The Coming Boom In Drug Discovery (original) (raw)
Tapping into the drug discovery potential of AI
Biopharma Dealmakers, 2021
In April of this year, the German biotechnology company Evotec announced a phase 1 clinical trial on a new anticancer molecule. The candidate was created in partnership with Exscientia, a company based in Oxford, UK, that applies artificial intelligence (AI) techniques to small-molecule drug discovery. Where it might have taken the traditional discovery process 4-5 years to come up with the drug candidate-an A2 receptor antagonist designed to help T cells fight solid tumors-it was found in 8 months by harnessing Exscientia's 'Centaur Chemist' AI design platform. This system can computationally sort through and compare various properties of millions of potential small molecules, looking for 10 or 20 to synthesize, test and optimize in lab experiments before selecting the eventual drug candidate for clinical trials. Within 3 weeks, Exscientia, a 2012 spinoff of the University of Dundee, UK, announced a 225millionSeriesDfinancinground(Table1),alongwitha225 million Series D financing round (Table 1), along with a 225millionSeriesDfinancinground(Table1),alongwitha300 million equity commitment that it can draw on at its discretion. In 2020, the company did much the same, announcing a drug candidate with Sumitomo Dainippon Pharma in Osaka, Japan, then later raising 100millioninSeriesCfunding.Thatdrug,aselectiveserotoninreuptakeinhibitor(SSRI)designedtotreatobsessivecompulsivedisorder(OCD),andtheoncologydrugarethefirsttwomoleculesdesignedwiththehelpofAItoenterclinicaltrials,Exscientiaclaims.ThecompanyhasalsoformeddrugdiscoverypartnershipswithBristolMyersSquibb(BMS),Sanofi,Bayer,GlaxoSmithKline,RocheandtheUniversityofOxford(Table2),andisbuildingitsownpipeline.ExscientiaisjustoneofmanycompaniesfoundedinthepastdecadearoundAI−basedstrategiesfordrugdiscoveryanddevelopment,severalofwhichhaveraisedsubstantialfundingrecently(Table1).Someofthesearealsodevelopingtoolstoacceleratetheidentificationofsmall−moleculedrugcandidates.OtherssuchasRecursionPharmaceuticals,whichrecentlyraised100 million in Series C funding. That drug, a selective serotonin reuptake inhibitor (SSRI) designed to treat obsessive compulsive disorder (OCD), and the oncology drug are the first two molecules designed with the help of AI to enter clinical trials, Exscientia claims. The company has also formed drug discovery partnerships with Bristol Myers Squibb (BMS), Sanofi, Bayer, GlaxoSmithKline, Roche and the University of Oxford (Table 2), and is building its own pipeline. Exscientia is just one of many companies founded in the past decade around AI-based strategies for drug discovery and development, several of which have raised substantial funding recently (Table 1). Some of these are also developing tools to accelerate the identification of small-molecule drug candidates. Others such as Recursion Pharmaceuticals, which recently raised 100millioninSeriesCfunding.Thatdrug,aselectiveserotoninreuptakeinhibitor(SSRI)designedtotreatobsessivecompulsivedisorder(OCD),andtheoncologydrugarethefirsttwomoleculesdesignedwiththehelpofAItoenterclinicaltrials,Exscientiaclaims.ThecompanyhasalsoformeddrugdiscoverypartnershipswithBristolMyersSquibb(BMS),Sanofi,Bayer,GlaxoSmithKline,RocheandtheUniversityofOxford(Table2),andisbuildingitsownpipeline.ExscientiaisjustoneofmanycompaniesfoundedinthepastdecadearoundAI−basedstrategiesfordrugdiscoveryanddevelopment,severalofwhichhaveraisedsubstantialfundingrecently(Table1).Someofthesearealsodevelopingtoolstoacceleratetheidentificationofsmall−moleculedrugcandidates.OtherssuchasRecursionPharmaceuticals,whichrecentlyraised436 million in Tappin g into the drug discovery potential of AI Plentiful financing and multiple pharma partnerships illustrate the burgeoning interest in applying artificial intelligence tools to drug research and development. Table 1 | Selected recent fi nancings of companies applying AI in drug discovery Company Date Headline Schrödinger February 2020 Drug discovery software company closes 232millionIPObackedbyBillGatesandDavidShaw.InsitroMay2020Insitroraises232 million IPO backed by Bill Gates and David Shaw. Insitro May 2020 Insitro raises 232millionIPObackedbyBillGatesandDavidShaw.InsitroMay2020Insitroraises143 million in Series B funding, to help drive its machine learning-based drug discovery approaches further. AbCellera May 2020 AbCellera raises 