Amplify scientific discovery with artificial intelligence

By Yolanda Gil, Mark T. Greaves, J. Hendler, H. Hirsh. 2014

153 citations recorded by the source corpus.

Subfields: Information Systems and Management, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems and Management

1. Philip S. Yu

In Molecular Biology

1. Olexandr Isayev
2. Mariya Popova
3. Philip S. Yu
4. Alexander Tropsha
5. Lichao Sun
6. Paola Gramatica
7. Antreas Afantitis
8. Georgia Melagraki

Citations in the corpus

• Concrete Problems in AI Safety (2016, neutral): “A I] 2 1 Ju n”
• A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT (2023, background): “On the contrary, AIGC for high-stakes applications, including healthcare [248], financial services [249], autonomous vehicles [250], and science discovery [251], are still challenging.”
• Deep reinforcement learning for de novo drug design (2017, background): “Artificial intelligence (AI) systems can radically transform the practice of scientific discovery(1).”