AI in Pharmacy: Revolutionizing Drug Discovery, Patient Care, and Beyond

The article is devoted to the history of the development of Information and Communication Technologies (ICT) and Artificial Intelligence (AI), their current and probable future achievements, and the problems (which have already arisen, but will become even more acute in the future) associated with the development of these technologies and their active introduction in society. The close connection between the development of AI and cognitive science, the penetration of ICT and AI into various fields, in particular the field of health care, is shown. A significant part of the article is devoted to the analysis of the concept of ‘artificial intelligence’, including the definition of generative AI. We analyze recent achievements in the field of Artificial Intelligence, describe the basic models, in particular the Large Linguistic Models (LLM), and forecast the development of AI and the dangers that await us in the coming decades. We identify the forces behind the aspiration to create artificial intelligence, which is increasingly approaching the capabilities of the so-called general/universal AI, and also suggest desirable measures to limit and channel the development of artificial intelligence. The authors emphasize that the threats and dangers of the development of ICT and AI are particularly aggravated by the monopolization of their development by the state, intelligence services, large corporations and those often referred to as globalists. The article forecasts the development of computers, ICT and AI in the coming decades, and also shows the changes in society that will be associated with them. The study consists of two articles. The first, presented below, provides a brief historical overview and characterizes the current situation in the field of ICT and AI, it also analyzes the concepts of artificial intelligence, including generative AI, changes in the understanding of AI related to the emergence of the so-called large language models and related new types of AI programs (ChatGPT). The article discusses the serious problems and dangers associated with the rapid and uncontrolled development of artificial intelligence. The second article, to be published in the next issue of the journal, describes and comments on current assessments of breakthroughs in the field of AI, analyzes various forecasts, and the authors give their own assessments and forecasts of future developments. Particular attention is given to the problems and dangers associated with the rapid and uncontrolled development of AI, the fact that achievements in the field of AI are becoming a powerful means of controlling the population, imposing ideology and choice, influencing the results of elections, and a weapon for undermining security and geopolitical struggle.

Historically, the term "artificial intelligence" dates to 1956 when it was first used in a conference at Dartmouth College in the US. Since then, the development of artificial intelligence has in part been shaped by the field of neuroscience. By understanding the human brain, scientists have attempted to build new intelligent machines capable of performing complex tasks akin to humans. Indeed, future research into artificial intelligence will continue to benefit from the study of the human brain. While the development of artificial intelligence algorithms has been fast paced, the actual use of most artificial intelligence (AI) algorithms in biomedical engineering and clinical practice is still markedly below its conceivably broader potentials. This is partly because for any algorithm to be incorporated into existing workflows it has to stand the test of scientific validation, clinical and personal utility, application context, and is equitable as well. In this context, there is much to be gained by combining AI and human intelligence (HI). Harnessing Big Data, computing power and storage capacities, and addressing societal issues emergent from algorithm applications, demand deploying HI in tandem with AI. Very few countries, even economically developed states, lack adequate and critical governance frames to best understand and steer the AI innovation trajectories in health care. Drug discovery and translational pharmaceutical research stand to gain from AI technology provided they are also informed by HI. In this expert review, we analyze the ways in which AI applications are likely to traverse the continuum of life from birth to death, and encompassing not only humans but also all animal, plant, and other living organisms that are increasingly touched by AI. Examples of AI applications include digital health, diagnosis of diseases in newborns, remote monitoring of health by smart devices, real-time Big Data analytics for prompt diagnosis of heart attacks, and facial analysis software with consequences on civil liberties. While we underscore the need for integration of AI and HI, we note that AI technology does not have to replace medical specialists or scientists and rather, is in need of such expert HI. Altogether, AI and HI offer synergy for responsible innovation and veritable prospects for improving health care from prevention to diagnosis to therapeutics while unintended consequences of automation emergent from AI and algorithms should be borne in mind on scientific cultures, work force, and society at large.

