).

The integration of multi-omics data spanning genomics, transcriptomics, proteomics, metabolomics, and epigenomes ”is revolutionizing systems-level medicate disclosure. Multi-omics examination empowers the recognizable proof of novel restorative targets and clinically significant biomarkers by giving a all-encompassing see of malady instruments (69). In any case, challenges such as tall dimensionality, information heterogeneity, and integration complexity require the utilize of AI-driven computational systems, counting machine learning (ML) and network-based strategies, to extricate significant bits of knowledge (70). Proceeded headway in computational science and high-throughput advances will advance engage multi-omics stages to improve personalized pharmaceutical and make strides treatment precision.

8. Introduction of AI and Robotics to Drug Discovery, Development, and Delivery

Artificial intelligence (AI) and robotics are rapidly transforming the landscape of drug discovery, development, and delivery by introducing unprecedented levels of efficiency and precision. AI technologies—including machine learning (ML) and deep learning (DL)—employ sophisticated algorithms to facilitate various stages of the drug development pipeline, including target identification, virtual screening, lead optimization, and molecular design (80). Figure 5 illustrates how AI tools are incorporated across the pharmaceutical R&D workflow.

The conventional process of drug discovery is often prolonged, expensive, and marked by a high attrition rate, with many compounds failing in late-stage trials due to efficacy or toxicity issues (81). AI-based approaches can mitigate these challenges by rapidly analyzing vast datasets, predicting pharmacokinetic profiles, and identifying viable drug candidates earlier in the pipeline. Additionally, robotics has revolutionized high-throughput screening, automated synthesis, and formulation, enabling consistent and reproducible experiments with minimal human error (82).

By integrating AI with robotic automation, pharmaceutical companies can accelerate innovation while reducing costs and human labor. This synergy facilitates the delivery of safer, more effective therapeutics to market, thereby reshaping the future of personalized medicine and global healthcare systems (83).

Fig.9. A flow chart of the role of AI in drug development

9. AI and Robotics in Drug Discovery, Development, and Delivery

9.1. Predictive and Active AI in Pharmaceutical R&D

Predictive AI utilizes data-driven models to forecast drug behavior, including pharmacokinetics and toxicity. In pharmaceutical quality-by-design (QbD), AI analyzes large-scale biomedical datasets to identify critical quality attributes (CQAs) and predict absorption, distribution, metabolism, and excretion (ADME) profiles (89). In parallel, active AI systems, such as robotic arms, automate laboratory processes including in vitro sample preparation, compound sorting, and high-throughput analysis (83). Machine learning (ML) techniques have become central to these advances by enabling the prediction of molecular properties, optimizing lead compounds, and identifying viable drug candidates, thus accelerating the transition from discovery to clinical application (80).

9.2. Historical Context and Milestones in AI and Robotics

The integration of AI into medicine traces back to the 1970s with the advent of computer-aided diagnosis (CAD) and expert systems like MYCIN for bacterial infection diagnosis (84). By the 1980s, AI algorithms were utilized to interpret medical imaging modalities, including MRI and CT scans (85). A pivotal breakthrough occurred in 1999 with the launch of the da Vinci Surgical System by Intuitive Surgical, which incorporated robotic and AI-assisted features for minimally invasive surgery (86). AI has since expanded into prosthetics and laboratory automation, significantly enhancing clinical capabilities and operational efficiency (83).

9.3. Target Identification and AI-Assisted Molecular Design

Drug discovery begins with identifying biomolecular targets implicated in disease pathogenesis—such as receptors, enzymes, or nucleic acids. AI algorithms analyze genomic, proteomic, and metabolomic databases to uncover novel targets and evaluate their therapeutic potential (87). A transformative AI example is AlphaFold, developed by DeepMind, which accurately predicts protein 3D structures based on amino acid sequences. In 2020, AlphaFold was deployed to predict structural configurations of five SARS-CoV-2 targets, supporting COVID-19 drug development efforts (88).

Once a target is validated, lead identification can proceed via high-throughput screening (HTS) or structure-based virtual screening. AI tools such as ORGANIC facilitate de novo molecule design, while DeepChem supports ML-driven compound screening (90). Molecular docking simulations are enhanced by reinforcement learning (RL) and Bayesian methods, enabling models like DeltaVina and PotentialNet to accurately predict ligand-target binding affinities (91).

