# Rethinking Nature’s Pharmacy: AI Era and Natural Product Drug Discovery

## TL;DR

AI is transforming natural product drug discovery by speeding up processes and improving success rates, while addressing sustainability and ethical challenges.

## Contribution

This study reviews recent AI advancements in natural product drug discovery, highlighting challenges and opportunities in the field.

## Key findings

- AI can reduce NP drug discovery time by up to 70% through virtual screening and molecular design.
- Success rates in NP drug discovery may increase from <1% to over 10% with AI integration.
- Challenges include limited NP representation in datasets and ethical issues in bioprospecting.

## Abstract

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.