105millioninSeriesBfundingroundtoexpanditsantibodydrugdiscoveryplatform.RelayTherapeuticsJuly2020RelayTherapeutics,whichfocusesonunderstandingproteinmotiontodesigndrugcandidates,closes105 million in Series B funding round to expand its antibody drug discovery platform. Relay Therapeutics July 2020 Relay Therapeutics, which focuses on understanding protein motion to design drug candidates, closes 105millioninSeriesBfundingroundtoexpanditsantibodydrugdiscoveryplatform.RelayTherapeuticsJuly2020RelayTherapeutics,whichfocusesonunderstandingproteinmotiontodesigndrugcandidates,closes400 million IPO. Atomwise August 2020 Sanabil Investments co-leads 123millionSeriesBfundingroundforAtomwisetosupportthedevelopmentofitsmoleculeidentificationsoftware.RecursionPharmaceuticalsSeptember2020RecursionPharmaceuticals,whichisapplyingmachinelearningtocellularimagingdata,raises123 million Series B funding round for Atomwise to support the development of its molecule identifi cation software. Recursion Pharmaceuticals September 2020 Recursion Pharmaceuticals, which is applying machine learning to cellular imaging data, raises 123millionSeriesBfundingroundforAtomwisetosupportthedevelopmentofitsmoleculeidentificationsoftware.RecursionPharmaceuticalsSeptember2020RecursionPharmaceuticals,whichisapplyingmachinelearningtocellularimagingdata,raises239 million in Series D fi nancing round led by Bayer's investment department Leaps. Other investors include Casdin Capital, Samsara BioCapital, Baillie Giff ord and Lux Capital. XtalPi September 2020 More than a dozen investment companies raise $318 million in Series C round for start-up XtalPi, which is applying quantum physics with AI to discover drug candidates.
International Journal of Multidisciplinary Sciences and Arts, 2023
The fusion of machine learning (ML) and artificial intelligence (AI) is experiencing a dramatic transition in the field of pharmaceutical research and development. This study examines the several effects of machine learning (ML) on different phases of medication discovery, development, and patient care. The capability of ML to quickly process huge chemical libraries and forecast interactions with target proteins is studied, starting with compound screening and selection. The potential for fewer false positives and negatives, improved hit prediction accuracy, and ensemble technique use are underlined. The part that machine learning plays in enhancing Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profile is then explained. ML models anticipate compound actions inside the human body by analyzing molecular structures and characteristics, improving assessments of drug safety and efficacy. The article goes into further detail about predictive modeling, highlighting how machine learning may be used to find prospective therapeutic targets and confirm their applicability. The combination of multi-omics data, deep learning, and the possibility to identify similar molecular pathways across diseases highlight the game-changing potential of machine learning in this field. The article also covers the use of ML in clinical trials, highlighting its benefits for trial planning, patient recruitment, real-time monitoring, and individualized therapy predictions. By utilizing computational analysis and quantum physics, the power of machine learning-driven de novo drug creation is examined, revealing the potential to develop new therapeutic candidates. In this article, the ethical issues surrounding AI-driven drug discovery are discussed, with a focus on the necessity of transparent data utilization, human oversight, and responsible data consumption. The report ends by predicting ML's potential for pharmaceutical R&D in the future. Accelerated drug discovery pipelines, the rise of customized medicine powered by predictive models, optimized clinical trials, and a change in medication repurposing tactics are all envisaged in this. The report emphasizes the revolutionary potential of ML in altering pharmaceutical research and development while noting obstacles in data quality, model interpretability, ethics, and interdisciplinary collaboration. It is suggested that the ethical integration of AI technologies, interdisciplinary cooperation, and regulatory modifications are essential steps to unlock the full potential of ML and AI and, ultimately, provide patients throughout the world with safer, more efficient, and individualized treatments.
Application of Machine Learning in Drug Discovery and Development Lifecycle
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.
