Novel molecular design via a scaffold-aware transformer with multi-scale attention mechanisms.

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 pressing need for sustainable materials and devices stems from growing environmental concerns and the imperative to mitigate climate change. Traditional materials and devices often rely on non-renewable resources and generate significant waste and pollution throughout their lifecycle. By prioritizing sustainability in material and device design, we can foster innovation, promote circular economies, and build a greener future for generations to come. Artificial intelligence (AI) and machine learning (ML) can analyze vast datasets to identify novel materials with desirable properties by reducing the experimental workload. In this paper, we explore the synergistic relationship between sustainable materials discovery and ML models. By leveraging advanced algorithms, researchers can efficiently explore vast chemical spaces to identify environmentally friendly materials with tailored properties. ML techniques, including predictive modeling and generative models, facilitate the rapid discovery and optimization of sustainable materials for various applications, ranging from renewable energy technologies to eco-friendly consumer products. We present a landscape view of the field with a focus on the most recent developments, focusing mainly on transitory materials such as metals, polymers, and semiconducting materials. Furthermore, classification and regression techniques to model the degradation behavior of polymers have been addressed, pointing to key challenges and proposing solutions for enhanced ML applications. The paper discusses the challenges of scaling up data-driven technologies from small molecules to polymers, underscoring AI’s role in discovering new molecular designs and optimizing existing ones for novel applications. It emphasizes the importance of defining and standardizing polymer systems to enable ML models to create a unified data collection system for AI and automation enhancements. Furthermore, it stresses the necessity of refining ML methods to harness the benefits of data-driven polymer chemistry fully, emphasizing the importance of reliable and diverse datasets for predictive models in polymer synthesis.

Pharmaceutical research essentially depends on drug-target interactions (DTIs). Standard methods of experimentation to uncover DTIs are costly and time-consuming, and thus artificial intelligence and machine learning have become popular. Multimodal imaging also provides significant amounts of anatomical, functional, and molecular information, accelerating drug discovery and development. Imaging technologies help understand disease mechanisms find new pharmacological targets and evaluate new drug candidates and how well they work. In this research, we developed a model based on deep learning (DL) that employs sequence information for targets and medicines to ascertain binding affinities of DTIs named feed-forward neural network (FNN)-DT binding affinity. Existing studies for the prediction of binding affinity of DTs either employ three-dimensional structures of protein–ligand complexes or two-dimensional characteristics of compounds. A novel technique used in this research: a dense network with dropouts were used to show the protein and drug sequences. These findings support the proposed DL-based approach for predicting binding affinity in DTIs, which use 1D representations of targets and medicines. In one of the standard datasets, the proposed FNNs outperformed the Kronecker regularized least squares, gradient boosting machines, deep drug target affinity algorithm, wide drug-target affinity, and similarity-based convolutional neural network model techniques with a 0.89 concordance index and 0.235 mean square error.

Molecular design is a critical aspect of various scientific and industrial fields, where the properties of molecules hold significant importance. In this study, a 3-fold methodology design is presented that leverages the power of generative artificial intelligence (AI), predictive modeling, and reinforcement learning to create tailored molecules with desired properties. This model synergistically combines deep learning techniques with Self-Referencing Embedded Strings (SELFIES) molecular representation to build a generative model that generates valid molecules and a graphical neural network model that accurately forecasts molecular properties. The Variational Autoencoder (VAE) coupled with reinforcement learning helps refine molecule generation based on targeted attributes. Data from an experimental study involving surfactants were used to test the framework. A validation of the structural integrity of the molecules generated was conducted, and Tanimoto similarities were used to quantify the similarity and diversity between the original and generated molecular structures. Also, saliency maps for the generated surfactants were produced to identify the features explaining the property values. Lastly, molecular dynamics simulations were used to validate the stability of the generated molecules. The results showed that the proposed framework can effectively produce valid molecules within the set property threshold value.