9.4. Optimization and SAR Modeling

Following identification, molecules are optimized through structure–activity relationship (SAR) studies to enhance efficacy and safety. Tools like DeepNeuralNetQSAR predict biological activity based on structural features, improving hit-to-lead optimization (Wu et al., 2020). Compared to traditional HTS and combinatorial chemistry, AI dramatically reduces time and cost while expanding chemical diversity.

9.5. AI in Clinical Trials and AI-Generated Drugs

AI also plays a pivotal role in clinical trials by assisting in patient stratification, biomarker identification, and real-time monitoring using digital tools. For example, INS018_055, developed by Insilico Medicine, is the first AI-generated drug to enter Phase II clinical trials for idiopathic pulmonary fibrosis (92). Similarly, AI has contributed to the discovery and repurposing of drugs like Pembrolizumab, an immunotherapy now used in various cancers (83).

9.6. AI-Enabled Nanorobotics and Smart Delivery Systems

Another innovation is nanorobotics, which involves AI-guided nanoscale devices capable of targeted drug delivery based on physiological triggers such as pH (93). These nanobots navigate complex biological environments to release therapeutic agents at specific sites. Coupled with microfabrication techniques, such systems can act as drug reservoirs that self-regulate plasma concentrations.

AI also assists in the design of nanocarriers and smart drug delivery systems. For example, artificial neural networks (ANNs) and central composite design (CCD) frameworks have been employed to optimize orodispersible tablet formulations of moxifloxacin, improving both bioavailability and patient compliance (94).

10. Robotics in Drug Discovery, development, formulation, and production processes

The integration of robotics into pharmaceutical research and manufacturing has transformed traditional practices, driving improvements in efficiency, safety, and precision. Since the industrial revolution, robotic systems have increasingly replaced manual operations, offering enhanced consistency and speed in drug discovery and development workflows (78). In both upstream and downstream processes—including compound screening, synthesis, and formulation, and large-scale production— robotics minimizes human contact, which is critical for maintaining the high purity standards essential in pharmaceutical products (79). Automated systems have enabled accelerated timelines for compound testing, high-throughput screening, and rapid formulation development, contributing to timely responses during public health emergencies such as the COVID-19 pandemic, as well as during Ebola, Polio, and Cholera outbreaks (80). Table 3 presents key examples of robotic tools and automation technologies currently employed across pharmaceutical pipelines to enhance productivity and reduce human error.

Table 2: Applications of Robotics in Drug Development, Formulation, and Production

Stage Application of Robotics Description References

Drug Discovery High-throughput screening (HTS) automation Robotic arms automate the testing of thousands of compounds to identify active drug candidates. (95)

Lead Optimization Automated compound synthesis Robots synthesize and purify compound libraries with desired structural and functional properties. (91)

Formulation Development Precision dosing and mixing systems Robotic systems ensure accurate and reproducible blending of excipients and active ingredients. (83)

Preclinical Testing In vitro and in vivo sample preparation Robotic systems automate pipetting, plate loading, and sample handling in toxicity and ADME assays. (80)

Tablet Manufacturing Automated granulation, compression, and coating Robotics ensure uniformity, high-speed operation, and reduced variability in solid dosage production. (96)

Sterile Manufacturing Robotic isolators and filling lines Ensures aseptic conditions for parenteral formulations and vaccines. (97)

Packaging and Labeling Automated blister packaging, vial labeling, serialization Robotics enhance traceability, reduce human error, and ensure regulatory compliance. (98)

Quality Control Robotic inspection systems Vision-based robots detect defects and ensure quality assurance of final pharmaceutical products. (99)

Logistics & Supply Chain Robotic warehouse management Automated storage and retrieval systems (AS/RS) optimize pharmaceutical inventory handling. (83)

11. Drawbacks of AI and Robotic Systems in Drug Development

Despite the remarkable advantages that AI and robotics offer in drug discovery, development, and delivery, several limitations hinder their widespread implementation and efficacy. One notable drawback is the high cost associated with robotic systems, such as the da Vinci Surgical System, which remains prohibitively expensive for many healthcare institutions, thereby limiting accessibility and adoption in resource-constrained settings (97). A critical limitation of AI systems lies in their dependency on large, consistent, and high-quality datasets. The scarcity of well-curated biological and pharmacological datasets impedes the development of robust models, often resulting in biased or inaccurate predictions (89). Inconsistent or poorly annotated data can skew results and compromise the reproducibility of AI-driven discoveries. Therefore, the reliability of AI outputs is inherently linked to the accuracy and representativeness of input data (83).

