Artificial intelligence (AI) is transforming medication research and development, giving clinicians new treatment options. Over the past 30 years, machine learning, deep learning, and neural networks have revolutionized drug design, target identification, and clinical trial predictions. AI has boosted pharmaceutical R&D (research and development) by identifying new therapeutic targets, improving chemical designs, and predicting complicated protein structures. Furthermore, generative AI is accelerating the development and re-engineering of medicinal molecules to cater to both common and rare diseases. Although, to date, no AI-generated medicinal drug has been FDA-approved, HLX-0201 for fragile X syndrome and new molecules for idiopathic pulmonary fibrosis have entered clinical trials. However, AI models are generally considered "black boxes," making their conclusions challenging to understand and limiting the potential due to a lack of model transparency and algorithmic bias. Despite these obstacles, AI-driven drug discovery has substantially reduced development times and costs, expediting the process and financial risks of bringing new medicines to market. In the future, AI is expected to continue to impact pharmaceutical innovation positively, making life-saving drug discoveries faster, more efficient, and more widespread.
Keywords: FDA; algorithms; artificial intelligence; black boxes; medicine discovery.
Artificial intelligence in drug development: reshaping the therapeutic landscape The pharmaceutical industry has enormous and growing amounts of data, and in terms of models, the best AI pharma model is not to build pure AI processes. Combining humans and AI is often superior to human processes or AI processes alone. Just as in chess, the combination of a human and a computer algorithm can usually beat a human or a computer algorithm alone. AI technology methods need to be sorted out and developed. AI’s attention, exploration, and application trials in all sectors of society will inevitably accelerate the maturation and innovation of AI technology methods. When the logic of the “large data → more accurate models → better drugs → more and better data” cycle matures in practice, AI pharma will be significantly accelerated. However, the application and diffusion of any technology are challenging to achieve overnight, and it is the law of development that new things spiral and move in waves. AI and data-driven pharma models need to be explored and practiced more and more before they can truly demonstrate their value.
© The Author(s), 2025.