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Vol. 5 No. 1 (2024)
2024 Edition

Articles

Deep Learning-Based Automated Bug Localization and Analysis in Chip Functional Verification

Published 2024-11-07

  • Jingyi Chen
    • ,
  • Yingqi Zhang

Jingyi Chen
Electrical and Computer Engineering, Carnegie Mellon University, PA, USA

Yingqi Zhang
Computer Science, Carnegie Mellon University, CA, USA

Abstract

Artificial intelligence has emerged as a transformative technology in biopharmaceutical research, fundamentally altering traditional drug discovery approaches. This paper examines the integration of advanced machine learning architectures, data-centric pipelines, and specialized hardware accelerators across the pharmaceutical development landscape. We analyze how predictive models revolutionize target identification and validation through multi-omics data integration, enabling identification of previously undruggable targets with significantly improved validation rates. The application of generative models for de novo drug design demonstrates substantial cycle time reduction, with case studies showing 3-4 fold acceleration in lead optimization campaigns. We evaluate the performance of deep learning architectures for drug-target interaction prediction and ADMET property modeling, quantifying accuracy improvements over traditional computational methods. Systematically addresses the challenges of implementation, including repetitive barriers, hardware safety vulnerability and developing regulatory frameworks, paying special attention to the validation requirements of the jurisdiction areas. The economic effects of finding an AI-accelerated drug extend to the reduction of direct cost, which may be democraticized by reducing obstacles to access and decentralized research properties. As cooperation between technology suppliers and pharmaceutical companies continues to develop into risk-sharing partnerships, AI methods promise to change the therapeutic development in economics, and at the same time extend research ability outside of traditional drug centers.

Keywords

Artificial Intelligence, Drug Discovery, Hardware Acceleration, Biopharmaceuticals

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