Integrating Artificial Intelligence into Neurotherapeutics: A New Frontier in Drug Design and Precision Healthcare

Background/Objectives: The integration of Artificial Intelligence (AI) and Machine Learning (ML) in pharmaceutical research and development is transforming the industry by improving efficiency and effectiveness across drug discovery, development, and healthcare delivery. This review explores the diverse applications of AI and ML, emphasizing their role in predictive modeling, drug repurposing, lead optimization, and clinical trials. Additionally, the review highlights AI’s contributions to regulatory compliance, pharmacovigilance, and personalized medicine while addressing ethical and regulatory considerations. Methods: A comprehensive literature review was conducted to assess the impact of AI and ML in various pharmaceutical domains. Research articles, case studies, and industry reports were analyzed to examine AI-driven advancements in predictive modeling, computational chemistry, clinical trials, drug safety, and supply chain management. Results: AI and ML have demonstrated significant advancements in pharmaceutical research, including improved target identification, accelerated drug discovery through generative models, and enhanced structure-based drug design via molecular docking and QSAR modeling. In clinical trials, AI streamlines patient recruitment, predicts trial outcomes, and enables real-time monitoring. AI-driven predictive maintenance, process optimization, and inventory management have enhanced efficiency in pharmaceutical manufacturing and supply chains. Furthermore, AI has revolutionized personalized medicine by enabling precise treatment strategies through genomic data analysis, biomarker discovery, and AI-driven diagnostics. Conclusions: AI and ML are reshaping pharmaceutical research, offering innovative solutions across drug discovery, regulatory compliance, and patient care. The integration of AI enhances treatment outcomes and operational efficiencies while raising ethical and regulatory challenges that require transparent, accountable applications. Future advancements in AI will rely on collaborative efforts to ensure its responsible implementation, ultimately driving the continued transformation of the pharmaceutical sector.

The 2022 SLAS technology ten: Translating life sciences innovation.

Artificial Intelligence (AI) and Quantum Computing together are going to transform medical research, diagnostics and the simulation of treatment. Although it has already been applied to medical imaging, drug discovery and predictive analytics, AI needs large amounts of computations that are sometimes beyond the capacity of more traditional computing. Quantum computers due to their capability to do parallel processing over quantum states can be fast and efficient in unprecedented ways. The paper will discuss the combination of AI and quantum computing in the medical field, including the characterization of medical applications of AI where it complements quantum computing, an overview of the current medical applications of AI and quantum computing, and real-time diagnostics, genomics, drug discovery, molecular dynamics, and personalized medicine. We discuss the current issues state-of-art including quantum error correction, interpretability of models, and ethical implications and point toward future opportunities including digital twins, federated learning using quantum resources, and hybrid quantum-classical models. The eventuality of this integration might end up speeding up medical simulations, decreasing the expense of drug development and enhancing the patient outcomes across the globe.

Artificial Intelligence (AI) is a revolutionary technology with transformative potential, notably in the pharmaceutical sector. This abstract provides a comprehensive overview of AI's applications in pharmaceuticals, encompassing drug discovery, development, manufacturing, and healthcare. In drug discovery and development, AI expedites candidate identification and enhances safety and efficacy profiling through advanced data analysis, covering genomics, chemical structure, and clinical data. AI enables drug repurposing by unveiling hidden therapeutic connections in existing medications, reducing costs and timelines, and addressing unmet medical needs. Personalized Medicine is another AI-driven frontier, customizing treatment plans based on patient-specific data like genomics and medical history, enhancing treatment effectiveness. In Clinical Trial Optimization, AI streamlines trial design, patient recruitment, and monitoring speeding approval and reducing costs. AI automates drug manufacturing and quality control, ensuring high-quality products and preventing defects. AI aids in regulatory compliance through real-time monitoring and reporting. Ethical and legal considerations include data privacy and bias mitigation, demanding meticulous attention. Data Security is essential, considering sensitive patient data. Robust cybersecurity safeguards data integrity. In conclusion, AI promises to revolutionize the pharmaceutical sector, accelerating drug discovery, improving patient care, and enhancing manufacturing. However, successful implementation hinges on addressing ethical, legal, and security considerations, fostering collaboration among stakeholders and balancing innovation with responsibility. AI helps in enhancing productivity as well as increases the quality control of the products. In pharmaceuticals, AI also may increase the efficacy of the drug discovery process. It reduces the time of the drug discovery journey along with enhanced efficacy and efficiency of the developed products.

