Artificial intelligence and machine learning have revolutionized the pharmaceutical industry, offering new approaches to drug discovery and development. These techniques have the potential to improve the efficiency and accuracy of the drug discovery process, leading to the development of more effective medications.In particular, AI-based algorithms can be employed to predict the efficacy and toxicity of new drug compounds, as well as to identify new targets for drug development. This paper provides an overview of the current landscape of AI in large-molecule drug discovery, highlighting the increasing application of these techniques to areas such as antibodies, gene therapies, and RNA-based therapies. The paper also discusses the challenges and opportunities associated with the use of AI in pharmaceutical research and development, emphasizing the importance of balancing the promise of AI with a continued reliance on the scientific method. While the promise of AI in pharmaceutical research is significant, it is crucial to recognize the limitations of these technologies and to maintain a balanced approach that leverages the strengths of both AI-driven and traditional, scientific methods. By doing so, researchers and developers can harness the power of AI to accelerate the drug discovery process, while ensuring that the development of new drugs remains grounded in robust scientific principles.

Artificial Intelligence, Machine Learning, Drug Discovery, Large Molecule Therapies, Pharmaceutical Research, Development

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