book chapter · Advances in computational intelligence and robotics book series
The integration of artificial intelligence (AI) into laboratory automation systems is revolutionizing the drug discovery process by enhancing efficiency and accuracy in experimental phases. By leveraging machine learning algorithms and robotic systems, researchers can achieve higher throughput in compound screening, optimize experimental designs, and reduce human error. A case study was discussed that demonstrated successful applications of AI in laboratory settings, highlighting advancements in high-throughput screening, data analysis, and predictive modeling. Additionally, we address the challenges associated with implementing AI in laboratory automation, including data integration, system interoperability, and the need for skilled personnel.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.4018/979-8-3693-9208-9.ch013
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