Decoding the Molecular Universe -- Workshop Report

It typically takes 15 years and 2.6 billion dollars to go from a small molecule in the lab to a clinically useable drug. This cost has only increased over the past decade despite advancements in drug development, with one of the biggest bottlenecks being drug target identification. Current efforts are driven by case specific experimentation - a slow and failure-prone process - and each time a new drug is developed these experiments need to be redone from scratch. Recently there has been an explosion of genomic, chemical, clinical, and pharmacological data but no target prediction method has successfully integrated these multiple pieces of evidence into a cohesive approach. Here we propose BANDIT: a Bayesian ANalysis to find Drug Interaction Targets. BANDIT combines data on drug efficacies, post-treatment transcriptional responses, drug structures, side effects, bioassay results, and known targets within a Bayesian framework to predict the targets of small molecules. When applied to a test set of drugs with known targets, BANDIT achieved a predictive power of 91% at identifying drug pairs that share a target (AUROC = 0.91). The power of BANDIT's classifier steadily increased from 60% to 91% as we increased the number of included data types- demonstrating the strength of BANDIT's Big Data approach. Additionally, we found that BANDIT could reproduce the results of a published experimental kinase inhibitor screen with no additional data or training, indicating its potential to replace otherwise tenuous and costly experiments. We next used BANDIT to predict interaction targets for over 50,000 small molecules with no known targets and specifically identified a set of unstudied small molecules predicted to inhibit microtubule formation. We focused on microtubules because of their relevance as chemotherapy targets. Using immunohistochemical staining and tubulin binding assays, we were able to validate these predictions in vitro, identifying new potential chemotherapeutic options. We also found that by clustering drugs based on their BANDIT output, we were able to correctly predict the mechanism of action, as well as the target, for these new small molecule predictions. When this method was applied to the verified microtubule inhibitors, we were able to accurately separate small molecules based on their method of inhibition - polymerizing vs. depolymerizing. Using BANDIT's output we then created an interaction network of all known drugs characterized by their ATC type and intended use. We observed a definite structure to this “drug universe” network with certain drug classes, such as “Dermatologicals” and “Musculo-skeletal System,” clustering together. This information could help advance future drug development efforts by providing information how certain drug classes are similar and could potentially interact in a biological system. Altogether these results attest to BANDIT's ability to provide a novel, broadly applicable way to identify drug targets. By predicting targets for new and established drugs, without the need for numerous target-specific assays, BANDIT could greatly expedite future drug discovery efforts by reducing cost and time. Additionally, by rapidly screening compounds that have been developed but never fully studied we could efficiently increase our potential anti-cancer therapeutic options. Citation Format: Neel S. Madhukar, Linda Huang, Prashant Khade, Katie Gayvert, Paraskevi Giannakakou, Olivier Elemento. Small molecule target prediction and identification of novel anti-cancer compounds using a data-driven bayesian approach. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr B162.

Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properties of molecules in complex mixtures and, in concert, are sensitive enough to resolve even stereoisomers. Despite these experimental advances, small molecule identification is inhibited by (i) chemical reference libraries (e.g., mass spectra, collision cross section, and other measurable property libraries) representing <1% of known molecules, limiting the number of possible identifications, and (ii) the lack of a method to generate candidate matches directly from experimental features (i.e., without a library). To this end, we developed a variational autoencoder (VAE) to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. We extended the VAE to include a chemical property decoder, trained as a multitask network, in order to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, with its focus on properties that can be obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involved a cascade of transfer learning iterations. First, molecular representation is learned from a large data set of structures with m/z labels. Next, in silico property values are used to continue training, as experimental property data is limited. Finally, the network is further refined by being trained with the experimental data. This allows the network to learn as much as possible at each stage, enabling success with progressively smaller data sets without overfitting. Once trained, the network can be used to predict chemical properties directly from structure, as well as generate candidate structures with desired chemical properties. Our approach is orders of magnitude faster than first-principles simulation for CCS property prediction. Additionally, the ability to generate novel molecules along manifolds, defined by chemical property analogues, positions DarkChem as highly useful in a number of application areas, including metabolomics and small molecule identification, drug discovery and design, chemical forensics, and beyond.

