The field of drug discovery and scientific research is undergoing a significant transformation with the integration of artificial intelligence (AI) agents. These agents are capable of autonomously reasoning, acting, and learning through complex research workflows, enabling the integration of diverse biomedical data, execution of tasks, and iterative refinement of hypotheses. The use of large language models (LLMs) coupled with perception, computation, action, and memory tools is allowing for the development of agentic AI systems that can accelerate scientific discovery. Notably, the application of AI agents in drug discovery is showing substantial gains in speed, reproducibility, and scalability, with the potential to compress workflows that once took months into hours. Furthermore, the development of benchmarks and platforms for evaluating the ability of AI agents to conduct innovative research is facilitating the advancement of the field.

Some noteworthy papers in this area include: The paper on InnovatorBench, which introduces a benchmark-platform pair for realistic, end-to-end assessment of agents performing Large Language Model research, demonstrating the potential of AI agents to accelerate scientific discovery. The paper on Deep Ideation, which proposes a framework for generating novel research ideas by leveraging large-scale scientific literature and a scientific concept network, showing improved quality of generated ideas compared to other methods.