Industrial production processes, especially in the pharmaceutical industry, are complex systems that require continuous monitoring to ensure efficiency, product quality, and safety. This paper presents a hybrid unsupervised learning strategy (HULS) for monitoring complex industrial processes. Addressing the limitations of traditional Self-Organizing Maps (SOMs), especially in scenarios with unbalanced data sets and highly correlated process variables, HULS combines existing unsupervised learning techniques to address these challenges. To evaluate the performance of the HULS concept, comparative experiments are performed based on a laboratory batch

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.

French startup Iktos has secured €15.5 million in Series A funding for its AI-powered drug discovery platform. The round was co-led by M Ventures and Debiopharm Innovation Fund, with participation from Omnes Capital, and will enable Iktos to develop its technology further and expand its SaaS software offering. The company is set to launch Robotics, an end-to-end drug discovery platform that uses AI and automation of chemical synthesis to accelerate drug discovery timelines. Additionally, the company will expand its application of solutions to biological products, allowing it to offer fully integrated drug discovery services to the pharmaceutical industry. Iktos, which was founded in 2016, uses a technology platform for deep learning-based drug design that offers significant productivity gains in upstream pharmaceutical R&D.

Artificial intelligence and other advances in technology are having a huge impact on every sector of human activity. New ways of researching, manufacturing, commercialising and delivering products and services appear every day, with more than a few paradigm-shifting technological breakthroughs seemingly on the horizon. The intersection of technology and medicine has always been an important one. The way we identify, diagnose and treat diseases is fundamentally linked to technology. In this article, I examine three key ways technology, and especially artificial intelligence (AI) and machine learning (ML), are changing the face of Pharma.

Dissolution testing is part of the target product quality that is essential in approving new products in the pharmaceutical industry. The prediction of the dissolution profile based on spectroscopic data is an alternative to the current destructive and time-consuming method. Raman and near-infrared (NIR) spectroscopies are two fast and complementary methods that provide information on the tablets' physical and chemical properties and can help predict their dissolution profiles. This work aims to compare the information collected by these spectroscopy methods to support the decision of which measurements should be used so that the accuracy requirement of the industry is met. Artificial neural network models were created, in which the spectroscopy data and the measured compression curves were used as an input individually and in different combinations in order to estimate the dissolution profiles. Results showed that using only the NIR transmission method along with the compression force data or the Raman and NIR reflection methods, the dissolution profile was estimated within the acceptance limits of the f2 similarity factor. Adding further spectroscopy measurements increased the prediction accuracy.

"The development of full artificial intelligence could spell the end of the human race…it would take off on its own, and re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn't compete, and would be superseded." Artificial Intelligence is undoubtedly one of the key technologies that defines the 21st century. Before throwing this two-word phrase around, having a general understanding of what Artificial Intelligence (AI) entails is important. To put it simply, AI is an attempt to emulate and simulate varied forms of human intelligence in machines.

The healthcare industry and national healthcare systems across the world have experienced unprecedented pressure in recent years, especially during the Covid-19 pandemic. Many HealthTech start-ups started investing in healthcare technology in the early 2010s, but the crisis in 2020 inevitably accelerated digital transformation to allow medical professionals to keep seeing patients during the lockdowns. Recent research by Virgin Media Business, examining the use of information technology in healthcare in the UK, found that the benefits of digital transformation in operational areas are already showing. For example, AI is helping to streamline patient triage, enabling doctors to treat urgent cases more rapidly with lifesaving outcomes. But what will be the real impact of information technology in healthcare going forward?

It was early one morning in 1996 when Andrew Hopkins, then a PhD biophysics student at Oxford University, had a brainwave as he walked home from a late-night lab meeting. He was trying to find molecules to fight HIV and to better understand drug resistance. "I remember this idea struck me that there must be a better way to do drug discovery other than the complex and expensive way everyone was following," he says. "Why couldn't we design an automated approach to drug design that would use all the information in parallel so that even a humble PhD student could create a medicine? That idea really stuck with me. I remember almost the exact moment to this day. And that was the genesis of the idea that eventually became Exscientia."

Santiago Miret is a deep learning researcher at Intel Labs, where he focuses on developing artificial intelligence (AI) solutions and exploring the intersection of AI and the physical sciences. The successful design and deployment of novel material technologies in the last couple of decades has enabled tremendous innovations across various industries. Building today's smartphones, for example, would have cost about 100 million dollars in the 1980s and yielded a 14 meters tall device, both of which would be very impractical. Furthermore, materials innovations surrounding silicon have enabled advances in microelectronics and computer technologies that build the foundation of a technology-enabled world, including the recent proliferation of artificial intelligence (AI). Similar, albeit different advances, in silicon technology and perovskites, a class of semiconductor materials that transport the electric charge of light, have provided the basis for solar photovoltaic cells which enable the harvesting of renewable solar energy thereby driving a redesign of the energy industry to a more sustainable and less carbon-heavy system.

"This person is tasked with packing the ML model into a container and deploying to production -- usually as a microservice," says Dattaraj Rao, innovation and R&D architect at technology services company Persistent Systems. The role requires expert back-end programming and server configuration skills, as well as knowledge of containers and continuous integration and delivery deployment, Rao says. They are crucial to AI initiatives because data needs to be both collected and made suitable for consumption before anything trustworthy can be done with it, says Erik Gfesser, director and chief architect at Deloitte. This person is an authority in their domain, can judge the quality of available data, and can communicate with the intended business users of an AI project to make sure it has real-world value. When Babych's company developed a computer-vision system to identify moving objects for autopilots as an alternative to LIDAR, they started the project without a domain expert.