Artificial Intelligence in Drug Discovery

Abstract

Artificial Intelligence (AI) has emerged as a revolutionary tool in the field of pharmaceutical sciences, particularly in drug discovery and development. Traditional drug discovery is highly resource-intensive, often taking over a decade to bring a single therapeutic molecule from initial screening to market approval. The integration of AI, encompassing machine learning (ML), deep learning (DL), natural language processing (NLP), and other computational approaches, enables the rapid analysis of complex datasets, prediction of molecular interactions, optimization of clinical trials, and even de novo drug design. AI facilitates cost-effective and time-efficient drug development, enhancing the probability of success in preclinical and clinical studies. This article provides a detailed exploration of AI in drug discovery, including historical background, key methodologies, applications across various stages of drug development, advantages, limitations, ethical considerations, and future prospects. Through examples, case studies, and detailed analysis, the transformative role of AI in modern pharmacology is highlighted, showing how it can accelerate research, optimize therapeutic development, and improve patient outcomes globally.

Keywords

Artificial Intelligence, Drug Discovery, Machine Learning, Deep Learning, Data Interpretation, ADMET, Clinical Trials, Pharmaceutical Research.

Introduction

Machine Learning is a subset of AI that focuses on developing algorithms capable of learning patterns from existing data and making predictions on unseen data. Unlike traditional programming, where explicit instructions are provided, ML systems learn rules from examples. In drug discovery, ML is used for:

1. Predicting Drug-Target Interactions: ML algorithms analyze chemical structures and biological activity data to predict which compounds are likely to bind effectively to target proteins.

2. Activity Prediction and Classification: ML can classify compounds as active or inactive against a particular target using historical bioassay data.

3. ADMET Modeling: ML predicts Absorption, Distribution, Metabolism, Excretion, and Toxicity properties, helping prioritize compounds likely to succeed in clinical trials.

Types of Machine Learning in Drug Discovery

• Supervised Learning: Uses labeled data to train models. For example, a dataset of compounds labeled as toxic or non-toxic is used to train a classifier for predicting toxicity.

• Unsupervised Learning: Identifies hidden patterns in unlabeled data, useful for clustering similar compounds or identifying novel chemical scaffolds.

• Reinforcement Learning (RL): Algorithms learn optimal strategies by trial-and-error, which can be applied in optimizing molecular structures for improved pharmacological activity.

Example: The platform ChemProp employs graph-based neural networks to predict molecular properties and bioactivity, allowing chemists to prioritize compounds with the highest likelihood of success.

2. Deep Learning (DL)

Deep Learning is a specialized branch of ML involving artificial neural networks with multiple hidden layers. DL can model highly non-linear, complex relationships that traditional ML

algorithms may not capture. In drug discovery, DL is particularly valuable in:

1. Protein Structure Prediction: DL algorithms, such as AlphaFold, have revolutionized structural biology by accurately predicting 3D protein structures from amino acid sequences.

2. Ligand-Protein Binding Affinity: Convolutional neural networks (CNNs) can analyze 3D molecular structures to predict how well a ligand binds to a protein target.

3. De Novo Drug Design: Generative adversarial networks (GANs) and recurrent neural networks (RNNs) can propose entirely new molecular structures optimized for target activity and drug-likeness.

Example: Insilico Medicine uses GANs to generate novel molecules for cancer targets, drastically reducing the time from target identification to lead compound development.

3. Natural Language Processing (NLP)

NLP enables computers to understand, interpret, and extract meaningful information from human language. In drug discovery, NLP applications include:

1. Biomedical Literature Mining: AI scans thousands of research papers and patents to extract relationships between targets, pathways, and compounds.

2. Knowledge Graph Construction: NLP algorithms organize extracted data into structured formats, enabling researchers to query complex biological relationships quickly.

3. Drug Repurposing: By analyzing text from publications and clinical trial reports, NLP can identify potential new indications for existing drugs.

