Decoding AI and its role in the ever-evolving pharmaceutical world

Luca Zanotti Fragonara, Head of the Advanced Technologies Competence Center and Senior Associate Partner at PQE Group, examines the evolution of AI from its origins to its transformative impact on the pharmaceutical industry and shows how advances in machine learning, deep learning and reinforcement learning are reshaping research and development.

By Luca Zanotti Fragonara | August 14, 2024

Artificial intelligence (AI) has come a long way since Logic Theorist was first introduced in 1956, a groundbreaking computer program to mimic human problem-solving skills that was supported by the RAND Corporation. While there are several different definitions that attempt to explain what AI is and what it does, it is generally agreed that the technology involves the development of computer systems capable of mimicking human intelligence through learning, pattern recognition, and decision-making. With language learning models (LLMs) such as OpenAI’s ChatGPT and Google’s Gemini entering the consumer AI space in 2022, it is easy to be misled into thinking that generative AI and LLMs are all that AI technology has to offer. Since Logic Theorist’s humble beginnings at the Dartmouth Summer Research Project on Artificial Intelligence in the late 1950s, the technology has seen a shift from simple logic and problem-solving tools to deep learning and everyday integration. This is thanks to the massive amounts of data and advanced computing power of the 2010s, which enabled the development of complex neural networks that can process massive data sets and achieve unprecedented accuracy in tasks such as image recognition, natural language processing, and autonomous decision making. Advances in AI have driven innovation not only in the technology sector, but also in technology-dependent fields such as pharmaceuticals and biomedical engineering. In the last decade alone, the use of AI in our industry has grown in unprecedented ways, with applications in compliance and advanced manufacturing, decision making, and supply chain management, pushing the technology further into critical areas such as personalized medicine, predictive maintenance, and efficient production.

Are AI, machine learning and deep learning the same thing?

Despite its popularity and growth over the past decade, AI is often misunderstood, largely due to mainstream media focusing on specific subfields of AI and portraying them as the entirety of the field. In fact, subfields of AI such as machine learning, deep learning, and natural language processing (NLP) are distinct yet interrelated categories within the broader AI sphere, each with its own scope, techniques, and applications.

Machine Learning vs. Deep Learning

Although both involve “learning,” deep learning and machine learning are distinct subfields with unique architectures within the broader field of artificial intelligence, with each taking a different approach to processing data. While machine learning relies on input from subject matter experts to manually engineer features from data, deep learning uses deep neural networks to automatically learn hierarchical representations of data directly from raw inputs. This unique approach to processing data makes deep learning the ideal method for dealing with complex unstructured data without human intervention, which can be incredibly useful for the pharmaceutical industry as this technology can identify patterns and correlations in massive biomedical datasets.

Deep learning vs. reinforcement learning

Unlike deep learning, which involves learning representations from data through layers of neural networks, reinforcement learning focuses on learning through trial and error to achieve intended goals. While deep learning excels at recognizing patterns in tasks such as image and speech recognition, reinforcement learning uses a reward and punishment system to guide actions and model behavior.

Prospects for an AI-powered pharmaceutical industry.

As AI finds its place in the pharmaceutical industry, there is a lot of untapped potential for innovation and progress in pharmaceutical research and development. With the ability to analyze vast amounts of data and provide predictions and recommendations, AI can help accelerate innovation by optimizing clinical trials and drug development to improve efficacy and safety. PQE Group is committed to integrating new technologies to advance the industry, including adopting AI to help customers produce better and safer products.

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