Development of a Computational Pipeline for Automated Pharmacophore Model Optimization

Objective
Pharmacophore modeling and virtual screening are essential techniques in modern drug discovery, enabling the identification of promising therapeutic candidates. However, the manual optimization of pharmacophore models remains a time-intensive and often inefficient task. This project aims to develop algorithmic solutions that automate the optimization process, enabling the computational identification of novel drug candidates.

Methods
To facilitate automated pharmacophore model optimization, we have developed a custom workflow that emulates the manual refinement process typically carried out by researchers. Implemented in Python as a command-line tool, this workflow interfaces directly with LigandScout¹, a widely used platform for pharmacophore modeling and virtual screening, requiring no manual input between steps. The approach is designed to replicate expert decision-making, ensuring consistency and reliability throughout the optimization process.

Results
The project delivers a fully automated optimization pipeline for pharmacophore models, removing the need for manual adjustments and significantly reducing researcher workload. By closely replicating expert-driven optimization strategies, the tool maintains high model quality while improving overall efficiency. Future enhancements, including the integration of heuristic methods, are expected to further accelerate the optimization process and expand the tool’s applicability to large-scale compound libraries, supporting faster and more resource-efficient drug discovery efforts.