Background: The pharmaceutical industry is undergoing rapid digital transformation, generating vast and complex datasets that challenge traditional drug discovery workflows. Artificial intelligence (AI) has emerged as a powerful solution capable of processing large-scale clinical, biological, and chemical information with high precision. Its ability to learn from data, uncover hidden patterns, and automate complex tasks positions AI as a transformative force in modern drug development. Objective: This editorial, through an AI lens in drug discovery, demonstrates the significance of AI applications in target identification, hit generation, lead optimization, predictive toxicology, ADMET profiling, and clinical trial design, and the issues and ethical considerations. The difficulties were acknowledged. Results: AI showed massive power in foreseeing drug–target interactions, virtually testing millions of compounds, and creating new chemical structures with better pharmacological profiles. Deep learning techniques were far superior to conventional machine learning methods when predicting ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties. The use of AI to perform virtual screening and generate modeling rapidly led to the identification of new drug candidates, while clinical trial design was improved through data-driven algorithms enabling enhanced patient stratification and adaptive protocol utilization. Moreover, AI techniques allowed for the earliest possible toxicity prediction, thus lowering last-stage failures and overall development costs. Conclusion: AI is a significant paradigm shift in drug discovery, which means therapeutics will be developed in a shorter period, at a lower cost, and with higher accuracy. However, the maximum benefit of AI can be achieved only if the technical, ethical, and regulatory challenges are solved through collaborative, transparent, and safe usage of AI-driven innovation facilitation frameworks.