Computational chemistry has become a central component of modern drug discovery by enabling rational design strategies that complement traditional experimental approaches. Computer‐aided drug discovery (CADD) integrates molecular modelling techniques such as structure‐based drug design (SBDD), ligand‐based modelling, molecular docking, molecular dynamics simulations, and free energy calculations to accelerate the identification and optimization of therapeutic candidates. In recent years, advances in artificial intelligence (AI) and machine learning (ML) have further expanded the capabilities of computational drug discovery by enabling accurate prediction of molecular properties and exploration of vast chemical spaces. Modern AI‐driven approaches, including graph neural networks and generative models, are increasingly combined with classical physics‐based simulations to improve predictive accuracy and efficiency in drug development workflows. This review provides a state‐of‐the‐art overview of contemporary computational methods used in drug discovery, highlighting both classical CADD techniques and emerging AI‐based strategies. Particular emphasis is placed on the application of these approaches in the discovery of natural product‐derived anticancer agents. Current challenges related to data quality, model interpretability, and computational limitations are also discussed. Overall, the integration of computational chemistry and AI is expected to play an increasingly important role in accelerating drug discovery and advancing precision medicine.

The clinical process to produce innovative medications generally spans over 10 years and billions of dollars from several businesses. The strength of computational chemistry and molecular modeling has improved nevertheless the conventional medical approaches deal with several important challenges due to their high failure numbers and their inability to find good medication candidates. The pharmaceutical business benefits from AI innovation through Generative AI since it offers a huge breakthrough in research capacity. Generative AI uses deep learning architectures VAEs GANs and Transformer-based systems to enable expedited drug discovery through quick chemical design and optimal drug characteristics alongside accurate biological interactions predictions. This research analyzes Generative AI's major influence on pharmaceutical science as it quickens the processes of identifying new medications. The application of AI models helps researchers to build new chemical structures while forecasting their medication performance behaviors and deliver more strong lead compounds at a better speed than old methodologies. Generative AI leads to the production of improved medication candidates with superior efficiency and decreased toxicity according to studies from Insilico Medicine and BenevolentAI and DeepMind’s AlphaFold. AI simulations offer automated drug screening combined with cost-efficient experimental approaches which boost the success rate of clinical trials. The clinical process to produce innovative medications generally spans over 10 years and billions of dollars from several businesses. The strength of computational chemistry and molecular modeling has improved nevertheless the conventional medical approaches deal with several important challenges due to their high failure numbers and their inability to find good medication candidates. Artificial Intelligence (AI) created new study opportunities in the pharmaceutical field while Generative AI stands as a revolutionary concept change. The deep learning algorithms known as Variational Autoencoders (VAEs) Generative Adversarial Networks (GANs) along with Transformer-based models in Generative AI showcase impressive capabilities to expedite drug discovery by developing molecules swiftly and improving drug characteristics as well as accurately forecasting biological drug interactions. The material offered in this study examines Generative AI's use in pharmaceutical innovation with an emphasis on its capacity to speed up drug discovery processes. The application of AI-driven models enables scientists to generate new chemical structures which following prediction of pharmacokinetic and pharmacodynamic features results in improved optimized lead compounds development rates beyond traditional methods. Generative AI leads to the production of improved medication candidates with superior efficiency and decreased toxicity according to studies from Insilico Medicine and BenevolentAI and DeepMind’s AlphaFold. AI simulations offer automated drug screening combined with cost-efficient experimental approaches which boost the success rate of clinical trials.

In the pharmaceutical industry, artificial intelligence (AI) is a game-changer, transforming clinical decision-making, patient care, and drug development. Using generative models, deep learning, and machine learning, artificial intelligence (AI) improves pharmaceutical operations’ accuracy, efficiency, and cost-effectiveness. AI’s various applications in pharmacy are examined in this paper, which also describes its classifications by capabilities (Artificial Narrow Intelligence, General Intelligence, and Super Intelligence), functionalities (reactive machines, limited memory systems), and learning techniques (machine learning, deep learning). Important fields include drug research, where AI speeds up molecular design, virtual screening, and target identification, slashing development times from 14 years to months and drastically lowering costs. In clinical practice, AI helps with medication interaction monitoring, individualized treatment regimens through EHR data analysis, and illness diagnosis (e.g., Alzheimer’s prediction by MRI analysis, cancer detection in dermatology). AI also makes remote patient monitoring and telemedicine more efficient, allowing for early intervention and real-time health tracking. AI-powered technologies (like AlphaFold2 and DeepChem) and databases (like PubChem and DrugBank) help the pharmaceutical business by streamlining post-market surveillance, clinical trial optimization, and medication creation. AI is also used in specialist sectors including infectious diseases, cardiology, and cancer to enhance treatment results and diagnosis accuracy. Implementation issues are examined together with ethical issues and future possibilities, such as the potential of artificial general intelligence (AGI). Accelerated innovation, better patient outcomes, and sustainable healthcare solutions are achieved by the industry by incorporating AI into the whole drug lifecycle, from preclinical research to commercialization. This thorough analysis emphasizes how important AI is to bringing pharmacy into the data-driven, patient-centered era.

