Pharmacophore-Conditioned Diffusion Model for Ligand-Based De Novo Drug Design (2505.10545v1)

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 paper introduces PharmaDiff, a pharmacophore-conditioned diffusion model using a transformer architecture and novel featurization, inpainting, and attention mechanisms for 3D molecular graph generation.
• Performance evaluation shows PharmaDiff excels in generating pharmacophore-compliant molecules with higher match scores and perfect match rates, and yields better docking scores in structure-based design experiments.
• PharmaDiff offers a robust framework for rational drug design, particularly for targets without structural data, accelerating the discovery of novel therapeutics with desired properties.

Overview of "Pharmacophore-Conditioned Diffusion Model for Ligand-Based De Novo Drug Design"

The paper "Pharmacophore-Conditioned Diffusion Model for Ligand-Based De Novo Drug Design" introduces PharmaDiff, an advanced diffusion model built upon a transformer-based architecture. PharmaDiff leverages pharmacophore conditioning for the generative process of 3D molecular graphs. This approach enables precise alignment with predefined pharmacophore hypotheses, highlighting its potential utility in rational drug design.

Pharmacophore models serve as a pivotal technique in Computer-Aided Drug Discovery (CADD). They encompass key steric and electronic features necessary for molecular bioactivity against a given target. PharmaDiff progresses beyond conventional virtual screening methodologies by integrating pharmacophore data directly into the generative model, thus bypassing the need for target protein structure data. As such, it represents a significant advancement in the creation of bioactive molecules where traditional target structural information is lacking.

Methodology

PharmaDiff builds on the MiDi architecture, extending it with pharmacophore-conditioning capabilities. The model introduces an atom-based representation of pharmacophores into the generative process. This is achieved through several pivotal strategies:

1. Featurization of Pharmacophores: The model employs a novel featurization approach where pharmacophoric features and corresponding atomic information are embedded in a sub-molecular graph.
2. Inpainting: This technique is utilized to insert pharmacophore-associated atoms into the noisy input during diffusion, maintaining consistency with the pharmacophore constraints.
3. Center of Mass Adjustment: Ensures that the E(3)-equivariance is preserved by adjusting the center of mass during the inpainting process.
4. Cross-Attention Mechanism: Enables dynamic integration of pharmacophoric information into molecular representation through a cross-attention layer, allowing contextual influence on generated atoms.
5. Loss Function: Emphasizes the accurate prediction of pharmacophoric features alongside general molecular properties, ensuring generated molecules adhere closely to pharmacophore constraints.

Performance Evaluation

The experiments demonstrate PharmaDiff’s superior capability in generating pharmacophore-compliant molecules compared to existing ligand-based and 3D structure-based models. PharmaDiff achieves a higher mean Match Score (MS), with a noted improvement in Perfect Match Rate (PMR), indicating its efficacy in generating molecules that more closely align with specific pharmacophore profiles.

In structure-based drug design experiments, PharmaDiff generates molecules with higher docking scores against protein targets such as BRD4 and VEGFR2, showcasing its potential to produce drug-like molecules with optimized binding affinities. Moreover, it maintains favorable synthetic accessibility and chemical diversity, further underlying its suitability for practical drug discovery applications.

Implications and Future Directions

The integration of pharmacophore conditioning in PharmaDiff provides a robust framework for rational drug design, especially for targets without available structural data. This presents clear implications for accelerating the discovery of novel therapeutics across a variety of biochemical targets. The ability to generate 3D molecular structures that meet specific pharmacophoric constraints offers a substantial advantage in designing bioactive drugs with desired biological properties.

PharmaDiff's architecture provides a fruitful avenue for further methodological enhancements, such as incorporating more sophisticated feature-aware models that can explicitly reinforce pharmacophoric features throughout the generative process. Additionally, the potential exploration of energy-guided sampling techniques or post-process adjustments could overcome challenges related to molecular connectivity and enhance the practicality of generated molecules.

In conclusion, PharmaDiff represents a noteworthy contribution to the field of de novo drug design, demonstrating the potential to improve the efficiency and effectiveness of pharmacophore-informed molecular generation strategies. The research opens new possibilities for sophisticated AI applications in drug design, suggesting a trajectory toward more effective computational tools in medicinal chemistry.