Generative AI for Personalized Drug Discovery and Development
Authors
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Wendy Young
Quantum Solutions Ltd.
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Ben Adams
Crimson Education Trust
https://orcid.org/0009-0000-0000-0000
Abstract
Traditional pharmaceutical research and development is notoriously slow and expensive, with a high failure rate. This research investigates the transformative role of generative AI models, particularly Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), in accelerating personalized drug discovery. We demonstrate how these models can learn from vast libraries of known molecules to explore a multi-dimensional chemical space, designing novel, *de novo* compounds with optimized therapeutic properties. Our framework focuses on generating molecules with high predicted binding affinity to specific protein targets and low off-target toxicity. Furthermore, we integrate patient-specific genetic data into the generative process, guiding the model to create compounds tailored to an individual's unique biomarker profile. This approach to personalized medicine promises to significantly shorten the drug discovery pipeline, reduce R&D costs, and deliver more effective treatments with fewer side effects by moving beyond the one-size-fits-all paradigm.
Copyright (c) 2025 Wendy Young, Ben Adams (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.