Unleashing Molecular Potential: A Process Discovery and Automation Workflow for Generative AI in Accelerating Drug Discovery

Saranya Balaguru; Alekhya Gandra1

1

Publication Date: 2024/11/30

Abstract: Advancements in generative artificial intelligence (AI) are reshaping the drug discovery landscape by introducing automated, data-driven workflows that significantly reduce development time and cost. This paper explores a process discovery and automation workflow tailored to generative AI applications in drug discovery, covering the key stages from data ingestion and preprocessing to molecule generation, validation, and optimization [1]. Through the lens of process discovery, we identify critical bottlenecks and opportunities for automation within traditional drug discovery workflows, demonstrating how generative AI, particularly models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), can efficiently generate diverse molecular candidates. Each stage of the workflow integrates automation to streamline high-throughput virtual screening, optimize lead compounds, and enhance predictive accuracy for pharmacological properties such as bioavailability, efficacy, and safety. By embedding automation into these processes, generative AI accelerates not only the generation of candidate compounds but also their assessment against complex biological criteria. The paper further addresses challenges in data quality, interpretability, and regulatory compliance while showcasing real-world case studies where AI-driven process automation led to breakthrough therapeutic discoveries. This structured workflow offers a blueprint for researchers and industry professionals seeking to leverage process automation and generative AI to drive innovation, efficiency, and scalability in drug discovery [1].

Keywords: Generative AI, Drug Discovery, Process improvement, Healthcare, Automation.

DOI: https://doi.org/10.38124/ijisrt/IJISRT24NOV958

PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24NOV958.pdf

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