Maximal use of minimal libraries through the adaptive substituent reordering algorithm.

Area of Science:

• Computational Chemistry
• Materials Science
• Drug Discovery

Background:

• Accurate prediction of molecular properties is crucial for efficient chemical library design and synthesis.
• Existing methods for property interpolation often rely on limited sampling and may lack robustness for complex molecular spaces.

Purpose of the Study:

• To develop and validate an adaptive algorithm for accurate property interpolation over molecular libraries.
• To introduce novel criteria for optimizing substituent orderings and managing library design challenges.

Main Methods:

• An adaptive algorithm utilizing coarse sampling and optimal substituent ordering for property interpolation.
• Implementation of data-driven root-mean-squared (RMS) and combined RMS/smoothness criteria for ordering optimization.
• Error propagation analysis for confidence intervals and substituent rescaling for library management.
• Adaptive methodology for iterative optimization of algorithmic factors using laboratory experiments.

Main Results:

• Demonstrated accurate interpolation of various properties for copolymer and transition metal complex libraries.
• The proposed RMS and combined criteria effectively identify optimal substituent orderings.
• Error propagation and rescaling techniques enhance the reliability and applicability of the algorithm.
• Adaptive experimental design maximizes prediction accuracy.

Conclusions:

• The developed adaptive algorithm significantly improves property interpolation accuracy in molecular libraries.
• The method offers a robust framework for guiding chemical synthesis and property assessment.
• This approach enhances the efficiency of discovering molecules with desired properties.