Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

AI in analytical chemistry: Advancements, challenges, and future directions

This article explores the influence and applications of Artificial Intelligence (AI) in analytical chemistry, highlighting its potential to revolutionize the analysis of complex data sets and the development of innovative analytical methods. Additionally, it discusses the role of AI in interpreting large-scale data and optimizing experimental processes. AI has been fundamental in managing heterogeneous data and in advanced analysis of complex spectra in areas such as spectroscopy and chromatography. The article also examines the historical development of AI in chemistry, its current challenges, including the interpretation of AI models and the integration of large volumes of data. Finally, it forecasts future trends and the potential impact of AI on analytical chemistry, emphasizing the need for ethical and secure approaches in the use of AI.

Comprehensive Review on the Virulence Factors and Therapeutic Strategies with the Aid of Artificial Intelligence against Mucormycosis

Mucormycosis, a rare but deadly fungal infection, was an epidemic during the COVID-19 pandemic. The rise in cases (COVID-19-associated mucormycosis, CAM) is attributed to excessive steroid and antibiotic use, poor hospital hygiene, and crowded settings. Major contributing factors include diabetes and weakened immune systems. The main manifesting forms of CAM─cutaneous, pulmonary, and the deadliest, rhinocerebral─and disseminated infections elevated mortality rates to 85%. Recent focus lies on small-molecule inhibitors due to their advantages over standard treatments like surgery and liposomal amphotericin B (which carry several long-term adverse effects), offering potential central nervous system penetration, diverse targets, and simpler dosing owing to their small size, rendering the ability to traverse the blood-brain barrier via passive diffusion facilitated by the phospholipid membrane. Adaptation and versatility in mucormycosis are facilitated by a multitude of virulence factors, enabling the pathogen to dynamically respond to various environmental stressors. A comprehensive understanding of these virulence mechanisms is imperative for devising effective therapeutic interventions against this highly opportunistic pathogen that thrives in immunocompromised individuals through its angio-invasive nature. Hence, this Review delineates the principal virulence factors of mucormycosis, the mechanisms it employs to persist in challenging host environments, and the current progress in developing small-molecule inhibitors against them.

Holistic One Health Surveillance Framework: Synergizing Environmental, Animal, and Human Determinants for Enhanced Infectious Disease Management

Recent pandemics, including the COVID-19 outbreak, have brought up growing concerns about transmission of zoonotic diseases from animals to humans. This highlights the requirement for a novel approach to discern and address the escalating health threats. The One Health paradigm has been developed as a responsive strategy to confront forthcoming outbreaks through early warning, highlighting the interconnectedness of humans, animals, and their environment. The system employs several innovative methods such as the use of advanced technology, global collaboration, and data-driven decision-making to come up with an extraordinary solution for improving worldwide disease responses. This Review deliberates environmental, animal, and human factors that influence disease risk, analyzes the challenges and advantages inherent in using the One Health surveillance system, and demonstrates how these can be empowered by Big Data and Artificial Intelligence. The Holistic One Health Surveillance Framework presented herein holds the potential to revolutionize our capacity to monitor, understand, and mitigate the impact of infectious diseases on global populations.

Attention is all you need: utilizing attention in AI-enabled drug discovery

Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. We further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation. Finally, we discuss the current challenges faced in the application of attention mechanisms and Artificial Intelligence technologies, including data quality, model interpretability and computational resource constraints, along with future directions for research. Given the accelerating pace of technological advancement, we believe that attention-based models will have an increasingly prominent role in future drug discovery. We anticipate that these models will usher in revolutionary breakthroughs in the pharmaceutical domain, significantly accelerating the pace of drug development.