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.

This article is dedicated to identifying the fundamental differences between artificial intelligence and machine learning. The aim of the research is to analyze, systematize, and improve the existing theoretical and methodological framework concerning the functioning of artificial intelligence and machine learning, as well as to define the distinctions between these systems. The article examines the current state and prospects for the development of artificial intelligence and machine learning as integral components of innovative technologies that facilitate the automation of complex processes and enhance efficiency across various fields. The research outlines the main stages in the development of artificial intelligence: weak artificial intelligence, which specializes in performing narrow tasks; strong artificial intelligence, aimed at achieving human cognitive capabilities; and superintelligence, which is expected to surpass human intelligence in many aspects. The study substantiates the role of machine learning as a key tool for implementing artificial intelligence, enabling the creation of systems that can self-learn and adapt to changing conditions without additional programming. The article provides examples of the application of artificial intelligence and machine learning in various sectors such as medicine, finance, cybersecurity, marketing, and transportation, where these technologies contribute to improving diagnostic processes, forecasting market fluctuations, and optimizing decision-making. In particular, the main advantages of machine learning are identified, including adaptability and the ability to make predictions based on large data sets, which enhances the effectiveness of analysis and decision-making in real time. The research into the development of artificial intelligence has revealed the technical and ethical challenges associated with creating strong artificial intelligence and superintelligence, which require the development of appropriate regulatory measures. The article emphasizes the significance of artificial intelligence and machine learning for modern society, their impact on various fields of economics, science, and technology, as well as the necessity for further research to ensure the safe and effective development of these technologies.

Since 2019, artificial intelligence (AI) has played an increasingly central role in daily life. Yet, this phase in AI’s evolution is only the beginning of even greater advancements. Catholic institutions, cognizant of the crucial role the Church must assume in the development of AI, have increasingly engaged with this technological frontier. Among these institutions is Longbeard, which has undertaken the creation of Magisterium AI, a compound AI system that leverages several AI technologies and a large database of Catholic knowledge to produce faithful answers. This article offers a critical analysis of the current state of Magisterium AI, drawing on its 2025 Report, Investor Deck, user feedback, and direct engagement with the system to evaluate its alignment with Catholic ethical principles. Specifically, it highlights the expansion of the database as a key area for improvement, while reflecting on the complexities inherent in such an undertaking. The article concludes that the development of a Catholic AI entails more than simply amassing documents in a database. It requires endowing the AI system with a profound grasp of Scripture and its interpretive traditions; a comprehensive knowledge of theology as a whole; a broad acquaintance with the history of ideas, including the development of culture, art, philosophy, and theology; and a finely honed awareness of the evolving nuances of language and cultural sensibilities through which eternal truths are expressed.

This study investigates how generative artificial intelligence (AI) could influence the media environment of that country. Given their prevalence in the industry, it seeks to uncover how general process of national advancement is supported by AI-powered technologies in media content generation, distribution, and personalisation. The research gathers data from media professionals, techies, and communication academics in major Chinese cities all around using properly crafted questionnaires in a quantitative manner. One examined the interactions of AI-driven media innovation, modernising pace, and consequences of national development using statistical methods like structural equation modelling and regression analysis. Linking modernism with national development projects, the indices of generative AI used in media show a definite positive link with their influence. Generative AI is becoming more and more important for China's attempts to modernise its communication infrastructure and undergo digital transformation, thereby enhancing the nation's cultural impact, technical capacity, and media creation. Modernism approaches enhance the possibilities presented by AI in media; this research reveals more dynamic storylines, audience interaction feasible, and global outreach. Every one of these components helps to forward objectives for national growth. Regarding developing plans to mix AI with goals for sustainable development, the findings give legislators, media outlets, and AI developers’ useful insights. This study offers a structure for further studies in new media technologies and national transformation by means of an experimental interaction between generative AI and national development seen from the perspective of modernism. It opens the conversation on the social opportunities AI offers.

