Journal of Chemical Information and Modeling· 2026Q1
Pharmacophore-Conditioned Diffusion Model for Ligand-Based De Novo Drug Design
- 3citations
- Q1SCImago
- 2026year
Short summary
PharmaDiff, a new diffusion model, generates 3D molecules that precisely match predefined pharmacophore hypotheses, outperforming existing ligand-based methods.
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Key points
- PharmaDiff is a 3D molecular generation model conditioned on pharmacophore hypotheses.
- It uses a transformer architecture to integrate atom-based 3D pharmacophore representations.
- The model generates 3D molecular graphs that precisely match predefined pharmacophore constraints.
- PharmaDiff outperforms existing ligand-based methods in matching 3D pharmacophores.
- It achieves higher docking scores against proteins without requiring their structures.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract Developing bioactive molecules remains a central, time- and cost-heavy challenge in drug discovery, particularly for novel targets lacking structural or functional data. Pharmacophore modeling presents an alternative for capturing the key features required for molecular bioactivity against a biological target. In this work, we present PharmaDiff, a pharmacophore-conditioned diffusion model for 3D molecular generation. PharmaDiff employs a transformer-based architecture to integrate an atom-based representation of the 3D pharmacophore into the generative process, enabling the precise generation of 3D molecular graphs that align with predefined pharmacophore hypotheses. Through comprehensive testing, PharmaDiff demonstrates superior performance in matching 3D pharmacophore constraints compared to ligand-based drug design methods. Additionally, it achieves higher docking scores across a range of proteins in structure-based drug design, without the need for target protein structures. By integrating pharmacophore modeling with 3D generative techniques, PharmaDiff offers a powerful and flexible framework for rational drug design.
The authors' abstract, as published at the source. Journal of Chemical Information and Modeling, 2026 · DOI ↗
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Field: Computational Theory and Mathematics
Computational Theory and MathematicsComputer Science