From Virtual Hits to Real Molecules: How to Address Synthetic Accessibility of the Chemical Spaces

Virtual screening has become one of the most powerful tools in modern drug discovery. With advanced computational methods, researchers can evaluate millions or even billions of molecules in a matter of hours. However, identifying promising hits in silico is only the beginning of the journey.

One of the main difficulties appears when scientists try to translate digital predictions into real chemistry. Many computational hits look promising on paper but turn out to be difficult or impossible to synthesize in practice. Modern synthon-based chemical spaces, such as those described at https://chem-space.com/freedom-space, aim to solve this challenge by designing libraries where compounds are built from validated building blocks and known reaction pathways.

Key chemical spaces’ challenge and how to avoid it

Traditional combinatorial libraries often generate molecules first and consider synthesis later. This approach frequently produces structures that are theoretically interesting but impractical in the laboratory.

One of the most popular approaches how to avoid the problem of synthetic accessibility is application of Machine Learning (ML) based filters for preselection of building blocks.

Machine learning (ML) is now fundamentally changing how chemical spaces are built — and in doing so, is eliminating the synthesizability problem at its source.

The key insight driving the next generation of synthetically accessible chemical spaces is that synthesizability should be built in at the building block level, not filtered out at the product level.

Rather than enumerating a large virtual library first and then applying synthesizability filters to the output, ML-based workflows train predictive models on historical reaction outcome data — tracking which building blocks actually succeeded or failed across thousands of real synthesis attempts — and use those models to curate the input reagent pools before enumeration begins. The result is a chemical space where every virtual compound is grounded in demonstrated chemistry, not just theoretical compatibility.

Expanding chemical space without losing practicality

Synthon-based spaces can represent hundreds of billions of virtual molecules while remaining connected to feasible chemistry – each compound traceable to purchasable starting materials and validated reaction protocols. However, this scale comes with a structural trade-off: the chemical space is inherently shaped by the reactions used to build it. Reaction types like amide coupling, reductive amination, and Suzuki coupling are well-represented; complex, multi-step, or stereocentre-rich chemotypes are not. For drug discovery teams, understanding both the scale and the structural boundaries of a synthon-based space is essential for interpreting virtual screening results accurately.

Toward a more practical model of drug discovery

Drug discovery is increasingly becoming a balance between computational prediction and chemical reality. Large virtual libraries provide immense exploration potential, but only when the compounds they contain can actually be synthesized.

Chemical spaces developed using ML models bridge this gap by aligning virtual screening with real chemistry from the very beginning. By reducing the number of impractical hits and enabling faster synthesis of promising molecules, these libraries help accelerate the path from digital discovery to real therapeutic candidates.