Moreover, selecting appropriate AI algorithms for specific pharmaceutical tasks is paramount. Employing unsuitable models may yield misleading results or fail to capture complex biological phenomena. Hence, domain-specific knowledge and interdisciplinary expertise are crucial for effective AI deployment (91).

Another persistent challenge is the interpretability of AI-generated outcomes. Many AI models, particularly deep neural networks, function as "black boxes," offering little insight into the rationale behind their predictions. For regulatory approval and scientific validation, the ability to explain model decisions remains essential (99). Ethical and technical issues related to data privacy also emerge prominently in AI-driven drug discovery. These models often rely on sensitive clinical and genomic data, necessitating secure data handling protocols that comply with regulations such as GDPR and HIPAA. However, stricter data governance can limit data access, thereby restricting model improvement and generalizability (100,101).

In robotics-assisted pharmaceutical applications, achieving real-time responsiveness is constrained by hardware limitations and the need for reliable sensor integration. Balancing computational efficiency with predictive accuracy is particularly difficult when systems must rapidly adapt to dynamic laboratory environments (102,103). Lastly, AI is fundamentally a predictive tool that identifies patterns within datasets, but these predictions must still undergo experimental validation. For instance, AI can propose novel compounds based on predicted structure–activity relationships (SAR) or simulate protein-ligand interactions, yet biological assays and preclinical tests are essential to verify these predictions, often requiring significant time and financial resources (104,105).

12. Summary and Conclusions

Artificial Intelligence (AI) and robotics are revolutionizing drug discovery, development, and delivery by enhancing speed, accuracy, and efficiency at every stage of the pharmaceutical pipeline. Over the past two decades, AI technologies such as machine learning (ML), deep learning (DL), natural language processing (NLP), reinforcement learning (RL), generative adversarial networks (GANs), and graph neural networks (GNNs) have been employed to tackle critical challenges in pharmacological research. These include target identification, lead optimization, molecular docking, toxicity prediction, and virtual screening, often outperforming traditional experimental approaches in terms of cost-effectiveness and time-efficiency. Robotics further complements AI systems by automating repetitive and complex laboratory tasks. Applications include automated high-throughput screening (HTS), robotic formulation, tablet manufacturing, aseptic filling, labeling, and packaging, thus reducing human error and increasing process reproducibility. Advanced robotic platforms such as the da Vinci Surgical System and AI-enhanced nanorobots demonstrate the synergy of intelligent automation and computational biology in both clinical and industrial settings.

Despite these advances, several challenges and limitations persist. Data heterogeneity, lack of high-quality annotated datasets, algorithmic bias, lack of model interpretability, high implementation costs, and ethical concerns (e.g., data privacy, informed consent, job displacement) continue to limit the widespread integration of AI and robotics in pharma. Many AI systems function as "black boxes," creating barriers for clinical and regulatory acceptance due to a lack of transparency in decision-making. Nevertheless, emerging trends offer promising solutions. Innovations like large language models (LLMs), retrieval-augmented generation (RAG), state space models (SSMs) such as Hyena and Evo, and multi-omics integration represent next-generation strategies for holistic, precise, and context-aware drug development. Tools such as AlphaFold, DiffDock, DeepPurpose, and INS018_055 (the first fully AI-designed drug in clinical trials) exemplify how computational biology is rapidly bridging the gap between in silico predictions and real-world therapeutic outcomes.

In real-world applications, AI has proven effective not only in early drug discovery but also in drug repurposing (e.g., Baricitinib for COVID-19) and clinical trial optimization, including digital twins, adaptive trial design, and patient stratification using electronic health records and genomics. To fully leverage AI and robotics, regulatory frameworks must evolve, ethical standards must be enforced, and interdisciplinary collaborations between academia, industry, and government are essential. Investment in AI education, data governance, and explainable AI (XAI) tools will further ensure responsible innovation.

Conclusion

The convergence of AI, robotics, and biotechnology holds transformative potential in reimagining pharmaceutical science. While hurdles related to data integrity, ethics, cost, and transparency remain, the ongoing integration of AI into drug development workflows signifies a paradigm shift toward precision, personalization, and predictive medicine. With continuous innovation and responsible deployment, AI and robotics can dramatically reduce drug discovery timelines, lower costs, and expand global access to safe and effective therapies.