Example: During the COVID-19 pandemic, NLP algorithms were used to mine existing literature and suggest candidate drugs for repurposing, expediting potential treatments.

4. Integration of AI Techniques in Drug Discovery

 AI techniques often work synergistically:

• ML + DL: Deep neural networks are a type of machine learning; ML can provide feature selection and preprocessing, while DL captures complex non-linear patterns.

• DL + NLP: Deep learning models can enhance NLP tasks, such as extracting relationships from
  unstructured biomedical literature with higher accuracy.

• RL + DL: Reinforcement learning combined with deep learning allows optimization of
   molecular generation, making drug design more precise.

Figure Description: (In the Word/PDF version, include a diagram showing “AI Techniques in Drug Discovery” linking ML, DL, NLP, RL to stages like target identification, lead optimization, ADMET prediction, clinical trial design.)

5. Advantages of Using AI Techniques

1. Handling Large-Scale Data: Omics datasets, high-throughput screening data, and clinical trial records are massive; AI can process and analyze these efficiently.

2. Enhanced Prediction Accuracy: AI models can identify subtle patterns in data that humans or conventional statistical methods may miss.

3. Time and Cost Efficiency: Computational predictions reduce the need for laborious in vitro and in vivo experiments.

4. Innovation in Molecular Design: AI can propose novel chemical entities and therapeutic strategies beyond human intuition.

AI in Target Identification and Validation

Target identification and validation is one of the most critical stages in the drug discovery pipeline. A drug target is typically a gene, protein, receptor, or pathway that plays a significant role in the pathology of a disease. Traditionally, target discovery relied heavily on experimental biology, which was time-consuming, expensive, and often prone to errors. The integration of Artificial Intelligence (AI) has revolutionized this process, enabling researchers to rapidly identify, prioritize, and validate potential therapeutic targets with greater accuracy.

1. Understanding Target Identification

Target identification involves discovering biological entities that can be modulated by a therapeutic agent to achieve a desired clinical outcome. Effective target identification requires an in-depth understanding of the disease mechanism, cellular pathways, and molecular interactions. Traditional experimental methods, such as gene knockout studies, RNA interference, or high-throughput screening (HTS), are laborious and may not reveal all relevant targets.

AI, particularly machine learning (ML) and deep learning (DL), allows researchers to analyze omics data (genomics, transcriptomics, proteomics, metabolomics) to identify patterns that indicate potential therapeutic targets. By learning from large datasets, AI models can detect subtle correlations between genetic variations and disease phenotypes, which might be overlooked by conventional methods.

2. AI Techniques in Target Identification

2.1 Machine Learning Approaches

Machine learning algorithms can classify genes or proteins as potential drug targets based on historical data. Features such as gene expression profiles, protein-protein interactions, and disease association scores are used as inputs.

Applications:

• Supervised Learning: Uses labeled datasets to train models. For example, a dataset of known drug targets can train a classifier to predict the probability of a new gene being a viable target.

• Unsupervised Learning: Clusters genes or proteins based on similarity in expression patterns or pathway involvement, helping identify novel targets.

Example: Random Forests and Support Vector Machines (SVMs) are commonly used ML algorithms for target prediction in oncology and neurodegenerative diseases.

2.2 Deep Learning for Protein Analysis

Deep learning, particularly convolutional neural networks (CNNs) and graph neural networks (GNNs), has significantly advanced protein structure prediction and target validation.

• AlphaFold by DeepMind: Accurately predicts 3D protein structures from amino acid sequences. Understanding the 3D conformation is crucial for identifying potential binding sites and designing effective ligands.

• GNNs for Network Analysis: Proteins function within complex networks. GNNs model these interactions, predicting which proteins are critical for disease progression and therefore good targets.

Example: In cancer research, AI models identified novel kinases involved in tumor progression, providing targets for small-molecule inhibitors.

