AI in Genomic Data Analysis for Drug Development

International Journal of Pharmaceutical Sciences, 2024

Background: This abstract explores the pivotal role of artificial intelligence (AI) in revolutionizing drug development processes. Analysing vast datasets, AI facilitates target identification, drug screening, and prediction of compound interactions, expediting the discovery of novel therapeutics. The integration of AI technologies marks a transformative era in drug development, offering unprecedented opportunities for innovation and improved patient outcomes. In this review, we delve into the dynamic landscape of artificial intelligence (AI) within the realm of drug development. Our exploration encompasses a thorough examination of recent literature, dissecting how AI accelerates drug discovery, refines clinical trial processes, and propels personalized medicine initiatives. Additionally, we project into the future, unravelling potential implications that AI holds for the evolving landscape of drug development. Main body of the abstract: This review delves into AI-powered approaches, such as machine learning and deep learning GNNs, CNNs, DNNs showcasing their impact on optimizing drug design, minimizing development timelines, and enhancing overall efficiency in the pharmaceutical industry. By scrutinizing challenges and limitations, we elucidate the nuanced interplay between AI and pharmaceutical innovation. Short conclusion: AI in drug development has shown great promise in revolutionizing the pharmaceutical industry. By utilizing large datasets and advanced algorithms, AI can assist in predicting outcomes and identifying potential drug candidates. This review serves as a comprehensive guide for researchers, practitioners, and stakeholders navigating the complex and promising intersection of AI and pharmaceuticals.

International Journal of Applied Pharmaceutics, 2025

AI and Machine Learning are revolutionizing drug development, which was previously characterized as being complicated, time-consuming, and costly. Through the application of enormous biomedical data and high-performance computing capabilities, AI/ml algorithms speed up different aspects of drug discovery, such as target identification, lead optimization, and clinical trial design. The technologies uncover intricate biological patterns, making it possible to have personalized treatments and more effective drug targeting at the bedside. AI methods have demonstrated phenomenal success in protein structure prediction, virtual compound screening, and de novo drug design, shortening project duration from years to weeks. Some of the highlights are Alpha Fold's record-breaking accuracy in protein structure predictions by DeepMind and Deep Tox's 86% accuracy for predicting toxicity. Neural networks have reached 92% accuracy in predicting protein-protein interactions, while AI integrated with organ-on-chip systems has cut down early-stage drug screening time by 60% and enhanced prediction accuracy by 40%. Joint efforts such as melloddy allow the pharmaceutical industry to collaborate without compromising data privacy and security measures. However considerable issues remain around data quality, interpretability of models, and regulation compliance in the pharma industry. Advances in AI in the future, fueled by cross-disciplinary cooperation and ongoing technological progress, will play a vital role in surmounting these issues and making AI/ml bring about revolutionary changes in drug discovery to make therapies more available and affordable to global patients. The science keeps on unfolding, tipping the balance between potential transformation and built-in complexities in pharmaceutical usage.

Machine learning and Artificial Intelligence have significantly advanced in recent years owing to their potential to considerably increase the quality of life while reducing human workload. The paper demonstrates how AI and ML are used in the drug development process to shorten and enhance the overall timeline. It contains pertinent information on a variety of Machine Learning approaches and algorithms that are used across the whole drug development process to speed up research, save expenses, and reduce risks related to clinical trials. A range of QSAR analysis, hit finding, and de novo drug design applications are used in the pharmaceutical industry to enhance decision-making. As technologies like high-throughput screening and computation analysis of databases used for lead and target identification and development create and integrate vast volumes of data, machine learning and deep learning have grown in importance. It has also been emphasized how these cognitive models and tools may be used in lead creation, optimization, and thorough virtual screening. In this paper, problem statements and the corresponding state-of-the-art models have been considered for target validation, prognostic biomarkers, and digital pathology. Machine Learning models play a vital role in the various operations related to clinical trials embracing protocol optimization, participant management, data analysis and storage, clinical trial data verification, and surveillance. Post-development drug monitoring and unique industrially prevalent ML applications of pharmacovigilance have also been discussed. As a result, the goal of this study is to investigate the machine learning and deep learning algorithms utilised across the drug development lifecycle as well as the supporting techniques that have the potential to be useful.

International Journal of Molecular Sciences

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various ...