Background: The pharmaceutical sector is a critical component of healthcare, driving innovation in drug discovery, development, and delivery. With the increasing integration of artificial intelligence (AI), digital health technologies, and biotechnology, the industry is transforming rapidly. This review examines the key areas of the pharmaceutical industry and highlights the growing impact of AI in enhancing various processes, from drug discovery to clinical trials. To explore the applications of AI in drug discovery, development, manufacturing, clinical trials, personalized medicine, and regulatory compliance. This review also addresses the challenges, such as data privacy and interoperability, that accompany the adoption of AI in the pharmaceutical sector. Methods: A comprehensive review of existing literature and case studies on the application of AI in pharmaceutical research and operations was conducted. Key areas of focus include AI's role in predictive analytics, target identification, manufacturing, supply chain management, clinical trial optimization, and pharmacovigilance. Results: AI significantly enhances drug discovery by improving target identification, predictive modeling, and high-throughput screening. It optimizes manufacturing through real-time quality control and process automation. In clinical trials, AI facilitates patient recruitment and adaptive trial designs, while in personalized medicine, it enables biomarker discovery and treatment optimization. AI also supports regulatory compliance through automated monitoring and risk assessment. Conclusion: AI is transforming the pharmaceutical sector, making processes more efficient, precise, and tailored to individual patients. However, challenges such as data privacy, ethical considerations, and interoperability must be addressed to fully harness AI's potential. Standardization and collaboration will be essential in driving the next phase of innovation in pharmaceutical development and healthcare delivery.

Artificial intelligence (AI) is driving innovation in clinical pharmacology and translational science with tools to advance drug development, clinical trials, and patient care. This review summarizes the key takeaways from the AI preconference at the American Society for Clinical Pharmacology and Therapeutics (ASCPT) 2024 Annual Meeting in Colorado Springs, where experts from academia, industry, and regulatory bodies discussed how AI is streamlining drug discovery, dosing strategies, outcome assessment, and patient care. The theme of the preconference was centered around how AI can empower clinical pharmacologists and translational researchers to make informed decisions and translate research findings into practice. The preconference also looked at the impact of large language models in biomedical research and how these tools are democratizing data analysis and empowering researchers. The application of explainable AI in predicting drug efficacy and safety, and the ethical considerations that should be applied when integrating AI into clinical and biomedical research were also touched upon. By sharing these diverse perspectives and real-world examples, this review shows how AI can be used in clinical pharmacology and translational science to bring efficiency and accelerate drug discovery and development to address patients' unmet clinical needs.

Advanced AI and ML frameworks for transforming drug discovery and optimization: With innovative insights in polypharmacology, drug repurposing, combination therapy and nanomedicine.

Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep learning, and natural language processing. These advancements have greatly influenced drug discovery, development, and precision medicine. AI algorithms analyze vast biomedical data identifying potential drug targets, predicting efficacy, and optimizing lead compounds. AI has diverse applications in pharmacological research, including target identification, drug repurposing, virtual screening, de novo drug design, toxicity prediction, and personalized medicine. AI improves patient selection, trial design, and real-time data analysis in clinical trials, leading to enhanced safety and efficacy outcomes. Post-marketing surveillance utilizes AI-based systems to monitor adverse events, detect drug interactions, and support pharmacovigilance efforts.Machine learning models extract patterns from complex datasets, enabling accurate predictions and informed decision-making, thus accelerating drug discovery. Deep learning, specifically convolutional neural networks (CNN), excels in image analysis, aiding biomarker identification and optimizing drug formulation. Natural language processing facilitates the mining and analysis of scientific literature, unlocking valuable insights and information.However, the adoption of AI in pharmacological research raises ethical considerations. Ensuring data privacy and security, addressing algorithm bias and transparency, obtaining informed consent, and maintaining human oversight in decision-making are crucial ethical concerns. The responsible deployment of AI necessitates robust frameworks and regulations.The future of AI in pharmacological research is promising, with integration with emerging technologies like genomics, proteomics, and metabolomics offering the potential for personalized medicine and targeted therapies. Collaboration among academia, industry, and regulatory bodies is essential for the ethical implementation of AI in drug discovery and development. Continuous research and development in AI techniques and comprehensive training programs will empower scientists and healthcare professionals to fully exploit AI's potential, leading to improved patient outcomes and innovative pharmacological interventions.