Introduction Author Arthur C. Clarke famously argued that in science fiction literature “any sufficiently advanced technology is indistinguishable from magic” (Clarke). On 30 November 2022, technology company OpenAI publicly released their Large Language Model (LLM)-based chatbot ChatGPT (Chat Generative Pre-Trained Transformer), and instantly it was hailed as world-changing. Initial media stories about ChatGPT highlighted the speed with which it generated new material as evidence that this tool might be both genuinely creative and actually intelligent, in both exciting and disturbing ways. Indeed, ChatGPT is part of a larger pool of Generative Artificial Intelligence (AI) tools that can very quickly generate seemingly novel outputs in a variety of media formats based on text prompts written by users. Yet, claims that AI has become sentient, or has even reached a recognisable level of general intelligence, remain in the realm of science fiction, for now at least (Leaver). That has not stopped technology companies, scientists, and others from suggesting that super-smart AI is just around the corner. Exemplifying this, the same people creating generative AI are also vocal signatories of public letters that ostensibly call for a temporary halt in AI development, but these letters are simultaneously feeding the myth that these tools are so powerful that they are the early form of imminent super-intelligent machines. For many people, the combination of AI technologies and media hype means generative AIs are basically magical insomuch as their workings seem impenetrable, and their existence could ostensibly change the world. This article explores how the hype around ChatGPT and generative AI was deployed across the first six months of 2023, and how these technologies were positioned as either utopian or dystopian, always seemingly magical, but never banal. We look at some initial responses to generative AI, ranging from schools in Australia to picket lines in Hollywood. We offer a critique of the utopian/dystopian binary positioning of generative AI, aligning with critics who rightly argue that focussing on these extremes displaces the more grounded and immediate challenges generative AI bring that need urgent answers. Finally, we loop back to the role of schools and educators in repositioning generative AI as something to be tested, examined, scrutinised, and played with both to ground understandings of generative AI, while also preparing today’s students for a future where these tools will be part of their work and cultural landscapes. Hype, Schools, and Hollywood In December 2022, one month after OpenAI launched ChatGPT, Elon Musk tweeted: “ChatGPT is scary good. We are not far from dangerously strong AI”. Musk’s post was retweeted 9400 times, liked 73 thousand times, and presumably seen by most of his 150 million Twitter followers. This type of engagement typified the early hype and language that surrounded the launch of ChatGPT, with reports that “crypto” had been replaced by generative AI as the “hot tech topic” and hopes that it would be “‘transformative’ for business” (Browne). By March 2023, global economic analysts at Goldman Sachs had released a report on the potentially transformative effects of generative AI, saying that it marked the “brink of a rapid acceleration in task automation that will drive labor cost savings and raise productivity” (Hatzius et al.). Further, they concluded that “its ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects” (Hatzius et al.). Speculation about the potentially transformative power and reach of generative AI technology was reinforced by warnings that it could also lead to “significant disruption” of the labour market, and the potential automation of up to 300 million jobs, with associated job losses for humans (Hatzius et al.). In addition, there was widespread buzz that ChatGPT’s “rationalization process may evidence human-like cognition” (Browne), claims that were supported by the emergent language of ChatGPT. The technology was explained as being “trained” on a “corpus” of datasets, using a “neural network” capable of producing “natural language“” (Dsouza), positioning the technology as human-like, and more than ‘artificial’ intelligence. Incorrect responses or errors produced by the tech were termed “hallucinations”, akin to magical thinking, which OpenAI founder Sam Altman insisted wasn’t a word that he associated with sentience (Intelligencer staff). Indeed, Altman asserts that he rejects moves to “anthropomorphize” (Intelligencer staff) the technology; however, arguably the language, hype, and Altman’s well-publicised misgivings about ChatGPT have had the combined effect of shaping our understanding of this generative AI as alive, vast, fast-moving, and potentially lethal to humanity. Unsurprisingly, the hype around the transformative effects of ChatGPT and its ability to generate ‘human-like’ answers and sophisticated essay-style responses was matched by a concomitant panic throughout educational institutions. The beginning of the 2023 Australian school year was marked by schools and state education ministers meeting to discuss the emerging problem of ChatGPT in the education system (Hiatt). Every state in Australia, bar South Australia, banned the use of the technology in public schools, with a “national expert task force” formed to “guide” schools on how to navigate ChatGPT in the classroom (Hiatt). Globally, schools banned the technology amid fears that students could use it to generate convincing essay responses whose plagiarism would be undetectable with current software (Clarence-Smith). Some schools banned the technology citing concerns that it would have a “negative impact on student learning”, while others cited its “lack of reliable safeguards preventing these tools exposing students to potentially explicit and harmful content” (Cassidy). ChatGPT investor Musk famously tweeted, “It’s a new world. Goodbye homework!”, further fuelling the growing alarm about the freely available technology that could “churn out convincing essays which can't be detected by their existing anti-plagiarism software” (Clarence-Smith). Universities were reported to be moving towards more “in-person supervision and increased paper assessments” (SBS), rather than essay-style assessments, in a bid to out-manoeuvre ChatGPT’s plagiarism potential. Seven months on, concerns about the technology seem to have been dialled back, with educators more curious about the ways the technology can be integrated into the classroom to good effect (Liu et al.); however, the full implications and impacts of the generative AI are still emerging. In May 2023, the Writer’s Guild of America (WGA), the union representing screenwriters across the US creative industries, went on strike, and one of their core issues were “regulations on the use of artificial intelligence in writing” (Porter). Early in the negotiations, Chris Keyser, co-chair of the WGA’s negotiating committee, lamented that “no one knows exactly what AI’s going to be, but the fact that the companies won’t talk about it is the best indication we’ve had that we have a reason to fear it” (Grobar). At the same time, the Screen Actors’ Guild (SAG) warned that members were being asked to agree to contracts that stipulated that an actor’s voice could be re-used in future scenarios without that actor’s additional consent, potentially reducing actors to a dataset to be animated by generative AI technologies (Scheiber and Koblin). In a statement issued by SAG, they made their position clear that the creation or (re)animation of any digital likeness of any part of an actor must be recognised as labour and properly paid, also warning that any attempt to legislate around these rights should be strongly resisted (Screen Actors Guild). Unlike the more sensationalised hype, the WGA and SAG responses to generative AI are grounded in labour relations. These unions quite rightly fear the immediate future where human labour could be augmented, reclassified, and exploited by, and in the name of, algorithmic systems. Screenwriters, for example, might be hired at much lower pay rates to edit scripts first generated by ChatGPT, even if those editors would really be doing most of the creative work to turn something clichéd and predictable into something more appealing. Rather than a dystopian world where machines do all the work, the WGA and SAG protests railed against a world where workers would be paid less because executives could pretend generative AI was doing most of the work (Bender). The Open Letter and Promotion of AI Panic In an open letter that received enormous press and media uptake, many of the leading figures in AI called for a pause in AI development since “advanced AI could represent a profound change in the history of life on Earth”; they warned early 2023 had already seen “an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control” (Future of Life Institute). Further, the open letter signatories called on “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4”, arguing that “labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts” (Future of Life Institute). Notably, many of the signatories work for the very companies involved in the “out-of-control race”. Indeed, while this letter could be read as a moment of ethical clarity for the AI industry, a more cynical reading might just be that in warning that their AIs could effectively destroy the w