Fig10: Research Focus in Drug Discovery (94,106,107)

References

1. DiMasi, J. A., Grabowski, H. G., & Hansen, R. W. (2016). Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics, 47, 20–33. https://doi.org/10.1016/j.jhealeco.2016.01.012

2. Wouters, O. J., McKee, M., & Luyten, J. (2020). Estimated research and development investment needed to bring a new medicine to market, 2009–2018. JAMA, 323(9), 844–853. https://doi.org/10.1001/jama.2020.1166

3. Mak, K. K., & Pichika, M. R. (2019). Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today, 24(3), 773–780. https://doi.org/10.1016/j.drudis.2018.11.014

4. Ekins, S., Puhl, A. C., Zorn, K. M., Lane, T. R., Russo, D. P., Klein, J. J., & Hickey, A. J. (2019). Exploiting machine learning for end-to-end drug discovery and development. Nature Materials, 18(5), 435–441. https://doi.org/10.1038/s41563-019-0338-z

5. Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2020). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241–1250. https://doi.org/10.1016/j.drudis.2017.10.010

6. Hay, M., Thomas, D. W., Craighead, J. L., Economides, C., & Rosenthal, J. (2014). Clinical development success rates for investigational drugs. Nature Biotechnology, 32(1), 40–51. https://doi.org/10.1038/nbt.2786

7. Vamathevan, J., Clark, D., Czodrowski, P., et al. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18, 463–477. https://doi.org/10.1038/s41573-019-0024-5

8. Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., et al. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(9), 1038–1040. https://doi.org/10.1038/s41587-019-0224-x

9. Russell, S. J., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

10. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539

11. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

12. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415.

13. Chen et al., 2018 Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241–1250. https://doi.org/10.1016/j.drudis.2018.01.039

14. Vamathevan et al., 2019 Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., & Zhao, S. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18, 463–477. https://doi.org/10.1038/s41573-019-0024-5

15. Zhou, J., Luo, L., Zeng, J., Chen, X., & Chen, K. (2020). Machine learning-based drug discovery and development: Recent advances and future prospects.

16. Rifaioglu, A. S., Atas, H., Martin, M. J., Cetin-Atalay, R., Atalay, V., & Doğan, T. (2019). https://doi.org/10.1016/j.drudis.2020.06.012

17. Recent applications of deep learning and machine intelligence on in silico drug discovery: Methods, tools and databases. Briefings in Bioinformatics, 20(5), 1878–1912. https://doi.org/10.1093/bib/bby061 Drug Discovery Today, 25(8), 1530–1538.

18. Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017). Neural message passing for quantum chemistry. In Proceedings of the 34th International Conference on Machine Learning (pp. 1263–1272).

19. Hassoun, M. H. (1995). Fundamentals of Artificial Neural Networks. MIT Press.

20. Sanchez-Lengeling, B., & Aspuru-Guzik, A. (2018). Inverse molecular design using machine learning: Generative models for matter engineering. Science, 361(6400), 360–365.

21. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.

22. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

23. Vamathevan, J., Clark, D., Czodrowski, P., et al. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18, 463–477.

24. Zhou, Z. H., Li, X., & Chen, Y. (2020). Evolutionary learning: Advances in theories and algorithms. Artificial Intelligence Review, 53, 3839–3873.

25. Rifaioglu, A. S., Sinan, U., Nalbat, H. A., et al. (2019). Deep learning applications in drug discovery: Recent advances and future prospects. Expert Opinion on Drug Discovery, 14(1), 1–16.

26. Shashi Kant1 · Deepika2 · Saheli Roy3 Artifcial intelligence in drug discovery and development: transforming challenges into opportunities Discover Pharmaceutical Sciences (2025) 1:7 | https://doi.org/10.1007/s44395-025-00007-3.

27. Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241–1250.

28. Zhou, J., et al. (2020). Artificial intelligence in drug discovery: applications and techniques. Drug Discovery Today, 25(9), 1566–1574.

29. Rifaioglu, A. S., et al. (2019). Deep learning and machine intelligence in in silico drug discovery: a review of recent applications. Current Medicinal Chemistry, 26(28), 5385–5409.