2.3 Natural Language Processing (NLP)

NLP algorithms analyze the scientific literature, patents, and clinical trial reports to extract knowledge about potential targets. By scanning thousands of publications, AI can identify genes or proteins associated with disease phenotypes and therapeutic interventions.

Example: During the COVID-19 pandemic, NLP tools scanned literature and rapidly highlighted ACE2 and TMPRSS2 as key viral entry targets for therapeutic intervention.

3. Target Validation Using AI

Once potential targets are identified, validation ensures that modulating the target will produce the desired therapeutic effect without unacceptable toxicity. AI assists in this stage by:

1. Predicting Functional Effects: ML models can forecast the downstream effects of modulating a target, including pathway activation or inhibition.

2. Predicting Off-Target Effects: Deep learning algorithms can simulate interactions with other proteins, reducing the risk of adverse effects.

3. Integrating Multi-Omics Data: AI combines genomics, proteomics, metabolomics, and transcriptomics data to validate targets across multiple biological levels.

Case Study: A 2021 study employed AI to integrate transcriptomic and proteomic data, identifying and validating novel targets for Alzheimer’s disease. The model successfully predicted proteins involved in amyloid-beta aggregation, later confirmed experimentally.

4. Workflow of AI-Based Target Identification and Validation

1. Data Collection: Omics datasets, literature, and clinical databases.

2. Preprocessing: Cleaning and normalizing data; removing noise.

3. Feature Extraction: Identifying relevant gene/protein features, interaction networks, and pathway data.

4. Model Training: Using supervised/unsupervised ML or deep learning algorithms to predict potential targets.

5. Validation: Cross-validation with experimental data, literature support, and simulation of downstream effects.

6. Prioritization: Ranking targets based on predicted efficacy, druggability, and safety profile.

Figure Description: (Include a diagram showing AI pipeline for target identification: Data → Preprocessing → ML/DL Model → Target Prediction → Validation → Prioritization.)

5. Advantages of AI in Target Identification and Validation

1. Speed: Reduces months or years of experimental work into weeks of computational analysis.

2. Accuracy: AI models can detect subtle correlations and interactions missed by conventional methods.

3. Cost Efficiency: Minimizes expensive wet-lab experiments by pre-selecting high-probability targets.

4. Novel Discoveries: Facilitates identification of previously unknown or unconventional targets.

5. Integration of Multiple Data Types: Combines omics, literature, and clinical data for comprehensive analysis.

AI has fundamentally transformed the early stages of drug discovery. By enabling rapid and accurate identification of therapeutic targets and providing computational validation, AI reduces risk, cost, and time associated with conventional methods. Integrating machine learning, deep learning, and NLP ensures that pharmaceutical research is data-driven, predictive, and highly efficient, paving the way for  more successful and personalized drug discovery initiatives.

AI in Hit Discovery and Lead Optimization

Hit discovery and lead optimization form the heart of the drug-discovery pipeline. After a therapeutic target is identified and validated, researchers need to find chemical compounds called hits that interact with that target in a biologically meaningful way. From these hits, promising candidates known as leads are optimized to improve potency, selectivity, and pharmacokinetic properties before pre-clinical testing. Traditionally, this has involved enormous experimental screening and chemical synthesis efforts. Artificial intelligence (AI) has transformed this stage by making the process faster, cheaper, and far more predictive.

1. Overview of Hit Discovery

In classical pharmacology, hit discovery relied on high-throughput screening (HTS), where millions of compounds were experimentally tested against a target. Although effective, HTS is expensive and produces a large number of false positives. AI offers computational alternatives such as virtual screening and de novo molecule generation that dramatically reduce experimental burden.

1.1 Virtual Screening

Virtual screening uses computer algorithms to predict how well small molecules will bind to a target protein. AI models trained on known ligand–target interactions can prioritize compounds from massive chemical libraries before any wet-lab work begins.

Machine learning (ML) and deep learning (DL) techniques enhance virtual screening by:

• predicting binding affinities based on molecular descriptors or 3-D structures,

• ranking compounds for experimental testing,

• filtering out toxic or unstable molecules.