Abstract: The traditional drug discovery process is notoriously time-consuming, expensive, and damaged, with high failure rates. However, the recent surge in Artificial Intelligence (AI) has presented itself as a game-changer in this field. This comprehensive review delves into the profound impact of AI on various aspects of drug discovery, encompassing crucial stages like target identification, molecular analysis, compound screening, and even drug development. Machine learning algorithms are pivotal in analyzing vast datasets to predict important aspects of potential drug candidates. These predictions include their pharmacokinetic properties (how the body absorbs and eliminates them) and possible toxicity, effectively streamlining the process and reducing risks associated with further development. Additionally, AI facilitates the exploration of vast chemical spaces, enabling the design and synthesis of novel drug candidates with enhanced efficacy and specificity. The review highlights AI's transformative potential in drug discovery and acknowledges the existing challenges. Concerns surrounding data quality, interpretability of AI models, and ethical considerations are addressed, paving the way for responsible development and integration of AI within the pharmaceutical industry. Ultimately, this review underscores AI's massive potential in revolutionizing drug discovery, offering a path towards faster development of life-saving treatments and fostering healthcare advancements.

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.

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 is defined as the totality of systems and programs that imitate human intelligence and can eventually surpass this intelligence over time. The rapid development of these technologies has raised various ethical debates such as moral responsibility, privacy, bias, respect for human rights, and social impacts. This study examines the technical infrastructure of artificial intelligence, the differences between weak and strong artificial intelligence, ethical issues, and theological dimensions in detail, providing a comprehensive perspective on the role of artificial intelligence in human life and the problems it brings. The historical development of artificial intelligence has been shaped by the contributions of various disciplines such as mathematical logic, cognitive science, philosophy, and engineering. From the ancient Greek philosophers to the present day, thoughts on artificial intelligence have raised deep philosophical questions such as human nature, consciousness, and responsibility. The algorithms developed by Alan Turing have contributed to the modern shaping of artificial intelligence and have put forward the first models to assess whether machines have human-like intelligence, such as the “Turing Test”. The study first analyzes the technical infrastructure of artificial intelligence in detail and discusses the current limits and potential of the technology through the distinction between weak and strong artificial intelligence. Weak artificial intelligence includes systems designed to perform specific tasks and do not exhibit general intelligence outside of those tasks, while strong artificial intelligence refers to systems with human-like general intelligence and flexible thinking capacity. Most of the widely used artificial intelligence applications today fall into the category of weak artificial intelligence. However, the development of strong artificial intelligence brings various ethical and theological consequences for humanity. The ethical issues of artificial intelligence include fundamental topics such as autonomy, responsibility, transparency, fairness, and privacy. The decision-making processes of autonomous systems raise serious ethical questions at the societal level. Especially autonomous weapons and artificial intelligence-managed justice systems raise concerns in terms of human rights and individual freedoms. In this context, the ethical framework of artificial intelligence has deep impacts on the future of humanity and human-machine interaction, not just limited to technological boundaries. From a theological perspective, the ability of artificial intelligence to imitate the human mind and creative processes raises deep theological issues such as the creativity of God, the place of human beings in the universe, and consciousness. The questions of whether artificial intelligence systems can gain consciousness and whether these conscious systems can have a spiritual status have led to new debates in theology and philosophy. The ethical principles of artificial intelligence are shaped around principles such as transparency, accountability, autonomy, human control, and data management. In conclusion, determining the ethical and theological principles that need to be considered in the development and application of artificial intelligence is critical for the future of humanity. A comprehensive examination of the ethical and theological dimensions of artificial intelligence technologies is necessary to understand and manage the social impacts of this technology. This study emphasizes the necessity of an interdisciplinary approach for the development of artificial intelligence in harmony with social values and for the benefit of humanity. The study provides an important theoretical framework for future research by shedding light on the complex ethical and theological issues arising from the development and widespread use of artificial intelligence.

