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Drug discovery is usually a rule-based process that is carefully carried out by pharmacists. However, a new trend is emerging in research and practice where artificial intelligence is being used for drug discovery to increase efficiency or to develop new drugs for previously untreatable diseases. Nevertheless, so far, no study takes a holistic view of AI-based drug discovery research. Given the importance and potential of AI for drug discovery, this lack of research is surprising. This study aimed to close this research gap by conducting a bibliometric analysis to identify all relevant studies and to analyze interrelationships among algorithms, institutions, countries, and funding sponsors. For this purpose, a sample of 3884 articles was examined bibliometrically, including studies from 1991 to 2022. We utilized various qualitative and quantitative methods, such as performance analysis, science mapping, and thematic analysis. Based on these findings, we furthermore developed a research agenda that aims to serve as a foundation for future researchers.

Urbanization is a global trend that continues to grow, leading to an increasing number of people to live in cities. This rapid expansion creates challenges such as traffic congestion, environmental pollution, and the need to ensure high living standards for all residents. To address these challenges, many cities adopt digital technologies to become smarter, more efficient, and more sustainable. Among these technologies, artificial intelligence (AI) has gained significant attention in recent years due to its transformative potential. In the context of smart cities, AI offers innovative solutions across various domains, including mobility, waste management, and energy optimization. Due to its multidisciplinary nature and rapid advancements, research on AI in smart cities has grown significantly. A comprehensive approach is needed to understand its role in urban transformation and identify key research gaps. This paper aims to synthesize existing knowledge on AI in smart cities, providing valuable insights for both researchers and practitioners. We define the scope of AI-related research by analyzing scientific literature and offer three main contributions. First, we provide a holistic overview of the field by conducting a bibliometric analysis to map the status and structure of existing knowledge. Second, we identify major research themes through co-citation clustering. Third, we outline a future research agenda by analyzing the most recent and influential journal articles. Our findings have both theoretical and practical implications for a wide range of disciplines, including computer science, energy, transportation, and security. Furthermore, our results can facilitate collaboration by identifying leading researchers and institutions, highlight critical research gaps, and foster discussions on the benefits and challenges of AI-driven smart city solutions.

Project management and project portfolio management (PPM) foster competitiveness by facilitating the implementation of organizational strategy. Although organizations often struggle to develop PPM capabilities, the academic community does not have an in-depth understanding of the conditions for successfully developing these capabilities. In response, we conducted a multiple-case study with 50 interviewees to develop a theoretical model of the PPM capability-building process. This model is built on the notion of organizational sensemaking and identifies aspects that comprehensively explain why it usually takes so long to develop PPM capabilities. We conceptualize the PPM capability-building process as one that is strongly influenced by (1) the effects of structural rearrangements, (2) the appropriate use of external resources during that process, (3) the role of executive support and legitimization, (4) episodes of regression, and (5) the need for internalization and habitualization. In addition, we provide starting points for explaining organizational capability building in more general terms.

Motivated by the historical use and increasing relevance of artificial intelligence (AI) in wildfire management, this study reviews the role of AI in wildfire management through a bibliometric study using a dataset of 1,985 peer-reviewed publications sourced from Scopus. The analysis identifies four thematic clusters: (1) geospatial and climatic analysis of wildfires using remote sensing and prediction, (2) technological and algorithmic advancements for wildfire detection and monitoring, (3) machine learning–driven wildfire prediction, risk assessment, and behavior modeling. Our findings show a multidisciplinary and application-oriented research field with increasing relevance due to climate change and escalating fire events. Based on our findings, we propose future research directions, including multimodal data integration, explainable AI, and real-time human-AI collaboration. This study contributes to a systematic understanding of current AI approaches in wildfire research and supports the development of a targeted research agenda for advancing technological and scientific responses to wildland fire challenges.

PurposeAccounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.Design/methodology/approachThe authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.FindingsThe authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.Research limitations/implicationsOwing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.Practical implicationsGiven the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.Originality/valueTo the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

Today’s cities face numerous challenges due to climate change and urbanization. The concept of a smart city aims to help cities to address these challenges by adapting modern information and communication technology. Smart mobility and transportation form one important aspect of smart cities. Inefficient mobility in cities can lead to problems such as traffic congestion, which results in frustration for residents and a decrease in the quality of life. Against the backdrop of global warming, cities also strive to reduce CO2 emissions, an attempt which requires sustainable and novel mobility concepts. Blockchain is a current technology, said to have huge potential, that is being investigated for application in many facets of smart cities. In the context of smart mobility, blockchain can be used for transactions relating to ridesharing and electric charging, handling of interactions of platoon members, or serving as a foundation for communication between vehicles. Although initial research about this topic exists, it is distributed among different use-cases and applications. This article conducts a systematic literature review to analyze blockchain’s role in mobility and transportation in smart cities, and its potential to increase efficiency in these areas. With this review, we aim to consolidate and summarize the current knowledge about this topic. As a first result, we present the findings from our literature review, which can be divided into five categories of use-cases. We also present a platform for further research about this emerging topic by identifying promising future research avenues. For this purpose, we derive a future research agenda based on our findings.

