Pharmacophore-Guided Generative Design of Novel Drug-Like Molecules
This work addresses the problem of inefficient generative drug design for pharmaceutical researchers, offering a method to accelerate hit-to-lead optimization, though it appears incremental as it builds on existing generative approaches with a specific guidance mechanism.
The paper tackles the computational expense and inaccuracy of docking optimization in generative drug design by introducing a pharmacophore-guided framework that balances similarity to reference compounds with structural diversity, demonstrating its application in generating novel estrogen receptor modulators for breast cancer with high pharmacophoric fidelity and structural novelty.
The integration of artificial intelligence (AI) in early-stage drug discovery offers unprecedented opportunities for exploring chemical space and accelerating hit-to-lead optimization. However, docking optimization in generative approaches is computationally expensive and may lead to inaccurate results. Here, we present a novel generative framework that balances pharmacophore similarity to reference compounds with structural diversity from active molecules. The framework allows users to provide custom reference sets, including FDA-approved drugs or clinical candidates, and guides the \textit{de novo} generation of potential therapeutics. We demonstrate its applicability through a case study targeting estrogen receptor modulators and antagonists for breast cancer. The generated compounds maintain high pharmacophoric fidelity to known active molecules while introducing substantial structural novelty, suggesting strong potential for functional innovation and patentability. Comprehensive evaluation of the generated molecules against common drug-like properties confirms the robustness and pharmaceutical relevance of the approach.