The potential and pitfalls of large language models in molecular biosciences

Abstract

Large language models (LLMs) have revolutionized information processing and are now being applied to complex problems in molecular biosciences. LLMs excel at learning patterns in biological sequences, enabling applications such as predicting the function of DNA regulatory elements, analysing chromatin modifications and predicting the effects of genetic variants. In drug discovery, LLMs are being used to optimize drug properties, design novel molecules and even automate compound synthesis. However, realizing the full potential of LLMs in the molecular biosciences requires addressing challenges such as designing effective learning objectives, generating high-quality, domain-specific datasets and building trust in AI-assisted decision-making. As these challenges are addressed, LLMs hold immense potential for advancing research and driving ground-breaking discoveries in molecular biosciences.