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This review article discusses the application of artificial intelligence (AI) in natural product (NP) drug discovery, highlighting how machine learning, deep learning, and generative AI can accelerate virtual screening, predict molecular interactions, and design novel NP-inspired scaffolds. The authors suggest AI could reduce drug discovery time by up to 70% and increase success rates from <1% to over 10%. The review also addresses challenges such as limited NP representation in datasets and ethical concerns related to bioprospecting.
AI-driven approaches promise to significantly accelerate and improve the efficiency of natural product drug discovery, potentially leading to novel therapeutics and addressing unmet medical needs.
Natural products (NPs) have long been a cornerstone of pharmaceutical innovation, contributing to approximately 50% of FDA-approved drugs over the past four decades. However, traditional NP drug discovery faces significant hurdles, including laborious isolation processes, biodiversity constraints, and low hit rates in high-throughput screening. These hurdles often extend the development timelines to 10-15 years with costs exceeding $2 billion per drug. Artificial intelligence (AI) emerges as a transformative force, leveraging machine learning (ML), deep learning (DL), and generative models (Gen. AI) to expedite these processes. AI facilitates virtual screening of vast chemical libraries, predicts molecular interactions with unprecedented accuracy, and designs novel NP-inspired scaffolds, potentially reducing discovery time by up to 70%. This interdisciplinary approach not only addresses unmet medical needs but also aligns with global sustainability goals, potentially increasing success rates from <1% in traditional pipelines to over 10%. Ultimately, AI hints at revitalizing NP drug discovery, fostering innovative, eco-friendly therapeutics. This study reviews recent advancements in AI applications for NP drug discovery, including the challenges such as NPs representing only ~5% of screened compounds in many datasets, interpretability issues in "black-box" models, and ethical concerns over bioprospecting in biodiverse regions.
