The field of scientific discovery is undergoing a significant transformation with the increasing use of artificial intelligence (AI) and large language models (LLMs). Recent developments have focused on improving the efficiency and effectiveness of scientific idea generation, experiment design, and hypothesis testing. Notably, researchers are exploring the use of LLMs to automate tasks such as dataset retrieval, baseline recommendation, and drug discovery. Additionally, there is a growing emphasis on developing standardized benchmarks and evaluation frameworks to assess the performance of AI-driven systems. These advancements have the potential to accelerate scientific progress and improve the discovery of new candidates for various applications.

Some noteworthy papers in this area include: The paper on ESCAPE presents a comprehensive framework for multilabel antimicrobial peptide classification, achieving state-of-the-art results and providing a reproducible evaluation framework. The AlphaResearch paper introduces an autonomous research agent that discovers new algorithms on open-ended problems, demonstrating the possibility of accelerating algorithm discovery with LLMs.