The research relevance is determined by the importance of a thorough study of methods, schemes and models used by artificial intelligence to mechanise creativity in modern conditions of active technological development. The study aims to analyse the main processes taking place in modern art in connection with active technologization of work processes, to identify the leading concepts regarding the possibility of creating machine art in the future, etc. The employed methods are theoretical, such as analysis, systematisation, generalisation, etc., for studying key problems and further development of creativity based on artificial intelligence. The study examines in detail the main developments of Artificial General Intelligence and Artificial Narrow Intelligence, in particular the achievements of Generative adversarial networks and Creative adversarial networks. Artificial intelligence-generated art demonstrates the remarkable capabilities of technologies. The evolving artificial intelligence in the arts introduces “digital art”. Generative Adversarial Networks are used as a foundational tool for artists who use digital methods and texture generation to create unique compositions. Furthermore, sculptors collaborate with artificial intelligence tools to convert drawings into 3D models or transform historical art databases into sculptures. Creative thinking, a hallmark of human intelligence, is determined as artificial intelligence’s ability to generate new and original ideas. The development of emotional intelligence in artificial intelligence enables empathetic responses and the identification of human emotions through voice and facial expressions. The issues of authorised internationality, awareness of the creative process, psychological foundations of artificial empathy and emotional intelligence define the prospects for the development of neuroscience. Challenges persist in defining creativity, authorship, and legal aspects of artificial intelligence-generated art. The study materials may be useful for artists, art educators, technologists, and researchers interested in the intersection of technology and art, legal professionals (especially intellectual property law), and individuals involved in artificial intelligence development may find these findings valuable

The 2025 3rd International Conference on Computer, Machine Learning and Artificial Intelligence (CMLAI 2025) had been held in San Francisco, USA during March 29-30, 2025. CMLAI 2025 is an international forum devoted specifically to facilitate synergies in research and development in the areas of computer science, machine learning, neural networks, algorithm design, and artificial intelligence. It provides the communication platform for those who are leading experts and scholars from around the world. The development of artificial intelligence and their corresponding technologies and industries has also been regarded as "national development strategy" for the world's leading countries and gradually developed into a new "trump card" in the game between countries. This conference was attended by over 60 researchers, scholars, academic and industry experts from many countries. During the two-day conference, 4 keynote speeches with topics on cloud computing, machine learning, neural networks, and artificial intelligence were delivered. And 24 oral and invited presentations were presented and discussed in-depth in 4 technical sessions, with interactive communication among the attendees. The conference successfully promoted exchanging new ideas and perspective discourse among the participants in their respective fields. CMLAI Organizing Committees San Francisco, USA

This paper explains the ethical implications of artificial intelligence (AI) development towards achieving the level of artificial general intelligence (AGI) and analyzes the need for its social control. With the acceleration and intensification of AI growth and development, especially with the ongoing AI race, the transition from narrow AI to AGI becomes certain. The achievement of generative AI, which climaxes with chatbots (such as ChatGPT and others), transforms AI into a machine capable of creation. Although this AI application still appears relatively limited by algorithms, its learning ability is remarkable, and continuous advancements and the launch of increasingly sophisticated versions bring it ever closer to the AGI model. Each day brings us closer to that moment, which will signify AI’s transition from narrow AI to AGI. Unlike narrow AI, AGI deeply delves into the realm of ethics, and interpersonal and social relationships. Regulating AI-related policy and legally controlling AI represents one of the most serious and complex issues. In recent years, the community, led by corporate executives developing AI, prominent experts, researchers, scientists, writers, and other stakeholders, has made significant steps towards raising public awareness of the risks posed by advanced AI and making decisions, initiatives, and measures for monitoring, analyzing, and socially controlling the use of AI.