30. Vamathevan, J., et al. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18(6), 463–477.

31. Ching, T., et al. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface, 15(141), 20170387.

32. Zhang, L., et al. (2022). Machine learning in biomarker discovery for precision medicine: applications and challenges. Signal Transduction and Targeted Therapy, 7(1), 1–14.

33. Wang, Y., et al. (2021). Unsupervised clustering for disease subtype discovery using omics data. Briefings in Bioinformatics, 22(4), bbaa294.

34. Tang, B., et al. (2021). AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2. Nature Communications, 12(1), 1–12.

35. Lo, Y. C., et al. (2018). Machine learning in chemoinformatics and drug discovery. Drug Discovery Today, 23(8), 1538–1546.

36. Stokes, J. M., et al. (2020). A deep learning approach to antibiotic discovery. Cell, 180(4), 688–702.e13.

37. Yang, H., et al. (2019). ADMETopt: a web server for ADMET optimization in drug design via molecular graphs. Nucleic Acids Research, 47(W1), W222–W229.

38. Segler, M. H. S., Preuss, M., & Waller, M. P. (2018). Planning chemical syntheses with deep neural networks and symbolic AI. Nature, 555(7698), 604–610.

39. Berman, H. M., et al. (2000). The Protein Data Bank. Nucleic Acids Research, 28(1), 235–242.

40. Liu, X., et al. (2020). Evaluating machine learning algorithms for ADMET prediction. Journal of Cheminformatics, 12(1), 1–14.

41. Zhang, W., et al. (2021). Deep learning-based drug–target interaction prediction: a survey. Current Medicinal Chemistry, 28(3), 448–471.

42. Esteva, A., et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29.

43. Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.

44. Fan, X., et al. (2022). A comprehensive deep learning framework for predicting hERG blockers. Journal of Chemical Information and Modeling, 62(3), 457–471.

45. The UniProt Consortium. (2021). UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Research, 49(D1), D480–D489.

46. Bica, I., et al. (2021). Real-time prediction of clinical trial outcomes using machine learning. Patterns, 2(7), 100242.

47. Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317–1318. https://doi.org/10.1001/jama.2017.18391.

48. Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care—addressing ethical challenges. New England Journal of Medicine, 378(11), 981–983. https://doi.org/10.1056/NEJMp1714229.

49. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.

50. Lipton, Z. C. (2018). The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Communications of the ACM, 61(10), 36–43. https://doi.org/10.1145/3233231.

51. Mak, K. K., & Pichika, M. R. (2019). Artificial intelligence in drug development: Present status and future prospects. Drug Discovery Today, 24(3), 773–780. https://doi.org/10.1016/j.drudis.2018.11.014.

52. Floridi, L., Cowls, J., Beltrametti, M., et al. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28, 689–707. https://doi.org/10.1007/s11023-018-9482-5.

53. Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020). From what to how: An initial review of publicly available AI ethics tools, methods and research to translate principles into practices. Science and Engineering Ethics, 26, 2141–2168. https://doi.org/10.1007/s11948-019-00165-5.

54. Corso, G., Stärk, H., Jing, B., Barzilay, R., & Jaakkola, T. (2022). DiffDock: Diffusion steps, twists, and turns for molecular docking. arXiv preprint arXiv:2210.01776.

55. Schwalbe-Koda, D., et al. (2021). Generative models for automatic chemical design. Nature Machine Intelligence, 3(12), 1045–1053. https://doi.org/10.1038/s42256-021-00418-2.

56. Hopkins, A. L., & Groom, C. R. (2020). The druggable genome. Nature Reviews Drug Discovery, 1(9), 727–730. https://doi.org/10.1038/nrd892

57. Zhou, Y., Wang, F., Tang, J., & Nussinov, R. (2020). Artificial intelligence in COVID-19 drug repurposing. The Lancet Digital Health, 2(12), e667–e676. https://doi.org/10.1016/S2589-7500(20)30204-6.

58. Saha, S., Halder, A. K., & Bandyopadhyay, S. (2021). Machine learning-based drug repurposing: a critical review. Computers in Biology and Medicine, 137, 104896. https://doi.org/10.1016/j.compbiomed.2021.104896.

59. Huang, K., Fu, T., Gao, W., et al. (2020). DeepPurpose: A deep learning-based drug–target interaction prediction toolkit. bioRxiv. https://doi.org/10.1101/2020.01.21.915967.