Example: The program AtomNet, a convolutional neural-network system, analyzes 3-D representations of protein ligand complexes and predicts binding probabilities with remarkable accuracy. It has successfully identified novel inhibitors for enzymes such as thrombin and BACE1, potential targets in cardiovascular and Alzheimer’s diseases.

1.2 De Novo Molecular Generation

Instead of screening existing libraries, AI can generate entirely new molecules. Generative models such as variational autoencoders (VAEs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) learn chemical rules from existing compounds and then design novel structures predicted to have desired biological activities.

Example: Insilico Medicine employed a GAN-based approach to design a potent DDR1 kinase inhibitor in under 46 days a process that would normally take years. This success demonstrated the speed and creative potential of AI-driven molecule generation.

2. Lead Optimization

Once promising hits are identified, lead optimization refines these molecules to improve potency, selectivity, solubility, stability, and safety. AI contributes by predicting how structural modifications will influence biological and physicochemical properties.

2.1 Quantitative Structure Activity Relationship (QSAR) Models

QSAR models use statistical and ML techniques to correlate molecular features with biological activity. AI has advanced QSAR by enabling non-linear modeling and feature discovery through deep networks, improving prediction accuracy for complex molecules.

2.2 Multi-Objective Optimization

Drug candidates must simultaneously satisfy many requirements efficacy, low toxicity, metabolic stability, and manufacturability. Reinforcement-learning (RL) algorithms can optimize multiple objectives at once, automatically proposing chemical modifications that balance these factors.

Example: The DeepReLeaSE platform applies reinforcement learning to generate molecules optimized for both target activity and drug-likeness, showing how AI can perform iterative design cycles autonomously.

2.3 Predicting ADMET Properties

AI predicts absorption, distribution, metabolism, excretion, and toxicity (ADMET) characteristics early in development. Models such as random forests, gradient boosting, and graph-based neural networks identify likely metabolic pathways or toxic substructures, reducing costly late-stage failures.

2.4 Docking and Binding-Pose Prediction

Deep-learning algorithms trained on protein ligand complexes can forecast how molecules orient within binding sites. Improved pose prediction guides medicinal chemists to design analogues with enhanced affinity.

3. Integrated AI Workflows

Modern discovery platforms integrate several AI components:

1. Data Collection: Chemical libraries, bioassay databases (ChEMBL, PubChem), and structural data (PDB).

2. Pre-processing: Feature extraction and molecular representation (SMILES, graphs, fingerprints).

3. Model Training: ML/DL algorithms learn relationships between structure and function.

4. Hit Generation: Virtual screening or de novo design yields candidate molecules.

5. Lead Optimization: Reinforcement learning and QSAR refinement.

6. Experimental Validation: Top candidates synthesized and tested in vitro/in vivo.

Such end-to-end AI pipelines offered by companies like Schrödinger, BenevolentAI, and Exscientia shorten the design make test analyze cycle from months to days.

4. Case Studies

• Exscientia and Sumitomo Dainippon Pharma: Their AI-designed molecule DSP-1181, a serotonin-5-HT1A receptor agonist for obsessive-compulsive disorder, entered clinical trials in record time (less than one year from concept).

• BenevolentAI’s Baricitinib Repurposing: AI analysis of biomedical data suggested baricitinib, originally for rheumatoid arthritis, as a potential COVID-19 therapy; subsequent clinical trials confirmed its benefits.

These examples illustrate how AI can both discover novel chemical entities and repurpose existing drugs.

5. Advantages and Current Limitations

Advantages

 • Enormous reduction in screening costs and timelines.

• Capability to explore chemical spaces far larger than any human could manually design.

• Early detection of toxicity and poor pharmacokinetics.

• Continuous learning: models improve as more experimental data become available.

Disadvantages of AI

Limitations

• Dependence on high-quality, diverse datasets.

