Computational Intelligence Modeling of Pharmaceutical Properties

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.In the pharmaceutical industry, a good understanding of the casual relationship between product quality and attributes of formulations is very useful in developing new products, and optimizing manufacturing processes. Feature selection is mandatory due to the abundance of noisy, irrelevant, or misleading features. The selected features will improve the performance of the prediction model and will provide a faster and more cost effective prediction than using all the features. With the big data captured in the pharmaceutical product development practice, computational intelligence (CI) models and machine learning algorithms could potentially be used to identify the process parameters of formulations and manufacturing processes. That needs a deep investigation of roller compaction process parameters of pharmaceutical formulations that affect the ribbons production. In this work, we are using the bio-inspired optimization algorithms for feature selection such as (grey wolf, Bat, flower pollination, social spider, antlion, moth-flame, genetic algorithms, and particle swarm) to predict the different pharmaceutical properties.European Cooperation in Science and Technology. COSTThis work was supported by the IPROCOM Marie Curie initial training network, funded through the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA grant agreement No. 316555. In addition, this work was partially supported by NESUS

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Designing algorithms to aid discovery by chemical robots

Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for knowledge discovery

Future Trends in Pharmaceuticals: Investigation of the Role of AI in Drug Discovery, 3D Printing of Medications, and Nanomedicine

The pharmaceutical sector has to deal with issues like high costs, difficult diseases, and the demand for tailored therapy. The transformational potential of AI, 3D printing, and nanomedicine is examined in this paper. Drug development is revolutionized by AI, which also predicts effectiveness and personalizes therapies. Tailors, prescriptions, and complex documents can all be 3D printed to help with compliance. Nanoparticles are used in nanomedicine to deliver drugs more precisely and enhance solubility. Future themes include AI-driven target identification and individualized treatment; the effectiveness and role of 3D printing in personalized medicine; and improved medication delivery through nanomedicine. These developments promise to alter healthcare, which will help a lot of people. The study results offers a thorough examination of upcoming trends in the pharmaceutical industry and similarly discusses developments in 3D printing and nanomedicine

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Revolutionizing Pharma: Unveiling the AI and LLM Trends in the Pharmaceutical Industry

This document offers a critical overview of the emerging trends and significant advancements in artificial intelligence (AI) within the pharmaceutical industry. Detailing its application across key operational areas, including research and development, animal testing, clinical trials, hospital clinical stages, production, regulatory affairs, quality control and other supporting areas, the paper categorically examines AI's role in each sector. Special emphasis is placed on cutting-edge AI technologies like machine learning algorithms and their contributions to various aspects of pharmaceutical operations. Through this comprehensive analysis, the paper highlights the transformative potential of AI in reshaping the pharmaceutical industry's future

The Coming Quantum Computing Evolution in the Pharmaceutical Industry and Drug R&D

Quantum computing is poised to revolutionize the pharmaceutical industry and dramatically accelerate the drug discovery and development process. By harnessing the power of quantum mechanics, quantum computers can analyze molecular interactions and simulate chemical processes with unprecedented speed and accuracy. This article provides a comprehensive overview of how quantum computing will impact drug R&D, with a focus on the key application areas of molecular modeling, genomics, clinical trials, and drug discovery. An in-depth analysis is provided on how quantum algorithms, quantum machine learning, and quantum simulations will enable faster and more targeted drug design, predictive modeling of drug interactions, accelerated genomics analysis, and improved clinical trial design. The challenges facing the development of quantum computing in pharma are also discussed. Overall, quantum computing offers immense promise to slash the time and cost of bringing new life-saving drugs to market, as well as unlocking new capabilities in personalized medicine and drug optimization