60. Stebbing, J., Krishnan, V., de Bono, S., et al. (2020). Mechanism of baricitinib supports artificial intelligence-predicted testing in COVID-19 patients. EMBO Molecular Medicine, 12(8), e12697. https://doi.org/10.15252/emmm.202012697.

61. Pushpakom, S., Iorio, F., Eyers, P. A., et al. (2019). Drug repurposing: Progress, challenges and recommendations. Nature Reviews Drug Discovery, 18(1), 41–58. https://doi.org/10.1038/nrd.2018.168.

62. Wegner, J. K., Aladjov, H., & Schwab, C. H. (2021). Adaptive design in clinical trials: Use of AI to predict patient enrollment and improve trial efficiency. Drug Discovery Today, 26(3), 597–603. https://doi.org/10.1016/j.drudis.2020.12.001.

63. Yousefi, S., Keshavarzi Arshadi, A., & Zhang, G. (2022). Trustworthy AI in healthcare: A systematic review of existing tools, policies, and challenges. NPJ Digital Medicine, 5(1), 67. https://doi.org/10.1038/s41746-022-00630-9.

64. Dwivedi, A. D., Srivastava, G., Dhar, S., & Singh, R. (2021). A decentralized privacy-preserving healthcare blockchain for IoT. Sensors, 21(4), 1212. https://doi.org/10.3390/s21041212.

65. He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30–36.

66. Walsh, I., Tardaguila, M., & Di Domenico, T. (2021). AI for tackling neglected tropical diseases. Trends in Parasitology, 37(7), 568–571.

67. Linder, J., Rusak, G., Mueller, N., et al. (2023). GPT-4 for Science: Early Explorations on New Frontiers. arXiv preprint arXiv:2304.03442.

68. Gu, A., & Dao, T. (2023). Mamba: Linear-Time Sequence Modeling with Selective State Spaces. arXiv preprint arXiv:2312.00752.

69. Hasin, Y., Seldin, M., & Lusis, A. (2017). Multi-omics approaches to disease. Genome Biology, 18, 83. https://doi.org/10.1186/s13059-017-1215-1.

70. Misra, B. B., Langefeld, C., Olivier, M., & Cox, L. A. (2019). Integrated omics: tools, advances and future approaches. Journal of Molecular Endocrinology, 62(1), R21–R45.

71. Vamathevan, J., Clark, D., Czodrowski, P., et al. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18, 463–477.

72. Rifaioglu, A. S., Atas, H., Martin, M. J., Cetin-Atalay, R., Atalay, V., & Dogan, T. (2019). Recent applications of deep learning and machine intelligence on in silico drug discovery: Methods, tools and databases. Briefings in Bioinformatics, 20(5), 1878–1912.

73. Mak, K. K., & Pichika, M. R. (2019). Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today, 24(3), 773–780.

74. Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020). From what to how: AI ethics toolkits and practices. Science and Engineering Ethics, 26(4), 2141–2168.

75. Deep generative molecular design reshapes drug discovery - Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/AI-and-deep-generative-model-applications-in-the-drug-discovery-pipeline_fig1_364843685

76. Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design - Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Drug-discovery-process-based-on-AI-ML-method_fig3_354032026.

77. Keshavarzi Arshadi A, Webb J, Salem M, Cruz E, Calad-Thomson S, Ghadirian N, Collins J, Diez-Cecilia E, Kelly B, Goodarzi H, Yuan JS. Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development. Front Artif Intell. 2020 Aug 18;3:65. doi: 10.3389/frai.2020.00065. PMID: 33733182; PMCID: PMC7861281.

78. Zhang, H., et al. (2020). Minimizing Contamination through Robotic Integration in Pharma. International Journal of Pharmaceutics.

79. Mital, A., et al. (2021). Role of Robotics in Emergency Pharmaceutical Response. Bioengineering and Translational Medicine.

80. Vamathevan, J., Clark, D., Czodrowski, P., et al. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18, 463–477. https://doi.org/10.1038/s41573-019-0024-5.

81. Pushpakom, S., et al. (2019). Drug repurposing: Progress, challenges and recommendations. Nature Reviews Drug Discovery, 18, 41–58. https://doi.org/10.1038/nrd.2018.168.

82. Schneider, G. (2018). Automating drug discovery. Nature Reviews Drug Discovery, 17, 97–113. https://doi.org/10.1038/nrd.2017.232.

83. Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25, 44–56. https://doi.org/10.1038/s41591-018-0300-7.

