Multimodal AI for Personalized Drug Discovery Through Joint Protein Structure Generation and Compound Interaction Prediction
Keywords:
multimodal artificial intelligence; personalized drug discovery; protein structure generation; compound interaction prediction; biomedical infrastructure; clinical translation; governance; fairness; sustainability; robust machine learningAbstract
Multimodal artificial intelligence is transforming drug discovery from a sequence of disconnected screening tasks into an integrated socio-technical system that couples molecular generation, structural biology, interaction prediction, and clinical decision support. This paper examines the architecture and governance of personalized drug discovery platforms that jointly generate protein structures and predict compound interactions. Such systems combine protein language models, geometric reasoning, generative molecular design, and multimodal representation learning to propose candidate compounds conditioned on patient-specific biological context. The central claim is that joint generation and prediction can improve therapeutic relevance, but only when the platform is designed as a governed infrastructure rather than a single predictive model. We analyze the structural trade-offs among model scale, interpretability, latency, privacy, and clinical accountability. We then discuss data provenance, federated learning, model cards, datasheets, and regulatory evidence generation as necessary complements to algorithmic performance. Particular attention is given to robustness under distribution shift, fairness across ancestral and socioeconomic groups, environmental sustainability of large-scale training and inference, and policy mechanisms for post-market surveillance. The paper argues that personalized drug discovery requires hybrid evaluation that combines computational benchmarks, wet-lab validation, and real-world clinical feedback. It also identifies failure modes that emerge when generative models optimize for plausible structures or molecules without sufficient interaction-level grounding. The conclusion is that multimodal AI can support more precise and equitable therapeutic discovery if architecture, infrastructure, evaluation, and governance co-evolve. The proposed perspective is system-level and forward-looking, emphasizing deployability, accountability, and long-term maintenance over narrow benchmark gains.
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