Constructing and characterizing trillion-scale combinatorial chemical library.

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Synthetic Chemistry

Background:

• Drug discovery relies on screening vast chemical spaces for potential drug candidates.
• Exploring ultra-large chemical spaces efficiently is a major challenge in hit identification.
• Current methods often struggle with the scale and diversity required for novel drug development.

Purpose of the Study:

• To construct a novel, trillion-scale combinatorial chemical library for drug discovery.
• To characterize the diversity, novelty, and properties of this large chemical space.
• To develop a resource-optimized synthesis strategy for generating molecules with desired properties.

Main Methods:

• Integration of a curated reaction set with over 1.8 million building blocks to generate a trillion-molecule chemical space.
• Random sampling of the library to assess structural diversity, novelty, and physicochemical properties.
• Application of a Quadratic Unconstrained Binary Optimization (QUBO) model for resource-efficient chemical synthesis.

Main Results:

• The library exhibits broad physicochemical diversity and significant scaffold novelty, extending beyond known natural products and FDA-approved drugs.
• Scaffold retrieval analyses confirm near-ideal structural diversity at scale.
• The QUBO-based strategy effectively maximizes building block utilization for synthesizing molecules with drug-like and other desired properties.

Conclusions:

• A synthetically accessible, trillion-scale combinatorial chemical library provides a scalable resource for ultra-large virtual screening.
• The developed QUBO-based synthesis strategy enables efficient generation of diverse molecules with targeted properties.
• This integrated framework enhances hit identification and downstream optimization in drug discovery by bridging large-scale exploration with practical synthesis.