Neurodegenerative diseases, including Alzheimer's, Parkinson's, and amyotrophic lateral sclerosis (ALS), represent major healthcare challenges worldwide. Despite advances in diagnosis and treatment, these conditions remain incurable, and there is a need for more effective management strategies. The integration of artificial intelligence (AI) in healthcare has emerged as a promising solution, offering new approaches to diagnosis, personalized treatment, and patient care. This paper explores the potential of AI to revolutionize the management of neurodegenerative diseases, with a focus on its synergistic role in pharmacy. By leveraging AI in drug discovery, personalized treatment plans, and clinical decision-making, AI can enhance therapeutic outcomes and improve patient quality of life. The study reviews the current landscape of AI applications in neurodegenerative disease management, with a focus on pharmacy-related interventions. The review includes AI-driven approaches in genomics, biomarkers, drug repurposing, and clinical trials. It also examines AI's role in optimizing pharmaceutical care, improving medication adherence, and tailoring treatments based on individual genetic profiles. AI has demonstrated its capability to analyze vast datasets, from genetic information to clinical records, to identify novel drug targets and predict patient responses to specific therapies. The use of AI in precision medicine has enabled more accurate diagnosis and has facilitated the development of personalized treatments for neurodegenerative diseases. Additionally, AI tools are enhancing medication management by providing personalized therapy adjustments and improving adherence. AI offers transformative potential for the future of neurodegenerative disease management. Its integration into pharmacy practice promises more effective, individualized treatments, accelerating drug discovery, and optimizing patient care. As AI technologies continue to advance, their role in managing complex neurological disorders will become increasingly vital.

Artificial Intelligence (AI) is transforming healthcare and biopharmaceutical industries by revolutionizing diagnostics, personalizing medicine, and accelerating drug discovery. This study examines the critical role of innovation management in integrating AI technologies to drive value creation in these sectors. Through a comprehensive review of literature from 2017 to 2025, including peer-reviewed articles, industry reports, and case studies, we explore the applications, challenges, and opportunities of AI in healthcare and biopharma. The findings reveal that AI has the potential to significantly enhance diagnostic accuracy, streamline clinical trials, and reduce the time and cost of drug development. For instance, AI-powered tools like machine learning algorithms are improving disease detection through advanced imaging, while predictive analytics are enabling personalized treatment plans based on genetic and clinical data. In biopharma, AI is accelerating drug discovery by identifying potential drug candidates and optimizing clinical trial designs, as demonstrated by platforms like Atomwise and Insilico Medicine. However, the integration of AI into healthcare and biopharma is not without challenges. Ethical considerations, data privacy concerns, and the need for robust regulatory frameworks remain significant barriers. Issues such as algorithmic bias, the "black box" problem, and the lack of standardized data further complicate AI adoption. Effective innovation management is essential to address these challenges, ensuring that AI technologies are deployed ethically and efficiently. Strategies such as public-private partnerships, capacity building, and the development of open-source AI solutions are crucial for scaling AI in low- and middle-income countries (LMICs), where healthcare disparities are most pronounced. By addressing these challenges, AI can drive transformative advancements in patient care, therapeutic development, and global health equity, paving the way for a more efficient, personalized, and inclusive healthcare ecosystem.

Advances in artificial intelligence (AI) and machine learning (ML) are transforming drug discovery by significantly reducing time and costs. This abstract highlights the AI/ML approaches employed in our lab at North Carolina Central University (NCCU), a Historically Black College and University (HBCU), to support drug repurposing efforts. Using Literature-Wide Association Studies (LWAS), a text-mining method, we analyzed over three million biomedical abstracts and identified 24 potential drugs as candidates for repurposing to treat inflammatory breast cancer (IBC), a rare and understudied disease. We also applied gene reversal rate (GRR) analysis—a computational approach that identifies drugs capable of reversing disease-associated gene expression profiles toward normal. By integrating disease gene expression profiles with drug-induced data from the Library of Integrated Network-based Cellular Signatures (LINCS), we predicted 19 additional candidate drugs for IBC. Currently, we are advancing our text-mining efforts by combining BioWordVec embeddings with LWAS to further expand our list of repurposing candidates for IBC. In parallel, we are utilizing the AIDDISON platform—an AI-driven tool that integrates generative AI, ML, and computer-aided drug design—to identify small-molecule inhibitors. Through similarity searches and molecular docking, we have discovered 23 potential inhibitors of the Hedgehog pathway transcription factor GLI1. Additionally, we employ artificial neural network (ANN) models for ligand discovery. These models were trained on more than 40,000 ligand–target pairs, incorporating IC50 and Ki values from BindingDB. The models link compound SMILES representations with target protein sequences to predict small molecules that may inhibit GLI1, a therapeutic target in several cancers. By leveraging AI/ML techniques, including LWAS, GRR, AIDDISON, and ANN, we aim to develop efficient, cost-effective, and rapid solutions for drug repurposing. These efforts support the discovery of new treatment options for rare diseases such as IBC and provide cutting-edge research and training opportunities for students and researchers at NCCU. Citation Format: Kevin P. Williams, Xiaojia Ji, Esraa Salim, Michael Tarpley, Weifan Zheng. Accelerating drug discovery at an HBCU with AI/ML: Text mining, computational modeling, and drug repurposing approaches [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A018.