The whirlwind advent of ChatGPT in 2022 has marked a new age of artificial intelligence (AI), the general name for the technology that combines computer technology, big data bases and machines. This AI technology quickly makes its presence felt with hundreds of popular programs and chatbots such as the portrait-making AI diffusion art and the thesis-writing ChatGPT. This paper investigates the conceptual metaphors representing AI and AI development in The Guardian, a UK-based newspaper, to figure out how this technology and its growth have been introduced to ordinary people via mass media. Employing the Conceptual Metaphor Theory proposed by Lakoff & Johnson (1980), this study found three AI-related conceptual metaphors, namely, AI IS A HUMAN BEING, AI IS AN ANIMAL and AI IS A NATURAL FORCE, which are realized by more than 100 linguistic expressions across 33 news articles. Also, this research found five conceptual metaphors related to AI development, namely AI DEVELOPMENT IS WAR, AI DEVELOPMENT IS A RACE, AI DEVELOPMENT IS A CONVERSATION, AI DEVELOPMENT IS A DANCE, AI DEVELOPMENT IS A GAME and these metaphors are manifested by approximately 40 linguistic expressions. This paper discusses the way that these metaphors could influence the way people and technology companies think about AI and AI development.

Purpose This study analyzes China’s strategic initiatives in metaverse and artificial intelligence (AI) development, examining their impact on academic research, industry innovation, and policy formulation. It aims to understand how government policies and investments have shaped research agendas and to identify challenges and opportunities in these fields. Design/methodology/approach The research employs a comprehensive analysis of government documents, funding schemes, and research output. It examines key policies, investment programs, and academic publications to track trends in metaverse and AI development in China. The study utilizes bibliometric analysis to assess publication trends, citation patterns, and international collaboration networks. Findings China’s proactive approach, characterized by strong government support and significant private sector investment, has led to a substantial increase in research output and quality in metaverse and AI fields. Chinese institutions have become major contributors to global publications, with growing citation rates and presence at international conferences. The research identifies emerging challenges in privacy, ethical AI development, and digital divide concerns. Practical implications The findings provide insights for policymakers, researchers, and industry stakeholders on the development trajectory of metaverse and AI technologies in China. They highlight the need for balanced approaches to innovation, regulation, and ethical considerations in these rapidly evolving fields. Social implications The study underscores the potential of metaverse and AI technologies to transform various sectors of society, from education and healthcare to entertainment and social interactions. It emphasizes the importance of addressing digital equity and ethical AI deployment to ensure broad societal benefits. Originality/value This research offers a comprehensive overview of China’s approach to metaverse and AI development, providing a unique perspective on the interplay between government initiatives, academic research, and industry innovation. It contributes to the broader discussion on the global development of these transformative technologies and their implications for future technological landscapes.

Artificial intelligence (AI) has rapidly become one of the technologies used for competitive advantage. However, there are also growing concerns about bias in AI models as AI developers risk introducing bias both unintentionally and intentionally. This study, using a qualitative approach, investigated how AI developers can contribute to the development of fair AI models. The key findings reveal that the risk of bias is mainly because of the lack of gender and social diversity in AI development teams, and haste from AI managers to deliver much-anticipated results. The integrity of AI developers is also critical as they may conceal bias from management and other AI stakeholders. The testing phase before model deployment risks bias because it is rarely representative of the diverse societal groups that may be affected. The study makes practical recommendations in four main areas: governance, social, technical, and training and development processes. Responsible organisations need to take deliberate actions to ensure that their AI developers adhere to fair processes when developing AI; AI developers must prioritise ethical considerations and consider the impact their models may have on society; partnerships between AI developers, AI stakeholders, and society that might be impacted by AI models should be established; and AI developers need to prioritise transparency and explainability in their models while ensuring adequate testing for bias and corrective measures before deployment. Emotional intelligence training should also be provided to the AI developers to help them engage in productive conversations with individuals outside the development team.

