BenchSci Blog Understanding the Limitations of Off-the-Shelf Foundation Models in Preclinical R&D

In recent years, artificial intelligence (AI) has reshaped numerous industries, enhancing innovation and efficiency through task automation and data analysis. This transformation is fueled, in part, by the rise of off-the-shelf foundation models, or large language models (LLMs), like OpenAI's GPT series, BigScience’s BLOOM, and Google's LaMDA. These powerful, pre-trained models, readily available through open-source or commercial platforms, have revolutionized natural language processing and understanding, providing a foundation for developing specialized AI applications and accelerating development cycles. Sectors like customer service, law, and marketing have readily benefited from the advanced capabilities of these models. However, sectors characterized by extreme complexity, such as drug discovery, aerospace, and finance and risk assessment, pose unique challenges that artificial general intelligence (AGI) models struggle to address effectively in their current form. Despite the progress of AI in drug discovery, the intricacies of biological systems and disease mechanisms require specialized approaches beyond the capabilities of off-the-shelf models.