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind.Graphic abstractThe primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

The pharmaceutical industry has seen a lot of transformation in the last five years because of technological innovations such as AI. AI-based technologies such as ML and DL are revolutionizing the sector and making processes such as drug discovery, research, dose optimization, therapeutic drug monitoring, drug repurposing, predictive analytics, and clinical trials much easier. Drug development is a complex, time consuming, and labor-intensive process. In some instances, drug development takes up to 10 years and a significant amount of investment. However, AI-based technologies are showing a lot of promise when it comes to simplifying the process and making it less-time consuming. The drug development involves a lot of data. AI-based technologies such as ML shows a lot of promise when it comes to analyzing and managing these large volumes of data making the process more manageable. AI has also simplified the process of identifying therapeutic targets. AI is also being used in drug design to help in making predictions of 3D structure of the target protein and predict drug-protein interactions. Other areas where AI is being used in drug discovery are de novo drug design, optimizing clinical trials, predictive modelling, and precision medicine. Despite the advantages that AI offers in pharma, it has its limitations. For instance, ethical considerations regarding patient data, privacy, and confidentiality remains a key issue. Risk of bias also raises ethical concerns that should be considered. Other limitations are limited skills that make it difficult to optimize AI, financial limitations that make it difficult to invest in AI, and data governance challenges. Keywords: Artificial intelligence (AI), machine learning (ML), deep learning (DL), drug discovery, clinical trials

Abstract: The integration of Artificial Intelligence (AI) and Big Data is revolutionizing healthcare by enabling predictive analytics and precision medicine, shifting the focus from reactive treatments to proactive, data-driven healthcare solutions. AI-powered machine learning models analyze vast datasets, including electronic health records (EHRs), genomic sequences, medical imaging, and real-time patient monitoring, to predict disease risks, personalize treatments, and enhance diagnostic accuracy. Precision medicine leverages AI to tailor therapies based on genetic profiles, lifestyle factors, and environmental influences, improving patient outcomes while minimizing adverse effects. Additionally, AI-driven drug discovery, robotic-assisted surgeries, and telemedicine innovations are accelerating medical advancements, reducing costs, and improving healthcare accessibility. Despite challenges related to data privacy, algorithmic bias, and regulatory compliance, AI continues to drive groundbreaking innovations in early disease detection, optimized treatment planning, and healthcare automation. As AI and Big Data evolve, the future of healthcare will be defined by intelligent, patient-centric, and highly efficient medical ecosystems, transforming the way healthcare is delivered worldwide. Keywords: AI in healthcare, Big Data analytics, predictive analytics, precision medicine, AI-driven diagnostics, medical imaging, machine learning in healthcare, genomics, pharmacogenomics, drug discovery, personalized medicine, healthcare automation, AI in telemedicine, robotic-assisted surgery, patient-centered AI.

Introduction: The use of artificial intelligence (AI) in drug discovery and the pharma industry has been rapidly expanding in recent years. AI algorithms can analyze vast amounts of data, identify patterns, and make predictions that can accelerate drug discovery and improve patient outcomes.
 Methods: AI is being used in various stages of the drug discovery process, from target identification and lead optimization to clinical trials and post-market surveillance. Machine learning algorithms, neural networks, and natural language processing are among the AI techniques used in drug discovery.
 Results: AI-based drug discovery has already shown promising results, with several drugs in clinical trials or approved for use that were discovered using AI. AI is also being used to improve clinical trial design and patient selection, as well as to monitor adverse drug events and optimize drug dosing.
 Conclusion: AI has the potential to transform the drug discovery and pharma industry, making drug development faster, more efficient, and more effective. However, there are still challenges that need to be addressed, such as the need for high-quality data and the potential for bias in AI algorithms. Overall, the use of AI in drug discovery and the pharma industry is an exciting and rapidly evolving field that has the potential to improve patient outcomes and revolutionize healthcare.