Arnav Arora. A Comprehensive Study on AI in Drug Discovery

Arnav Arora

Review Article

Austin Med Sci. 2025; 10(1): 1079.

A Comprehensive Study on AI in Drug Discovery

Arnav Arora*

Department of Sciences, Vit Campus, Jagatpura, Jaipur, India

*Corresponding author: Arnav Arora, Department of Sciences, Vit Campus, Jagatpura, Jaipur, India Email: 23bsc2fs004@vgu.ac.in

Received: April 03, 2025 Accepted: April 17, 2025 Published: April 22, 2025

Abstract

Artificial Intelligence (AI) has become a disruptive force in pharmaceutical sciences and forensic applications, primarily because of its associated enhancement in efficiency, accuracy, and cost reduction. This review explains in detail the role of AI across different stages of drug discovery, from target identification to lead compound generation, optimization, preclinical validation, clinical trial design, and drug repurposing. It focuses on using machine learning, deep learning, and natural language processing to analyze complex chemical and biological datasets, predict molecular interactions, and identify novel drug candidates. It also discusses AI’s potential in solving global health crises such as pandemics with the help of rapid drug repurposing.

Forensic science is revolutionized with AI-driven platforms in detecting, characterizing, and profiling illicit substances, counterfeit drugs, and toxicological contaminants. By leveraging advanced algorithms, forensic scientists can determine chemical compositions with accuracy, trace their origins, and enhance the reproducibility of analyses. Such enhancement in forensic investigation strengthens criminal justice outcomes.

While such progress has been made, significant challenges persist concerning data quality, model interpretability, and ethical considerations. The requirement felt by many for more transparency and collaborative frameworks is underscored by issues of biased datasets, regulatory compliance, and the “blackbox” nature of AI models. The paper has stressed the need for interdisciplinary efforts in biology, chemistry, computer science, and policy-making to approach these challenges.

Prospects include multimodal data integration, advancements in realtime substance detection, and AI-driven innovations in personalized medicine and criminal behavior analytics. This review demonstrates the transformative potential for AI in reshaping healthcare and forensic science but also provides a call for responsible and ethical implementation in maximizing its benefits to global health and justice systems.

Introduction

Over the past few decades, rapid advancements in artificial intelligence have situated it as a transformational force across a wide swath of scientific and industrial domains. Of these, pharmaceutical sciences and forensic investigations stand out as those disciplines where AI has had the most profound influence on traditional workflows, driving innovation and reshaping processes once considered timeconsuming, cost-intensive, and prone to human error.

Drug discovery has conventionally been a very time-consuming and costly process, entailing high investments of time and money. The conventional pipeline—from target identification to lead compound generation, optimization, preclinical validation, and clinical trial design—can take more than a decade and cost more than a billion dollars. Bringing AI into the workflow has introduced a paradigm shift, adding precision, reducing timelines, and significantly cutting costs. AI-driven platforms use machine learning, deep learning, and natural language processing to mine large datasets, identify new targets, and predict molecular interactions with remarkable accuracy. This will also enable drug repurposing, the rapid identification of new therapeutic uses of existing drugs—a necessary ability in the case of an emerging global health crisis, such as that presented by pandemics.

Parallel to its applications in drug discovery, AI has also emerged as a game-changer in forensic science. Traditional forensic methodologies often involve complex analyses of biological and chemical evidence, requiring meticulous interpretation by experts. AI-based systems enhance these processes by automating data analysis, improving the accuracy and reliability of results, and enabling faster decisionmaking. For instance, ML algorithms can process spectral data from techniques like gas chromatography-mass spectrometry (GC-MS) and infrared spectroscopy to identify illicit substances, trace their origins, and establish links between evidence and criminal activity. The integration of AI into drug discovery and forensic science is not without its challenges, however: poor data quality and availability, model interpretability, and regulatory compliance are only some of the most critical barriers to wider adoption. Ethical considerations such as algorithmic bias and potential misuse of technologies also underscore the requirement felt by many for more transparent and collaborative approaches in AI development and deployment.

This review aims to offer an in-depth analysis of the contributions AI has made to drug discovery and forensic science. The technology and recent innovations in the field are considered, along with key applications and successes. This paper strongly advocates for interdisciplinary collaboration among researchers, clinicians, forensic experts, policy thinkers, and technologists to harvest the full potential of AI. Lastly, the study envisions a future where AI will continue transforming health and criminal justice, achieving greater efficiency, accuracy, and fairness in its deliverables for society.