ACCELERATING PHARMACEUTICAL INNOVATION: THE IMPACT OF AI ON DRUG DISCOVERY
IAEME PUBLICATION, 2024
The integration of artificial intelligence (AI) and machine learning technologies in drug discovery has the potential to revolutionize the pharmaceutical industry. Traditional drug discovery processes are time-consuming, costly, and often inefficient, with the average drug development timeline spanning over a decade and costs exceeding billions of dollars. However, AI-powered approaches offer a transformative solution by efficiently analyzing vast amounts of data, predicting molecular interactions, and identifying promising drug candidates. This article explores the various methodologies employed in AI-driven drug discovery, including deep learning models, virtual screening, molecular docking, and generative models. The results section talks about several successful case studies that show how AI can speed up the search for new therapeutic agents. For example, powerful inhibitors for cancer-related enzymes were found, and drug candidates for obsessive-compulsive disorder and antibiotic-resistant bacteria were made very quickly. Despite the promising results, challenges such as data quality, model interpretability, and experimental validation need to be addressed to fully realize the potential of AI in drug discovery.
The AI-Driven Future of Drug Discovery: Innovations, Applications, and Challenges
Asian Journal of Research in Pharmaceutical Sciences, 2025
AI is notably overcoming the long-standing problems, such as high costs, prolonged timelines, and complex biological data analysis, in drug discovery, which in turn is revolutionising the pharmaceutical industry. This article is aimed at making readers realise the significance of AI in drug discovery and what actual changes it has triggered by innovating in areas of target identification, virtual screening, automated drug design, compound optimisation, and biomarker discovery. AI applications, like deep learning or generative models, are now moving much faster and are more accurate in the identification of potential drug targets, while AI-powered virtual screening is the advanced method that makes possible lead identification by the prediction of ligand-receptor binding affinities. For automated drug design tools, generative adversarial networks (GANs) are used for optimising the properties of new molecules, thereby producing the most effective drugs, and reinforcement learning allows the reduction of the possible side effects to further improve the quality of compounds. Biomarker discovery, which is powered by AI, helps in precision medicine by allowing patient stratification and optimisation of clinical trials. Nevertheless, the difficulties in this matter are still poignant. Data handling, transparency in models, regulation uncertainties, and ethical problems such as privacy and bias limit AI in drug development. Collaboration of data sharing among the organisations and the progress in the regulatory frameworks are the most important points to be addressed to solve these issues. Despite these drawbacks, the future of artificial intelligence applications is quite bright, showing possibilities to decrease the spending on R&D, cut the timelines for drug development, deliver precision medicines that improve patients' outcomes, and spur the world's global healthcare solutions.
Investigating the Impact of Machine Learning in Pharmaceutical Industry
Journal of Pharmaceutical Research International
In the pharmaceutical and consumer health industries, artificial intelligence and machine learning played an important role. These technologies are critical for the identification of patients with improved intelligence applications, such as disease detection and diagnostics for clinical testing, for medicine production and predictive forecasts. In recent years, advances in numerous analysis tools and machine learning algorithms have led to novel applications for machine learning in several areas of pharmaceutical science. This paper examines the past, present, and future impacts of machine learning on several areas, including medicine design and discovery. Artificial neural networks are employed in pharmaceutical machine learning because they can reproduce nonlinear interactions typical in pharmaceutical research. AI and learning machines are examined in everyday pharmaceutical needs, industrial and regulatory insights.
Artificial intelligence in drug discovery
Future medicinal chemistry, 2018
While many of the new approaches have yet to bear fruit in terms of drugs being progressed to market, initial reports tend toward the belief that they will become even more integral in the drug discovery process than has hitherto been seen. "
New-Generation Drug Discovery using Machine Learning
International Journal for Research in Applied Science and Engineering Technology, 2023
Finding innovative molecules with specific chemical properties to treat diseases is one of the goals of drug discovery. Recent years have seen the production of a sizable volume of biological data from many sources. These statistics and molecular analyses have been used to determine the most effective medications. Medical research often frustrates people and is far more expensive. The work at hand is made easier by having the ability to predict whether a medicine will be active or not. The information about the drug can also be used to develop other drugs. One application that makes use of machine learning to enhance decision-making in pharmaceutical data across numerous applications is quantitative structure activity relationship (QSAR) analysis. Machine learning-based predictive models have recently gained a lot of attention in areas outside of preclinical research. Costs and research times associated with finding new drugs are considerably decreased at this stage. Drug research is growing and more commonly utilising machine learning, algorithms for pattern recognition, knowledge of mathematical correlations, and knowledge of the chemical and biological characteristics of molecules. The necessity for a sizable volume of data, the incapacity to interpret the data, and other issues are further restrictions. Without the need for computational resources, massive amounts of data can be analysed using both physical models and machine learning approaches.
