AI-Driven Robotic Laboratory Automation for High-Throughput Screening and Automated Synthesis in Drug Discovery: Technologies, Applications, and Future Perspectives
Aadarsh Kumar, Md Moidul Islam, Abhishek Kumar, Ashok Kumar, Moidul Islam Judder, Himanshu Jain, MD Nasiruddin KhanIntroduction:
Drug discovery is widely known to be an extremely complex and costly process due to its cost framework, long cycles, and high turnover rates. Robotics and Artificial Intelligence (AI) introduce the world to a disruptive, mechanized, and data analytics paradigm that can accelerate the initial stages of drug development.
Materials and Methods:
accelerate the initial stages of drug development. Materials and Methods
Results:
The results show that AI and robotics improve assay reproducibility, optimize chemical reactions, and enable the conversion of a hit into a lead. Automated procedures lead to greater data reliability, less experimental time and cost, and higher success rates. Opening new chemical and biological spaces that were previously unexplored, faster discovery timelines, and an even more efficient allocation of resources are demonstrated in both industrial and academic examples.
Discussion:
The collaboration of robotics, AIs, and human expertise creates a hybrid workflow, where routine work is automated, freeing investigators to focus on creative and decision-making work. The ongoing barriers include technical constraints, model interpretation issues, financial constraints, legal compliance and ethical considerations, and personnel adjustments.
Conclusion:
The field of drug discovery is being radically transformed by robotics and AI, allowing scalable, efficient and innovative approaches. A trend towards fully autonomous, self-learning laboratories, including generative AI, quantum computing and digital biology, is anticipated, likely to develop new therapeutics faster, more accurately and at significantly lower cost.