AI in Drug Discovery

Target Identification

AI facilitates the fast mining of genomic, proteomic, and metabolomic data for identifying potential biological targets with high precision. Disease pathways are deconstructed by AI models that simulate protein-protein interactions and predict drug targets. Highpotential targets are prioritized by advanced algorithms, thereby reducing time and resources in initial discovery [1].

Compound Screening and Lead Optimization

AI-powered high-throughput screening evaluates huge chemical libraries to identify promising candidates. Machine learning models predict compound efficacy, toxicity, and bioavailability with accuracy for the fast-track optimization of lead compounds. Similarly, AIdriven generative models design novel compounds with better pharmacological properties for better candidate selection for drug development [2].

Preclinical and Clinical Validation

AI enhances preclinical studies by predicting drug interactions, and adverse effects, and identifying biomarkers. AI optimizes clinical trial design, patient selection, and data analysis, which increases success rates while reducing costs. Predictive analytics allow for realtime changes during trial phases to improve efficiency and outcomes.

Drug Repurposing and Emerging Health Crises

AI identifies existing drugs that can be repurposed for new indications, tackling rare diseases and global health crises. For example, in the COVID-19 pandemic, AI tools analysed existing drug libraries and suggested potential treatments, speeding up response times. AI also predicts and models possible outbreaks, supporting proactive health measures [3-5].

Personalized Medicine and Precision Therapeutics

AI combines patient data, including genomics, lifestyle, and medical history, to develop personalized therapeutic strategies. Predictive algorithms ensure that the appropriate treatment is given to the appropriate patient, with fewer side effects and higher efficacy. AI also helps define subpopulations that benefit from specific drugs [6].

Regulatory and Compliance Support

AI supports regulatory compliance by automating documentation, monitoring adherence to guidelines, and predicting potential issues. NLP tools assist in analysing regulatory texts and streamlining approval processes. AI-driven tools also ensure post-marketing surveillance of drugs for safety and efficacy [7].

AI in Forensic Applications for Drug Discovery

Detection and Analysis of Illicit Drugs

AI provides effective identification and analysis of illicit drugs using data derived from advanced techniques, including GC-MS and infrared spectroscopy. Deep learning models can be applied to classify chemical compounds, predict properties of chemical compounds, and trace their geographical origin, therefore furthering drug intelligence efforts [8].

Substrate Tracking and Chemical Profiling

Machine learning tools help the work of forensic scientists in chemical profiling by processing analyses of seized-substance compositions. AI could actually map relations between drug samples, follow their paths of production and distribution, and provide actionable intelligence to law enforcement.

Toxicology and Contamination Testing

AI is very instrumental in forensic toxicology in the identification of unknown toxicants in biological samples. AI-driven platforms predict toxicity profiles and assess substance effects, helping determine causes of death, poisoning, or contamination.

Counterfeit Drug Detection

AI algorithms identify counterfeit drugs by analysing chemical compositions and visual characteristics. Using image recognition and chemical fingerprinting, AI can authenticate products, ensuring safety and preventing fraudulent drug circulation [9].

Predictive Drug Crime Analytics

AI models analyse historical drug-related data to predict emerging trends in drug production and distribution. These predictive tools help authorities allocate resources effectively and anticipate potential hotspots of illicit drug activity.

Personalized Drug Analysis

AI-enabled platforms offer personalized analysis of drug interacti ons with specific biological samples, helping investigators determine the exact impact of substances on individuals. This is especially useful in forensic case studies.

Rapid Screening and Automation

AI-driven robotic systems perform high-throughput screening for drugs and contaminants in complicated matrices, such as food, beverages, and biological fluids. Automation improves the accuracy of the process while reducing the time required in forensic laboratories [10].

Dilemmas in AI Applications

Data Quality and Availability

AI applications need good-quality data. In domains such as drug discovery and forensic science, incomplete, biased, or noisy datasets can result in inaccurate predictions or unreliable results; this may also lead to critical errors. Ensuring the accuracy, representativeness, and completeness of data remains a considerable challenge.

Model Interpretability

Most AI models are "black boxes," so their decision processes are not transparent. A lack of transparency blocks trust, regulatory approval, and user confidence. The development of interpretable models or explainable AI (XAI) will be important for widespread adoption and ethical use in most sensitive fields [11-15].

Regulatory and Ethical Factors

The applications of AI have to operate within very stringent regulatory frameworks, especially in health and legal scenarios. Issues related to algorithmic bias can reinforce inequity in society and give rise to ethical dilemmas. These can only be dealt with through effective governance frameworks and ethical guidelines for fairness, accountability, and inclusion.