• “Black-box” nature of deep models complicates mechanistic interpretation.

• Regulatory acceptance of AI-generated molecules is still evolving.

• Human expertise remains essential for experimental validation and clinical translation.

Ethical & Regulatory Considerations in AI Based Drug Discovery

AI is revolutionizing drug discovery, but its rapid integration raises important ethical and regulatory concerns. These are crucial because errors or biases in AI systems can directly impact patient safety, data privacy, and public trust.

1. Data Privacy & Security

AI relies on large volumes of sensitive biomedical data, including patient health records, genomic sequences, and clinical trial information. Protecting this data is critical.

• Risk of misuse: Personal health data can be stolen or misused if not properly secured.

• Regulatory frameworks:

• HIPAA (Health Insurance Portability and Accountability Act) in the US mandates the protection of patient health information.

• GDPR (General Data Protection Regulation) in the EU emphasizes consent, anonymization, and data minimization.

• AI-specific challenges:

• Some AI models require raw patient data for training.

• Balancing data utility for model training vs. patient confidentiality is a delicate ethical issue.

Example: Hospitals using AI to predict disease susceptibility must encrypt data, anonymize patient identifiers, and restrict access to trained professionals only.

2. Fairness and Bias

AI models are only as good as the data they are trained on. Biases in datasets can lead to unequal or unfair predictions.

• Demographic bias:

• If most training data comes from one population (e.g., European descent), AI may fail to predict drug responses accurately for other ethnic groups.

•  Outcome bias:

• Models may favor drugs that historically received more funding or research, ignoring potentially better alternatives.

• Ethical concern:

•Unfair predictions can lead to adverse effects in underrepresented populations.

Example: A cardiovascular drug AI model trained mainly on male patients may incorrectly estimate risk in female patients, causing unsafe dosing recommendations.

3. Transparency and Explainability for Regulatory Compliance (FDA/EMA)

Regulatory authorities like FDA and EMA demand that AI-driven drug discovery systems are explainable.

• Problem with black-box AI:

• Deep learning models often make predictions without revealing the rationale.

• Regulators cannot approve a drug solely based on a model whose decisions cannot be justified scientifically.

• Need for explainable AI (XAI):

• Models should provide insights into why a particular compound is predicted to bind a target or fail ADMET tests.

• Benefit:

• Ensures trust, facilitates auditing, and aligns with legal accountability.

Example: If AI predicts that a drug will fail due to liver toxicity, XAI can highlight which molecular substructures are responsible. Regulators can then validate or challenge the prediction.

4. Informed Consent & Patient Protection

When AI is used in clinical trials or personalized therapy:

• Patients must understand:

• How their data will be used in AI modeling.

• Potential risks and benefits of AI-driven decisions.

• Ethical principle:

• Patients’ autonomy must be respected; consent must be voluntary and informed.

• Risk mitigation:

• Any AI-guided drug recommendation should be supervised by qualified healthcare professionals.

Example: A patient enrolled in an AI-assisted oncology trial is explained how AI selects optimal drug combinations, and gives consent before any AI-guided intervention occurs.

Future Directions in AI Drug Discovery

The next decade promises transformational advances that could make drug discovery faster, cheaper, and more precise.

1. Quantum Computing + AI for Protein Folding

• Problem today: Protein folding simulations are computationally intensive and slow.

• Quantum computing advantage: Can process massive molecular configurations simultaneously.

• Integration with AI:

• AI models can guide quantum simulations to predict stable protein structures faster.

• Impact:

• Accelerates target identification and drug design for complex proteins.

Example: AlphaFold demonstrated AI’s ability to predict protein folding; quantum-assisted AI could solve even larger, more complex proteins in minutes.

2. Autonomous AI-Driven Laboratories

• Definition: Labs where AI controls experiment design, execution, and analysis with minimal human intervention.

