
Enveda secures three hundred eleven million dollars to advance nature-derived ai drugs
Enveda has raised a three hundred eleven million dollar Series E funding round, reaching a two billion dollar valuation. The biotech startup leverages artificial intelligence to discover potential medicines from natural sources like plants and microbes.
Published by Jin · 2 min read · 24 SEPT 2026
Enveda has completed a three hundred eleven million dollar Series E financing round, bringing its total valuation to two billion dollars. This new capital doubles the valuation the biotech startup achieved just twelve months prior. The funding round was led by Catalio Capital Management, with additional participation from Iconiq and other investors.
Founded in 2019 by Viswa Colluru, an early employee at Recursion Pharmaceuticals, the startup approaches drug discovery through a distinct lens. Rather than synthesizing potential compounds completely from scratch within a laboratory setting, the company focuses on harnessing powerful medicines that already exist naturally within plants and microbes.
The role of artificial intelligence
Advanced technology serves as the primary accelerator for this natural product research. By utilizing artificial intelligence and specialized techniques, the organization can rapidly analyze complex natural chemical spaces that traditionally took researchers years to map and understand manually.
Moving toward clinical validation
Although artificial intelligence has not yet produced any fully FDA-approved pharmaceutical products, Enveda represents a growing wave of biotechnology enterprises successfully advancing AI-discovered candidates into human clinical trials.
The startup currently has several drug candidates undergoing patient testing. These active trials include treatments targeted at severe skin conditions as well as a specialized compound designed to help maintain weight loss after patients stop taking GLP-1 medications. As these trials progress, they will offer further insight into the viability of integrating machine learning with natural product pharmacology.
Source — Original announcement ↗
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