84. Shortliffe, E. H., & Buchanan, B. G. (1975). A model of inexact reasoning in medicine. Mathematical Biosciences, 23(3-4), 351–379. https://doi.org/10.1016/0025-5564(75)90047-4

85. Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care — addressing ethical challenges. New England Journal of Medicine, 378, 981–983. https://doi.org/10.1056/NEJMp1714229.

86. Haidegger, T., Nagy, M., Lehotsky, A., & Pinter, R. (2019). The dawn of AI-supported robotic surgery. IEEE Transactions on Medical Robotics and Bionics, 1(1), 6–17. https://doi.org/10.1109/TMRB.2019.2909190.

87. Zhou, J., Wang, Y., Zhang, X., et al. (2020). Artificial intelligence in COVID-19 drug repurposing. The Lancet Digital Health, 2(12), e667–e676. https://doi.org/10.1016/S2589-7500(20)30204-9.

88. Jumper, J., Evans, R., Pritzel, A., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596, 583–589. https://doi.org/10.1038/s41586-021-03819-2

89. Mak, K. K., & Pichika, M. R. (2019). Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today, 24(3), 773–780. https://doi.org/10.1016/j.drudis.2018.11.014.

90. Ramsundar, B., Eastman, P., Walters, P., & Pande, V. (2019). Deep Learning for the Life Sciences. O’Reilly Media.

91. Paul, D., Sanap, G., Shenoy, S., et al. (2021). Artificial intelligence in drug discovery and development. Drug Discovery Today, 26(1), 80–93. https://doi.org/10.1016/j.drudis.2020.10.010.

92. Li, J., Liu, Y., Zhang, Y., et al. (2022). Nanorobots for intelligent drug delivery: From concept to clinical development. Advanced Drug Delivery Reviews, 190, 114568. https://doi.org/10.1016/j.addr.2022.114568

93. Deshkar, S. S., Mahajan, H. S., & Tekade, R. K. (2022). Formulation design and optimization using artificial neural network: A case study with moxifloxacin. Current Drug Delivery, 19(5), 424–432. https://doi.org/10.2174/1567201819666220208115337.

94. Vamathevan, J., Clark, D., Czodrowski, P., et al. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18, 463–477.

95. U.S. Food & Drug Administration (FDA). (2022). Pharmaceutical CGMPs and robotic applications. https://www.fda.gov

96. European Medicines Agency (EMA). (2021). Guidelines on aseptic processing and automated technologies. https://www.ema.europa.eu.

97. Haidegger, T., Nagy, M., Lehotsky, A., & Pinter, R. (2019). The dawn of AI-supported robotic surgery. IEEE Transactions on Medical Robotics and Bionics, 1(1), 6–17.

98. Li, J., Liu, Y., Zhang, Y., et al. (2022). Robotics in pharmaceutical manufacturing: advances and future perspectives. Advanced Drug Delivery Reviews, 190, 114568.

99. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.

100. Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care—addressing ethical challenges. New England Journal of Medicine, 378(11), 981–983.

101. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.

102. Bini, S. A., et al. (2021). Artificial intelligence, robotics, and precision medicine in orthopedic surgery. Journal of Bone and Joint Surgery, 103(2), 114–122.

103. Schwalbe-Koda, D., et al. (2021). Learning quantum mechanical forces for drug discovery. Nature Communications, 12, 5101.

104. Schwalbe-Koda, D., et al. (2021). Learning quantum mechanical forces for drug discovery. Nature Communications, 12, 5101.

105. Zhang, Q., et al. (2022). Artificial intelligence in drug design: Progress, challenges and future directions. Drug Discovery Today, 27(1), 70–83.

106. 1. Zhou et al., 2020

107. Zhou, J., Luo, L., Wang, J., Lu, Y., Yang, H., & Hou, T. (2020). Recent advances in applications of deep learning for drug discovery. Journal of Medicinal Chemistry, 63(16), 8705–8722. https://doi.org/10.1021/acs.jmedchem.9b01120.

108. Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P. M., Zietz, M., Hoffman, M. M., Xie, W., Rosen, G. L., Lengerich, B. J., Israeli, J., Lanchantin, J., Woloszynek, S., Carpenter, A. E., Shrikumar, A., Xu, J., ... & Greene, C. S. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of the Royal Society Interface, 15(141), 20170387. https://doi.org/10.1098/rsif.2017.0387