This article dives into the ethical complexities of Artificial Intelligence (AI). It starts by laying the groundwork, defining both ethics and morality, and emphasizing how ethical principles can evolve over time.Next, it explores "applied ethics," which examines real-world dilemmas like genetic engineering, AI's impact on jobs, and environmental concerns. From there, it shifts its focus specifically to AI ethics, differentiating between two key aspects the Ethics of AI where broader framework considers ethical issues surrounding AI development, encompassing concerns like transparency, data security, privacy, and accountability and in Ethical AI where the concept focuses on building AI systems with the ability to make ethical choices. It emphasizes aligning these systems with ethical values, minimizing bias, and fostering user trust. The article delves deeper into specific ethical concerns in the AI landscape, such as transparency, data privacy, and issues of autonomy, intentionality, and responsibility. It explores their relevance and the challenges they pose for AI development. Furthermore, it examines the social implications of these ethical concerns. Key areas examined include automation and job displacement, equitable access to technology, and the potential impact of AI on democratic principles and civil liberties. The article highlights the potential consequences of AI-driven automation on employment, the need for inclusive digital access, and the importance of ethical considerations in governance and citizen rights amidst the growing influence of AI. The article serves as a comprehensive guide to understanding the ethical dimensions of AI. It covers fundamental ethical concepts, specific AI ethics considerations, the importance of building ethical AI systems, and the challenges and societal impacts of AI development. It's a valuable resource for anyone seeking to understand the ethical landscape surrounding Artificial Intelligence.

Artificial intelligence (AI) has revolutionized the pharmaceutical industry, improving drug discovery, development, and personalized patient care. Through machine learning (ML), deep learning, natural language processing (NLP), and robotic automation, AI has enhanced efficiency, accuracy, and innovation in the field. The purpose of this review is to shed light on the practical applications and potential of AI in various pharmaceutical fields. These fields include medicinal chemistry, pharmaceutics, pharmacology and toxicology, clinical pharmacy, pharmaceutical biotechnology, pharmaceutical nanotechnology, pharmacognosy, and pharmaceutical management and economics. By leveraging AI technologies such as ML, deep learning, NLP, and robotic automation, this review delves into the role of AI in enhancing drug discovery, development processes, and personalized patient care. It analyzes AI's impact in specific areas such as drug synthesis planning, formulation development, toxicology predictions, pharmacy automation, and market analysis. Artificial intelligence integration into pharmaceutical sciences has significantly improved medicinal chemistry, drug discovery, and synthesis planning. In pharmaceutics, AI has advanced personalized medicine and formulation development. In pharmacology and toxicology, AI offers predictive capabilities for drug mechanisms and toxic effects. In clinical pharmacy, AI has facilitated automation and enhanced patient care. Additionally, AI has contributed to protein engineering, gene therapy, nanocarrier design, discovery of natural product therapeutics, and pharmaceutical management and economics, including marketing research and clinical trials management. Artificial intelligence has transformed pharmaceuticals, improving efficiency, accuracy, and innovation. This review highlights AI's role in drug development and personalized care, serving as a reference for professionals. The future promises a revolutionized field with AI-driven methodologies.

The article is devoted to the history of development of Information and Communication Technologies (ICT) and Artificial Intelligence (AI), their current and probable future achievements and the problems (which have already arisen, but will become even more acute in the future) associated with the development of these technologies and their active introduction in society. The close connection between the development of AI and cognitive science, the penetration of ICT and AI into various fields, in particular the field of health care, is shown. A significant part of the article is devoted to the analysis of the concept of “artificial intelligence”, including the definition of generative AI. There is performed the analysis of recent achievements in the field of Artificial Intelligence, and there are given descriptions of the basic models, in particular Large Linguistic Models (LLM), and forecasts of the development of AI and the dangers that will await us in the coming decades. We identify the forces behind the aspiration to create artificial intelligence, which is increasingly approaching the capabilities of the so-called general/universal AI, and also suggest desirable measures to limit and channel the development of artificial intelligence. The authors emphasize that the threats and dangers of the development of ICT and AI are partuclarly aggrevated by the monopolization of their development by the state, intelligence services, major corporations and those often referred to as globalists. The article forecasts the development of computers, ICT and AI in the coming decades, and also shows the changes in society that will be associated with them. The study consists of two articles. The first, presented below, provides a brief historical overview and characterizes the current situation in the field of ICT and AI, it also analyzes the concepts of artificial intelligence, including generative AI, changes in the understanding of AI in connection with the emergence of the so-called large language models and related new types of AI programs (ChatGPT). The article discusses the serious problems and dangers associated with the rapid and uncontrolled development of artificial intelligence. The second article, to be published in the next issue of the journal, describes and comments on current assessments of breakthroughs in the field of AI, analyzes various forecasts, and the authors give their own assessments and forecasts of future developments. Particular attention is given to the problems and dangers associated with the rapid and uncontrolled development of AI, the fact that achievements in the field of AI are becoming a powerful means of control over the population, imposing ideology and choice, influencing the results of elections, and a weapon for undermining security and geopolitical struggle.