Artificial intelligence (AI) refers to the replication of human cognitive abilities in machines, allowing them to analyze data, learn from patterns, and make autonomous decisions. In oncology, AI is transforming cancer research by improving early detection methods and accelerating drug discovery. Machine learning (ML), a subset of AI, enables computational models to process vast medical datasets, enhancing diagnostic accuracy and supporting personalized treatment strategies. This paper explores AI-driven methodologies in cancer detection, including imaging analysis, biomarker identification, and liquid biopsy. AI-powered imaging enhances scan interpretation for early-stage diagnosis, while biomarker analysis facilitates malignancy detection. Liquid biopsy utilizes AI to identify cancer-related genetic material in blood samples, enabling early diagnosis and continuous monitoring. Additionally, AI-driven drug discovery expedites target identification, molecular design, and drug repurposing, thereby reducing the time and costs associated with pharmaceutical development. Predictive models optimize drug formulations and assess existing medications for potential cancer treatment applications. This study examines the clinical integration of AI in oncology, emphasizing its role in refining diagnostic and therapeutic approaches while improving patient outcomes.

Rare and intractable diseases affect an estimated 3.5% to 5.9% of the global population but remain largely underserved in terms of diagnosis and treatment, with effective therapies available for only about 5% of conditions. This paper presents an overview of recent advances in artificial intelligence (AI) applications targeting these challenges. In diagnostic support, AI has been utilized to analyze genomic data and facial images, enhancing the accuracy and efficiency of identifying rare genetic syndromes. In therapeutic development, AI-driven analysis of biomedical knowledge graphs has enabled the prediction of potential treatment candidates for diseases lacking existing therapies. Additionally, generative models have accelerated drug discovery by identifying novel targets and designing candidate compounds, some of which have progressed to clinical evaluation. AI has also facilitated clinical trial support by automating patient eligibility screening using electronic health records, improving recruitment efficiency for trials that often struggle with small, geographically dispersed patient populations. Despite these advancements, challenges remain in ensuring data quality, interpretability of AI outputs, and the standardization of infrastructure across institutions. Moving forward, international data-sharing platforms integrating diverse modalities - clinical, genomic and image - are expected to play a pivotal role in enabling reliable, scalable, and ethically responsible AI applications. These developments hold the potential to transform the landscape of rare disease diagnosis, treatment, and research.

The convergence of big data and biotechnology is transforming the landscape of healthcare, pharmaceutical research, and fungal biology. This review explores the emerging synergy across these domains, emphasizing predictive analytics, artificial intelligence (AI), and machine learning (ML) enabling real-time decision-making, accelerating drug discovery, and advancing ecological and mycological research. In healthcare, big data collected from electronic health records (EHRs), wearable devices, and population-level datasets support early disease detection, risk stratification, and personalized treatment plans. In pharmaceuticals, AI models including deep learning and generative framework streamline drug development by facilitating target identification, virtual screening, and predictive ADMET modeling. These innovations have significantly reduced development timelines and improved precision in therapeutic design. Parallel advancements in fungal biotechnology, driven by image-based classification and genomic analysis, are revealing fungi as critical sources of bioactive compounds, enzymes, and ecological indicators. Predictive models are now capable of identifying fungal species, mapping metabolic pathways, and forecasting ecological patterns, thus positioning fungi at the intersection of environmental monitoring and drug discovery. Despite these advances, challenges persist including data interoperability, algorithmic bias, regulatory barriers, and ethical concerns related to privacy, equity, and bioprospecting. This review also discusses the infrastructure needed to support cross-sector innovation, such as cloud computing, graph neural networks, FAIR data standards, and open science platforms. It outlines strategic priorities for building integrated, explainable, and accessible AI systems, particularly in underserved regions. By highlighting case studies, shared challenges, and future directions, the review underscores the importance of interdisciplinary collaboration in leveraging big data–biotech synergy.

