ReviewNatural products and bioprospecting2025
Bridging chemical space and biological efficacy: advances and challenges in applying generative models in structural modification of natural products.
Review in Natural products and bioprospecting, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Medicinal Chemistry Perspectives on Drug Repurposing: Innovative Approaches and Therapeutic Breakthrough.Therapeutic innovation & regulatory science · 2026Review
- KAN-PROSPECT: a Kolmogorov-Arnold Networks-integrated framework for predicting the effects and adverse reactions of natural products via transfer learning.Scientific reports · 2026Article
- Artificial Intelligence Virtual Organoids (AIVOs).Bioactive materials · 2026Review
- AI-driven generative framework integrating ML-QSAR and fragment learning for isoform-selective PI3K inhibitor design.Molecular diversity · 2026Article
- AI-Driven Plant-Derived Anti-Infectives: Integrating Traditional Wisdom into Precision Medicine Against AMR.Life (Basel, Switzerland) · 2026Review
- Research and Application of the Polyene Macrolide Antibiotic Nystatin.Molecules (Basel, Switzerland) · 2026Review
- RL-MSVP: Reinforcement Learning and Multi-scale Validation Pipeline Investigating Azole-Fused Pyrimidines as PotentialACS omega · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Natural products (NPs) are invaluable resources for drug discovery, characterized by their intricate scaffolds and diverse bioactivities. AI drug discovery & design (AIDD) has emerged as a transformative approach for the rational structural modification of NPs. This review examines a variety of molecular generation models since 2020, focusing on their potential applications in two primary scenarios of NPs structure modification: modifications when the target is identified and when it remains unidentified. Most of the molecular generative models discussed herein are open-source, and their applicability across different domains and technical feasibility have been evaluated. This evaluation was accomplished by integrating a limited number of research cases and successful practices observed in the molecular optimization of synthetic compounds. Furthermore, the challenges and prospects of employing molecular generation modeling for the structural modification of NPs are discussed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.