Combined Chemoinformatics Approach to Solvent Library Design Using clusterSim and Multidimensional Scaling.

Area of Science:

• * Pharmaceutical sciences
• * Materials science
• * Physical chemistry

Background:

• * Solvent selection is critical for physical form screening in drug development.
• * Traditional solvent selection methods can be inefficient and lack a systematic basis.
• * Chemoinformatics offers novel tools for analyzing and predicting solvent behavior.

Purpose of the Study:

• * To develop a rational, data-driven approach for solvent selection in physical form screening.
• * To assess the effectiveness of various clustering methods for solvent classification.
• * To create a visual tool (solvent map) for aiding solvent selection.

Main Methods:

• * Chemoinformatics analysis of 94 solvents using calculated molecular descriptors.
• * Application and evaluation of eight different clustering methods via the clusterSim package in R.
• * Utilization of multidimensional scaling for visualizing cluster analysis results.

Main Results:

• * Identified optimal clustering methods for solvent classification based on statistical measures.
• * Demonstrated increased efficiency in solid form discovery through a cluster-based solvent selection strategy.
• * Developed a two-dimensional solvent map illustrating solvent properties and relationships.

Conclusions:

• * The chemoinformatics approach provides a rational basis for solvent selection in physical form screening.
• * The solvent map is a valuable tool for efficient solvent choice in solid form discovery.
• * This methodology is applicable to diverse scientific fields requiring solubility-dependent solvent decisions.