Currently submitted to: JMIR Dermatology
Date Submitted: Apr 21, 2026
Open Peer Review Period: May 1, 2026 - Jun 26, 2026
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Prompt-Driven Drug Discovery of Centella asiatica for Leprosy: Bridging Generative AI and Dermatological Therapeutics
ABSTRACT
Background:
Leprosy remains a significant global health challenge, further complicated by increasing antibiotic resistance in Mycobacterium leprae. Traditional medicinal plants offer promising sources of novel anti-leprotic compounds, especially in regions with limited access to advanced drug discovery infrastructure.
Objective:
This study aimed to investigate Centella asiatica, a traditional Indonesian medicinal plant, as a potential source of new anti-leprosy agents using an AI-driven molecular docking approach.
Methods:
A five-step methodology was employed, beginning with an ethnomedicine review to identify relevant bioactive compounds. Target proteins and ligands were selected from the RCSB Protein Data Bank and PubChem database. Pharmacological activity screening was conducted using PASS Online to predict the potential anti-leprotic effects. The MARA AI platform facilitated prompt-driven protein preparation and cloud-based molecular docking against the 2NTV target protein. The pharmacokinetic properties of the compounds were assessed using SwissADME to evaluate drug likeness and absorption potential.
Results:
Flavonoids Quercetin, Kaempferol, and Apigenin demonstrated strong binding affinities ranging from -9.04 to -9.32 kcal/mol against the 2NTV protein and complied with Lipinski’s Rule of Five, indicating favorable pharmacokinetic profiles suitable for oral administration. Rutin exhibited the highest binding affinity(-11.21 kcal/mol); however, it violated key pharmacokinetic parameters, suggesting its suitability for topical rather than systemic use.
Conclusions:
The prompt-driven platform provides an efficient, accessible, and scalable workflow for in silico screening of anti-leprotic compounds without requiring high-performance computing resources. While the docking results are promising, further validation through Molecular Dynamics simulations is necessary to confirm compound stability and efficacy. This AI-assisted approach supports accelerated drug discovery efforts in resource-limited endemic regions and aligns with the World Health Organization’s “Zero Leprosy 2030” initiative by facilitating the identification of novel therapeutic candidates. Clinical Trial: • Status: Not Applicable. • Reason: This study is an in silico computational simulation/molecular docking study and does not involve human subjects or clinical interventions.
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