The convergence of big data and biotechnology is transforming the landscape of healthcare, pharmaceutical research, and fungal biology. This review explores the emerging synergy across these domains, emphasizing predictive analytics, artificial intelligence (AI), and machine learning (ML) enabling real-time decision-making, accelerating drug discovery, and advancing ecological and mycological research. In healthcare, big data collected from electronic health records (EHRs), wearable devices, and population-level datasets support early disease detection, risk stratification, and personalized treatment plans. In pharmaceuticals, AI models including deep learning and generative framework streamline drug development by facilitating target identification, virtual screening, and predictive ADMET modeling. These innovations have significantly reduced development timelines and improved precision in therapeutic design. Parallel advancements in fungal biotechnology, driven by image-based classification and genomic analysis, are revealing fungi as critical sources of bioactive compounds, enzymes, and ecological indicators. Predictive models are now capable of identifying fungal species, mapping metabolic pathways, and forecasting ecological patterns, thus positioning fungi at the intersection of environmental monitoring and drug discovery. Despite these advances, challenges persist including data interoperability, algorithmic bias, regulatory barriers, and ethical concerns related to privacy, equity, and bioprospecting. This review also discusses the infrastructure needed to support cross-sector innovation, such as cloud computing, graph neural networks, FAIR data standards, and open science platforms. It outlines strategic priorities for building integrated, explainable, and accessible AI systems, particularly in underserved regions. By highlighting case studies, shared challenges, and future directions, the review underscores the importance of interdisciplinary collaboration in leveraging big data–biotech synergy.

Traditional drug discovery methods such as wet-lab testing, validations, and synthetic techniques are time-consuming and expensive. Artificial Intelligence (AI) approaches have progressed to the point where they can have a significant impact on the drug discovery process. Using massive volumes of open data, artificial intelligence methods are revolutionizing the pharmaceutical industry. In the last few decades, many AI-based models have been developed and implemented in many areas of the drug development process. These models have been used as a supplement to conventional research to uncover superior pharmaceuticals expeditiously. AI's involvement in the pharmaceutical industry was used mostly for reverse engineering of existing patents and the invention of new synthesis pathways. Drug research and development to repurposing and productivity benefits in the pharmaceutical business through clinical trials. AI is studied in this article for its numerous potential uses. We have discussed how AI can be put to use in the pharmaceutical sector, specifically for predicting a drug's toxicity, bioactivity, and physicochemical characteristics, among other things. In this review article, we have discussed its application to a variety of problems, including de novo drug discovery, target structure prediction, interaction prediction, and binding affinity prediction. AI for predicting drug interactions and nanomedicines were also considered.

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.

Background: Artificial Intelligence (AI) is transforming scientific research by enhancing data analysis, predictive modeling and automation across various disciplines. Its integration into fields such as healthcare, drug discovery, chemistry, physics, and environmental sciences has significantly improved efficiency and accuracy. Aim: This review aims to explore AI’s applications in scientific research, highlighting its contributions to diagnostics, personalized medicine, material discovery, and environmental modeling while addressing existing challenges and future prospects. Methods: A systematic review of AI applications in scientific research was conducted, focusing on studies utilizing machine learning (ML) and deep learning (DL) to improve predictive accuracy, optimize complex systems, and enhance decision-making. Key challenges such as algorithmic bias, data privacy, and ethical considerations were also analzed. Results: AI-driven innovations have led to breakthroughs in scientific research, including enhanced disease diagnostics, accelerated drug discovery, improved material optimization, and accurate environmental predictions. AI has also facilitated the development of personalized medicine by analyzing vast datasets with high precision. However, challenges related to data integrity, transparency, and ethical concerns remain significant barriers to widespread adoption. Conclusion: AI continues to revolutionize scientific research, yet overcoming challenges such as ethical concerns, data security, and algorithm interpretability is crucial for its full potential to be realized. Future research should focus on developing explainable AI models, fostering interdisciplinary collaboration, and establishing ethical frameworks to ensure responsible AI implementation.

On Chatbots and Generative Artificial Intelligence.