Resource Intensity

AI systems often demand significant computational power and storage resources, leading to high costs and energy consumption. This can limit accessibility, particularly for smaller organizations or developing regions, and raises environmental sustainability concerns.

Integration with Human Expertise

Balancing AI decision-making with human judgment is critical. Over-reliance on AI can lead to automation bias, while under-utilization undermines its potential. Establishing effective collaboration between AI systems and human experts ensures optimal outcomes and accountability.

Cybersecurity Risks

AI systems are vulnerable to adversarial attacks, data breaches, and manipulation. Compromised systems can lead to catastrophic consequences, especially in sensitive applications like healthcare or national security. Robust cybersecurity measures are necessary to mitigate these risks.

Future Outlooks and Multidisciplinary Collaboration

Multimodal Data Integration

The data from diverse scientific fields is being integrated by AI systems to make them more accurate and widely applicable. For instance:

Genomics and Proteomics: Genetic and protein-level information combined for personalized medicine, which enables the early detection of diseases and targeted treatment.

Chemical Analyses: The use of AI in analysing complex chemical structures will lead to the expedited discovery of drugs and innovative materials.

Cross-domain Fusion: Integration of environmental, behavioural, and biological data creates comprehensive predictive models in healthcare and forensic applications.

Healthcare and Justice Innovation Using AI

Advances in AI are bringing changes to healthcare and justice:

Personalized Medicine: AI-driven diagnostic tools predict patient outcomes, optimize treatment regimens, and accelerate drug development.

Forensic Investigations: AI helps in the analysis of evidence, the identification of suspects, and in deciphering crime patterns.

Interdisciplinary Collaboration: Synergy among computer scientists, biologists, chemists, medical professionals, and forensic experts is critical for effective innovation in solving these complex challenges.

AI Expands to Investigate Forensic Materials

AI is changing the face of forensic science by introducing capabilities such as:

Real-time Substance Detection: Portable AI devices identify drugs, toxins, and explosives instantly.

Predictive Criminal Analytics: Machine learning models predict criminal behavior patterns, enhancing prevention and resource allocation.

Ethical Considerations and Bias Mitigation in AI

Bias in Data: Ensuring that the training data is unbiased and representative to avoid perpetuating systemic inequalities.

Privacy Concerns: Building secure systems that respect privacy while still leveraging sensitive health or forensic data.

Accountability Frameworks: Establishing clear guidelines for AI-driven decision-making in high-stakes fields like healthcare and law enforcement

Conclusion

With the arrival of Artificial Intelligence (AI), new dimensions in the realms of pharmaceutical and forensic sciences have emerged, overturning conventional methods and creating unprecedented leaps. Its incorporation into these fields has not only increased efficiency and precision but also unlocked new pathways of scientific research and application.

AI has revolutionized the development of pharmaceutical sciences in every step of drug discovery to development. From the identification of therapeutic targets and design of drug candidates to optimization in clinical trials, AI-driven technologies like deep learning models, machine learning algorithms, and natural language processing have significantly speeded up and refined these processes. For example, these tools allow scientists to analyze huge biological and chemical datasets with very high accuracy, predict molecular interactions, and also quickly identify promising drug candidates. The role of AI is important in drug repurposing during a global health crisis such as a pandemic.

Likewise, in the forensic sciences, AI has revolutionized the detection, analysis, and interpretation of evidence. AI systems bring a marked increase in speed and precision to the identification of illicit substances, chemical contaminants, and distinct chemical signatures. These capabilities contribute not only toward stronger forensic investigations but also to the enhancement in the reliability of evidence presented in criminal justice systems, ensuring fairness and accuracy within legal processes.

Challenges still remain. Challenges in data quality, model interpretability, ethical considerations, and regulatory compliance have to be tackled fully to harvest the benefits of AI. The key to effectiveness in AI models is the availability of high-quality and diverse datasets; any bias within the dataset may lead to inaccurate or unfair outcomes. In addition, the complexity of AI algorithms often makes them opaque, which brings about issues of transparency and accountability. Regulatory frameworks will need to change in order to fit the unique challenges that AI presents, ensuring that applications are safe and ethical.

In the final analysis, AI has the great potential to change the face of pharmaceutical and forensic sciences, pushing the boundaries of what was thought to be impossible. Its ability to increase efficiency, accuracy, and equity makes it a cornerstone of innovation in healthcare and criminal justice. By working through current challenges and fostering interdisciplinary collaboration, AI can open up new possibilities for scientific discovery and societal progress. A future with responsible and transparent integration of AI is not a possibility but an imperative for the advancement of these critical field.