Purpose. The digital transformation of business in green smart cities is crucial due to the evolving needs of residents and businesses, as well as the increasing demands for enhancing efficiency, improving the quality of urban life, and protecting the environment. The aim of this publication is to characterize the directions of development in artificial intelligence (AI) and cloud computing that support digital transformation and sustainable urban development. It is worth noting that there is a gap in the literature, as clear ideas are lacking on how to effectively utilize deep learning based on artificial neural networks (ANN) in the cloud. Methods. Basic research methods include critical analysis of the literature on the subject. In addition, modeling was used to simulate the use of deep learning and cloud computing in smart city management systems. Intensive computational experiments were carried out to analyze the quality of the solutions, which were determined by the proposed deep learning methods using ANN based on Long Short Term Memory (LSTM). Results. The results of theoretical research and numerical experiments confirmed significantly contributing AI and cloud computing in the increasing the efficiency of the city and improving the quality of resident’s life and protecting the natural environment.

The present study investigates the potential impact of artificial intelligence (AI) on the future trajectory of human civilization. It focuses on topics such as super-exponential growth, the potential emergence of a galactic civilization, and the associated "doom" hazards. A significant advancement in machine intelligence with human-like consciousness, strong artificial intelligence (AI), also known as artificial general intelligence (AGI), creates new opportunities and capacities. There's growing anxiety about the risk that weak AI will eventually become strong AI. Every year, new transformer models that are more like human interactions are being created, and we have already witnessed some indications of AGI. It is anticipated that AI will reach a "singularity" and advance on its own without assistance from humans. This thesis explores the theoretical and practical foundations, model building blocks, development processes, challenges, and ethical issues surrounding the creation of Consciousness AI (AGI). This paper examines the meaning of the term "technological singularity," the various types of singularities that have no point of return idea, the philosophical risks associated with the development of AI, and the implications of AI singularity for monetary theory and the new economic order. As a new perspective on the deployment of ethical AI in the face of tremendous technological advancements, the study not only contributes to the theoretical discourse but also explores the possible practical implications of AI on our shared future. Several obstacles to AI advancement are covered in the paper, along with prospective directions for future research.

Integrating artificial intelligence (AI) into health care offers the potential to address critical challenges related to access to care, workforce burnout, and health inequities. Despite its promise, AI adoption remains limited due to safety, efficacy, and equity concerns. This paper presents a novel and comprehensive framework for responsible AI development, evaluation, and deployment in health care, encompassing four key phases: (1) AI Solution Design and Development, (2) AI Solution Qualification, (3) AI Solution Efficacy and Safety Evaluation, and (4) AI Solution Impact. By establishing rigorous standards for operational, clinical, and technical quality, the framework aims to guide AI developers and health care professionals toward creating AI solutions that are ethical, effective, and scalable. This structured approach fosters collaboration and mitigates risks to help AI achieve its full potential in improving patient outcomes and health care efficiency.

New Synthesis: Chemical Ecology and Sustainable Food Production

In recent decades, rapid development of artificial intelligence (AI) and other new technologies, such as big data, cloud computation and Internet of Things (IoT), have produced huge impacts on human society. The purpose of this chapter is to discuss the impacts and challenges of the Fourth Industrial Revolution and AI on social, commercial and geopolitical structures. Specifically, this chapter will explain how AI and new industrial revolution redistribute wealth, power and jobs across firms, social classes and nations.

In recent years, due to the rapid development of artificial intelligence (AI) and machine learning, its application has been widely used in many aspects of financial area, as well as significantly impacts financial market, institutions and regulation. The artificial intelligence technology brings enormous change to the entire financial industry, which creates a series of innovative financial services such as intelligent consultant, intelligent lending, monitoring and warning, and intelligent customer service as times required. In this paper, it aims to summarize the development and application of artificial intelligence and machine learning in financial system, as well as its impacts on macroeconomics and microeconomics. In the meantime, it is realised that a series of problems and risks were conducted by artificial intelligence during its use. Lastly, some suggestions and strategies are provided for reasonable usage of artificial intelligence in financial risk management, based on the financial risk management raised by artificial intelligence.