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.
Revolutionizing Drug Discovery: Harnessing Machine Learning Algorithms
International Journal For Multidisciplinary Research, 2024
Drug discovery is a crucial element of biomedical research, with the goal of finding and creating new medical treatments for a variety of illnesses. Yet, the conventional process of finding new drugs is frequently impeded by its intrinsic difficulties, such as expensive expenses, long durations, and poor success rates in trials with patients. Recently, the incorporation of machine learning (ML) algorithms has become a revolutionary method to streamline and improve different phases of drug discovery. This summary offers a glimpse into the rapidly growing area of drug discovery using machine learning algorithms, emphasizing its potential to transform the process of developing treatments. The usual process of discovering drugs involves various stages such as identifying the target, finding lead compounds, conducting preclinical tests, undergoing clinical trials, and obtaining regulatory approval. All these phases require a lot of labor, time, and resources, leading to high attrition rates and limited success in turning potential compounds into approved therapies. Nevertheless, researchers can enhance and speed up crucial parts of the drug discovery process by using ML algorithms. ML algorithms use data to aid in drug discovery by utilizing computational models to examine large quantities of biological, chemical, and clinical data. These algorithms can learn from various types of data, such as genomic data, chemical structures, protein interactions, and clinical outcomes, to discover hidden patterns, find new targets for drugs, and forecast the effectiveness and safety of potential treatments. Moreover, machine learning algorithms allow for the investigation of intricate connections between molecular structures and biological effects, making it easier to create improved drug candidates with better effectiveness and specificity. Important uses of machine learning in pharmaceutical research involve finding and confirming targets, screening compounds and improving leads, repurposing drugs, and tailoring treatments for individuals. Commonly used for classification and regression tasks, supervised learning algorithms like support vector machines and random forests predict compound activity, toxicity, and pharmacokinetic properties. Clustering and dimensionality reduction techniques utilized in unsupervised learning algorithms help analyze vast datasets and discover new drug-target interactions. Advanced abilities for analyzing molecular structures, virtual screening, and designing new drugs are provided by deep learning models like convolutional neural networks and recurrent neural networks. Multiple case studies demonstrate how ML algorithms can significantly impact drug discovery. Collaboration among academia, industry, and research institutions has resulted in the creation of new MLbased methods for drug development, identifying targets, and categorizing patients. Nevertheless, there are challenges accompanying the widespread use of ML in drug discovery. In healthcare, it is crucial to address ethical considerations, regulatory hurdles, and data privacy concerns to ensure the responsible and ethical use of ML algorithms.
Artificial intelligence in drug development: present status and future prospects
Artificial intelligence (AI) uses personified knowledge and learns from the solutions it produces to address not only specific but also complex problems. Remarkable improvements in computational power coupled with advancements in AI technology could be utilised to revolutionise the drug development process. At present, the pharmaceutical industry is facing challenges in sustaining their drug development programmes because of increased R&D costs and reduced efficiency. In this review, we discuss the major causes of attrition rates in new drug approvals, the possible ways that AI can improve the efficiency of the drug development process and collaboration of pharmaceutical industry giants with AI-powered drug discovery firms.
Artificial Intelligence: A New Era in Drug Discovery
Asian Journal of Pharmaceutical Research and Development, 2021
Artificial intelligence (AI) is a simulation of the process of human intelligence through computers. The process involves obtaining information, developing rules for using information, making possible or accurate conclusions, and self-correcting. The development of new drug residues begins when basic scientists learn about biological targets (receptor, enzyme, protein, and gene). These targets involve the biological processes that occur in patients with a disease. Drug discovery can be through target identification, target verification, lead identification, and effectiveness of lead. AI can offer revolutionary insights into medicine, through data from genetics, proteomics and other life sciences that advance the process of discovery and development. Artificial Intelligence (AI) has recently been developed as a fiery element in the medical care industry. AI has exciting potential for prosperity in the field of biopharmaceutical. The biopharmaceutical industry makes efforts to approac...