• Benefits:

• Continuous operation (24/7)

• Reduced human error

• Faster iteration of compound synthesis and testing

• Impact:

• Could shrink the time from drug concept to lead optimization from years to months.

Example: Robotic labs combined with AI algorithms automatically generate, test, and analyze hundreds of compounds simultaneously.

3. Digital Twins for Virtual Patient Testing

• Concept: A digital twin is a virtual model of a patient based on genomics, physiology, and medical history.

• Use in drug discovery:

• Test drug responses virtually before clinical trials.

• Predict adverse reactions for specific patient profiles.

• Impact:

• Reduces risks in early trials

• Enables personalized therapy design

• Minimizes need for large animal or human testing initially

Example: A digital twin of a diabetic patient can simulate blood sugar response to different drug formulations.

4. Multimodal AI Integrating Genomics, Imaging, and Clinical Data

• Definition: Combines multiple data types genomics, proteomics, MRI/CT scans, HER to predict drug efficacy.

• Advantage:

• Provides holistic view of disease and drug response

• Improves accuracy of predictions for both drug design and personalized medicine

• Impact:

• Can detect complex interactions that single-modality AI may miss

• Facilitates precision medicine at scale

Example: In cancer therapy, AI combines tumor genomics, histopathology images, and patient lab records to predict which drug combination maximizes survival.

SUMMARY

Ethical & Regulatory Considerations:

• Ensure data privacy, fairness, explainability, and patient protection.

Future Directions:

• Quantum computing + AI

• Autonomous laboratories

• Digital twins

• Multimodal AI integration

These trends show AI not only accelerates drug discovery but also makes it safer, personalized, and more efficient, provided ethical and regulatory frameworks keep pace.

CONCLUSION

Artificial Intelligence (AI) has emerged as a transformative force in the field of drug discovery, fundamentally altering the traditional timelines, costs, and methodologies associated with pharmaceutical research. The integration of AI technologies such as machine learning, deep learning, reinforcement learning, and natural language processing has enabled researchers to analyze vast and complex datasets with unprecedented speed and accuracy. From target identification and virtual screening to de novo drug design and clinical trial optimization, AI has consistently demonstrated its ability to streamline each stage of drug development, reducing both time and resource expenditure.

The applications of AI in predicting ADMET properties, toxicity profiles, and patient-specific drug responses have particularly enhanced the safety and efficiency of early-stage drug development. Moreover, AI-driven platforms have proven instrumental in accelerating drug repurposing efforts, as exemplified during the COVID-19 pandemic, where existing drugs were rapidly evaluated for new therapeutic applications. Personalized medicine has also greatly benefited from AI, allowing treatments to be tailored to individual genetic, physiological, and clinical profiles, thereby maximizing therapeutic efficacy while minimizing adverse effects.

However, the rapid adoption of AI in drug discovery also introduces significant ethical, regulatory, and technical challenges. Ensuring data privacy, mitigating algorithmic bias, maintaining transparency for regulatory compliance, and safeguarding patient autonomy remain critical concerns that must be addressed to maintain public trust and scientific integrity. Regulatory agencies such as the FDA and EMA are actively developing guidelines to govern AI applications in drug research, emphasizing explainability, reproducibility, and patient safety.

Looking toward the future, innovations such as quantum computing, autonomous AI-driven laboratories, digital twins, and multimodal AI integration are poised to further revolutionize drug discovery. These advancements promise even faster protein structure predictions, virtual patient testing, continuous automated experimentation, and holistic analysis of complex biomedical data, paving the way for highly precise, efficient, and personalized therapeutics.

In conclusion, while challenges remain, AI is unequivocally transforming the landscape of drug discovery. By enabling faster, safer, and more cost-effective research, AI not only accelerates the journey from molecule to medicine but also lays the foundation for a future where treatments are highly individualized, ethically guided, and scientifically robust. The continued synergy between human expertise and AI-driven intelligence will define the next era of pharmaceutical innovation, promising improved healthcare outcomes globally.

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