Big Data and Cognitive Computing

Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace. This review elaborates on the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from domains such as PubMed, Science Direct and Google scholar using specific keywords and phrases such as ‘Artificial intelligence’, ‘Pharmaceutical research’, ‘drug discovery’, ‘clinical trial’, ‘disease diagnosis’, etc. to select the research and review articles published within the last five years. The application of AI in disease diagnosis, digital therapy, personalized treatment, drug discovery and forecasting epidemics or pandemics was extensively reviewed in this article. Deep learning and neural networks are the most used AI technologies; Bayesian nonparametric models are the poten...

2020

Over the past few years, pharmaceutical R&D has become aware of the potential benefits of leveraging artificial intelligence and its collective subfields including machine learning, deep learning, data science and advanced analytics. These technologies are being embraced across industries to provide enhanced automation, gain insights into data, and improve data-driven decision making. The evangelization from lower level technical experts has now been echoed by the top levels of many organizations, as exemplified by Vas Narasimhan’s (Novartis CEO) goal to evolve AI to place it at the “heart of the company” [1] and Alex Bourla’s (Pfizer CEO) aim to win the digital race in pharma using machine learning and AI to expedite R&D [2]. Although its value compared to pure science continues to be questioned, machine learning and particularly deep learning have introduced many compelling use cases.

Membrane Technology, 2025

The paper investigates how AI technologies, particularly machine learning, with deep learning and natural language processing, transform drug development processes. The pharmaceutical R&D sector transforms its operations through artificial intelligence technology implementation into industry operations. Pharmaceutical R&D operates more efficiently with AI implementation because they can reach drug market launches quickly with precise drug development results obtained at reduced research costs through shorter product development frameworks. This initial part highlights how conventional research development encounters increasing barriers due to high project fees and numerous unsuccessful outcomes AI intervention becomes essential. The analysis combines literature research, pharmaceutical case studies and expert professional interviews to develop findings. The research employed data derived from peer-reviewed publications and corporate reports as well as AI conference papers. The research studies how AI is implemented through three core R&D segments that concentrate on target discovery together with drug transformation operations alongside clinical trial optimization initiatives. The evaluation of drug development acceleration examines two advanced tools, which consist of AIpowered molecular modeling and predictive analytics. Pharmaceutical research development speeds quickly transform through AI systems, which improve operational effectiveness across drug discovery and development processes. AI technology has proven to reduce pharmaceutical timelines while decreasing production costs while simultaneously improving predictive abilities for decision support. Pharmaceutical R&D organizations speed up development processes with AI technology to address critical global health needs, according to research findings. AI exploitation in the pharmaceutical sector needs a complete evaluation of ethical components with regulatory structures and expert workforce deployment approaches.

Asian Journal of Applied Science and Technology (AJAST) , 2025

Artificial Intelligence (AI) has become an integral component of pharmaceutical sciences, transforming drug discovery, development, and patient care. This review examines the historical context of AI in pharmaceuticals, its current applications, and prospects. The present explore the evolution from early rule-based systems to sophisticated machine-learning techniques that analyze vast datasets, enhancing efficiency and accuracy. Key applications include drug discovery, clinical trial optimization, personalized medicine, and drug manufacturing. However, the integration of AI poses challenges such as data privacy, bias, and regulatory compliance. By addressing these concerns and fostering collaboration among stakeholders, the pharmaceutical industry can harness the full potential of AI to improve patient outcomes and streamline processes. This review highlights AI's ongoing advancements and future directions in pharmaceutical sciences.

Review Article , 2025

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

2023

Drug discovery is an extended and challenging procedure with four primary stages: (i) target selection and validation; (ii) compound screening and lead optimization; (iii) preclinical research; and (iv) clinical trials. The combination of artificial intelligence (AI) with new experimental technologies is intended to improve the search for novel drugs faster, cheaper, and more effective. AI is the term used to describe the intelligence produced by human-made machines. It is a broad field of study that encompasses languages, cybernetics, neurophysiology, psychology, and computer science. The drug development company is attracted to Artificial Intelligence technologies because of their robotic nature, predictive powers, and the resulting anticipated gain in productivity. Using feature-finding strategies, unsupervised machine learning can provide outcomes such as disease target and illness subtype detection. A new AI diagnostic tool is aware of when to consult a doctor. The ability of a new artificial intelligence diagnostic system to acknowledge its own limitations and seeks the aid of a carbon-based lifeform that may be able to make a more accurate judgment. The pharmaceutical and medical industries are anticipated to undergo a revolution due to artificial intelligence, according to a forecast rise of 40 percent from 2017 to 2024.Global pharmaceutical industry is working with artificial intelligence organization to build not only vital healthcare tools and drug molecules for rare disease but also for market research.