Drug Discovery Using Machine Learning and Data Analysis
International Journal for Research in Applied Science and Engineering Technology (IJRASET), 2022
An objective of drug discovery is to identify novel substances with certain chemical properties for the treatment of diseases. A significant amount of biological data has been produced recently from a variety of sources. Using this data, molecular analysis has been used to determine the most successful treatments. Trial-and-error medicine is frequently frustrating and significantly more expensive. This makes it easier to complete the work by predicting whether a drug will be active or not. The information about the drug can also be used to develop new medications. Quantitative Structure Activity Relationship (QSAR) analysis is one application that uses machine learning to improve decision-making in pharmaceutical data across multiple applications. Predictive models based on machine learning have recently grown substantially in prominence with in phase beyond preclinical research. In this stage, new drug discovery expenses and research times are significantly reduced. Utilizing pattern recognition algorithms, deciphering mathematical correlations, chemical and biological features of compounds, and machine learning has been used for drug development increasingly and more frequently, with positive outcomes. Other restrictions include the necessity for a large volume of data, a lack of interpretability, etc. Machine learning approaches are comparable to physical models in that they may be applied to large data sets without the need for computational resources.
AI Assistance in the Drug Development Process: Reaching for a Regulatory Framework
Seton Hall law review, 2024
The process of discovering and developing a new drug takes approximately six to ten years; 1 however, in April 2021, the UK-based company Exscientia-partnered with German biotechnology company Evotech-announced that after just eight months of research, their new anticancer molecule entered Phase I of clinical trials. 2 This accelerated timeline can be attributed to the application of artificial intelligence (AI) techniques to the drug discovery process. 3 This feat marks Exscientia's second AI-designed drug to enter into Phase I of clinical testing. 4 Other companies have also tapped into AI within the drug discovery context. In February 2022, the pharma-technology company Insilico Medicine announced the beginning of "the world's first Phase
Machine Learning Techniques in Drug Discovery and Development
International Journal of Applied Research, 2021
The advancement and progress in technology and related techniques have created an opportunity for progress in many scientific fields and various industries. Machine learning has become important tool for drug designs and discovery with the availability of bit data from large databases. IN this paper I analyze Machine Learning and Deep learning techniques which help Pharma industry in all stages of drug discovery which includes target validation, prognostic biomarkers, clinical trials. Introduction Machine Learning methods and techniques and tools are used to help solve diagnostic and prognostic problems in wide medical domains. It is also being used to analyze the importance of clinical parameters and their combination for prognosis. Advancements in technology in the medicinal field have accelerated development in medical field and also accuracy. Machine learning has influenced wide variety of task in modeling and cheminformatics like synthesis planning [1] , toxicity prediction, and virtual screening. Artificial intelligence is being widely used in medicinal field and in drug and pharmaceutical field. Machine Learning is a subfield of AI and requires computational and mathematical theory. Machine learning is based on developing models from the exposure to training data. Machine learning now a days can be used with wide variety of data types, and methods, like imaging, protein structures, and instead of being restricted to certain data types previously like protein sequences and compounds. Use of machine learning for drug discovery has been growing continuously, which is yielding good results by using pattern recognition algorithms, discerning, mathematical relationships between empirical observations of small molecules and extrapolate them to predict chemical, biological and physical properties of novel compounds in contrast to the models which rely on explicit physical equations [2]. There are also certain limitations like need for huge amount of data, lack of interpretability etc. In comparison to physical model's machine learning techniques can also be easily used on big data sets without having the need for computational resources., There are totally seven phases in the process of drug discovery. Let us discuss about the phases.
Artificial intelligence's essential role in the process of drug discovery
Future Drug Discovery
Three specific benefits of AI-based approaches for the drug discovery process are: an automation of manual and labor intensive tasks, an ability to process, integrate and make sense of the large volumes of complex and disparate biomedical and regulatory data and an AI-enhanced process of knowledge discovery, which is especially important in the early phases of drug discovery "
Using Artificial Intelligence for Drug Discovery: A Bibliometric Study and Future Research Agenda
Pharmaceuticals
Drug discovery is usually a rule-based process that is carefully carried out by pharmacists. However, a new trend is emerging in research and practice where artificial intelligence is being used for drug discovery to increase efficiency or to develop new drugs for previously untreatable diseases. Nevertheless, so far, no study takes a holistic view of AI-based drug discovery research. Given the importance and potential of AI for drug discovery, this lack of research is surprising. This study aimed to close this research gap by conducting a bibliometric analysis to identify all relevant studies and to analyze interrelationships among algorithms, institutions, countries, and funding sponsors. For this purpose, a sample of 3884 articles was examined bibliometrically, including studies from 1991 to 2022. We utilized various qualitative and quantitative methods, such as performance analysis, science mapping, and thematic analysis. Based on these findings, we furthermore developed a resea...
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.
A Review On Role Of Artificial Intelligence In 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.