Data are the foundation of the digital economy, but various challenges regarding managing data assets still exist. One approach to solving these challenges is applying the data-sharing economy principles. Many companies are, however, unsure of the factors that need to be developed to enter a data ecosystem successfully with other, partially competing organizations. Based on qualitative data gathered from an interview study, this research paper applies a framework for organizational readiness factors to data ecosystems. Legal foundation, top management support, and stakeholder involvement in data ecosystems are the main factors highlighted by the study. Furthermore, our empirical results confirmed our preliminary findings from a structured literature review and extend the given research framework. Daten sind die Grundlage der digitalen Wirtschaft. Allerdings gibt es immer noch verschiedene Herausforderungen, Daten als Vermögenswert zu managen. Eine Lösung davon ist die Anwendung von Prinzipien der Data Sharing Economy. Viele Unternehmen sind unsicher, welche Faktoren entwickelt werden müssen, um erfolgreich in ein Datenökosystem mit anderen, teilweise konkurrierenden Organisationen einzutreten. Auf der Grundlage von qualitativen Daten, die im Rahmen einer Interviewstudie gesammelt wurden, erforschen wir organisatorische Bereitschaftsfaktoren für Datenökosysteme. Die rechtlichen Grundlagen, die Unterstützung durch das Topmanagement und die Einbeziehung der Stakeholder in DEs sind die am häufigsten genannten Faktoren. Außerdem bestätigen unsere empirischen Ergebnisse die Erkenntnisse aus unserer Literaturrecherche und erweitern den vorgegebenen Forschungsrahmen.

The transition to multicellularity has occurred numerous times in all domains of life, yet its initial steps are poorly understood. The volvocine green algae are a tractable system for understanding the genetic basis of multicellularity including the initial formation of cooperative cell groups. Here we report the genome sequence of the undifferentiated colonial alga, Gonium pectorale, where group formation evolved by co-option of the retinoblastoma cell cycle regulatory pathway. Significantly, expression of the Gonium retinoblastoma cell cycle regulator in unicellular Chlamydomonas causes it to become colonial. The presence of these changes in undifferentiated Gonium indicates extensive group-level adaptation during the initial step in the evolution of multicellularity. These results emphasize an early and formative step in the evolution of multicellularity, the evolution of cell cycle regulation, one that may shed light on the evolutionary history of other multicellular innovations and evolutionary transitions. The undifferentiated Gonium pectorale represents the initial transition to multicellularity. Here, Bradley Olson, Erik Hanschen and colleagues describe the genome of Gonium pectorale , demonstrating that co-option of the retinoblastoma cell cycle regulatory pathway was a key genetic change in the evolution of multicellularity.

People living with HIV infection (PLWH) often have attenuated responses to infections and vaccination. This study aimed to better understand how HIV-associated inflammation and chronic T-cell activation influenced the immune responses to mRNA vaccination or neutralization assay analyses. PLWH on ART or healthy donor controls were analyzed using systems serology and viral T-cell phenotyping to Spike or HIV-1 Gag peptide stimulation after primary mRNA COVID-19 vaccination. Neutralization assays using a lentiviral pseudovirus construct were compromised by the presence of integrase strand transfer inhibitor (INSTI) drugs in plasma from HIV + subjects taking certain ART. This combination of lentiviral pseudovirus reporter assays and INSTIs led to false positive neutralization results. Spike-specific IgG1, IgG3, IgA1, IgA2, and antibody-dependent cellular phagocytosis (ADCP) were altered post-vaccination in PLWH compared to controls. Network and multivariate analyses revealed post-vaccination outcomes were strongly correlated to CD4 immunodeficiency and Gag-specific T-cells, including effector CD8 T-cells and Th1 CD4 T-cells. Given the growing use of pseudovirus neutralization assays for serological evaluation and mRNA technology in novel vaccines that could be recommended for PLWH Pseudovirus neutralization assays need to be carefully selected to prevent ART drugs in patient samples from impacting results. Spike-specific antibody and CD4 T-cell phenotypes are influenced by both CD4 immunodeficiency and Gag-specific T-cell effector populations. This work has clinical relevance beyond COVID, with future considerations of pseudovirus assay evaluations and mRNA vaccine design for chronically infected hosts.