Accelerated development in artificial intelligence (AI). The phrase has encouraged advancements in drug discovery and development. In this study, we probe the constraints of AI models—AlphaFold, AtomNet, and Insilico GANs—on predictive precision and cross-therapeutic generalizability. We propose HybridAI, a hybrid AI framework that combines geometric deep learning (GDL), reinforcement learning (RL), and federated learning (FL) for improved predictive modelling of drug-target interactions. They were evaluated against metrics such as ROCAUC, RMSD, and hit-rate accuracy across four therapeutic categories: oncology, antimicrobial resistance, neurodegenerative disease, and autoimmune disease. HybridAI was implemented and validated on a dataset of 150 structurally diverse compounds from ChEMBL and DrugBank. The model outperformed current AI frameworks, achieving 92 parcent accuracy in predicting drug-kinase interactions, with a 34 parcent reduction in toxicity prediction error compared to conventional ADME models. A case study involving non-small cell lung cancer (NSCLC) illustrated the in vitro applicability of Hybrid AI. The system correctly identified afatinib as a potent kinase inhibitor, with a predicted binding affinity of 89 parcent. The prediction was confirmed by molecular docking and in vitro assays within 14 days. Our findings highlight the limitations of single-purpose AI models and underscore the need for hybrid systems, such as Hybrid AI, to enhance precision, flexibility, and scalability. The research supports the use of advanced learning methodologies to facilitate personalised medicine and expedite the drug development process. By integrating various AI methods, HybridAI raises the bar for intelligent drug discovery architectures. The rapid growth of artificial intelligence (AI) in drug discovery necessitates a critical evaluation of its predictive validity and therapeutic applicability. The current study aims to compare the predictive performance of different AI-based models for predicting the success of drug therapy and to introduce a novel combinational AI method, HybridAI, to enhance predictive strength and cross-therapeutic applicability. Seven AI models, such as AlphaFold, AtomNet, and Insilico GANs, were thoroughly assessed for drug efficacy, toxicity, and binding affinity prediction in four disease areas: oncology, antimicrobial resistance, neurodegenerative disorders, and autoimmune diseases. Normalized metrics such as receiver operating characteristic (ROC-AUC), root mean square deviation (RMSD), and hit-rate accuracy were used to evaluate the models. HybridAI, a new combinational model incorporating geometric deep learning GDL, reinforcement learning RL, and federated learning FL, was tested on a 150-structurally different compound dataset that was extracted from ChEMBL and DrugBank. Comparative analysis revealed that the existing AI models are 78– 85 parcent accurate in target-specific drug design but show extreme variability (12–28 parcent) in cross-therapeutic generalizability. Hybrid AI outperformed individual models by achieving 92 parcent drug-kinase interactions (compared to 79 parcent with AlphaFold) and a 34 parcent reduction in errors in toxicity prediction compared to conventional ADMET predictors. HybridAI was cross-validated through a case study by repurposing kinase inhibitors for non-small cell lung cancer (NSCLC), with a correct prediction of afatinib based on 89 parcent binding affinity, and subsequently confirmed in vitro within 14 days. The findings highlight the limitations of single AI models for drug discovery and underscore the importance of hybrid AI architectures in delivering greater predictive reliability. By utilising multi-modal learning frameworks, Hybrid AI provides an open and adaptable infrastructure that facilitates the acceleration of precision medicine, reduces inefficiencies in drug development, and personalises therapeutic strategies.