Molecule Moiety Optimization Using Pharmacophore-Guided Building Blocks

The patent segments the molecule into multiple moieties (e.g., head, body, tail groups) that can be independently optimized. This segmentation allows the optimization algorithm to work with smaller, more manageable units while still achieving global optimization of the complete molecule. Each moiety can be optimized for specific pharmacological properties, and the combination of optimized moieties yields a globally optimal molecule.

The patent introduces a new dimension to the optimization problem by incorporating pharmacophore matching as an additional optimization criterion beyond local chemical space exploitation. This multi-dimensional approach combines local structural optimization with global pharmacological property optimization, enabling the system to find molecules that satisfy both computational efficiency and global optimality requirements.

The patent implements self-service optimization through automated algorithms that perform moiety decomposition and pharmacophore matching without human intervention. The system automatically identifies optimal moieties, generates candidate structures, evaluates pharmacological properties, and iterates to find optimized molecules. This automation maintains the high optimization quality previously requiring expert manual work while dramatically reducing the time required.

The patent replaces the mechanical process of manual molecular optimization with an automated computational system. Instead of relying on human experts to manually manipulate molecular structures, the system uses algorithms to automatically perform decomposition, generation, evaluation, and optimization steps, substituting human intellectual labor with automated computational processes.

The patent extracts the essential pharmacological requirements from multiple reference molecules into a simplified pharmacophore model. Instead of using all details from multiple complex molecules, the system identifies and extracts key pharmacophoric features (essential structural and spatial requirements) that capture the necessary pharmacological properties. This extraction process reduces computational complexity while maintaining comprehensive pharmacological coverage.

The patent performs preliminary pharmacophore determination from reference molecules before the actual ligand matching process. By pre-processing the reference molecules to extract pharmacophoric features in advance, the system prepares optimized search criteria that can be efficiently applied during ligand matching. This preliminary action separates the complex analysis phase from the matching phase, reducing overall computational complexity.

The patent applies local quality optimization by focusing the search on specific regions of chemical space that are most likely to yield improved pharmacological properties. Instead of uniformly exploring all possible chemical space, the system identifies and concentrates computational resources on promising local regions based on pharmacophore constraints and moiety optimization, achieving better pharmacological results with reduced search time.

The patent performs preliminary filtering and constraint application before extensive chemical space exploration. By pre-establishing pharmacophore requirements, moiety constraints, and optimization criteria, the system narrows down the effective search space in advance. This preliminary action eliminates obviously unpromising candidates early, allowing the subsequent extensive search to focus only on potentially optimal regions, thereby reducing overall search time.