How Direct-to-Biology Approaches Help to De-Risk Drug Discovery

Drug discovery teams are pursuing more challenging targets and disease mechanisms. Decisions made during the early stages of hit-to-lead (HtL) can determine where teams commit substantial time and investment later in discovery.

Conventional medicinal chemistry workflows require the purification of newly synthesized compounds before biological evaluation. Although essential in many contexts, this process can restrict the chemical space teams can explore by limiting how many compounds they can assess within available resources, timelines, and budgets.

Direct-to-Biology (D2B) approaches can reduce this bottleneck by allowing crude reaction mixtures to progress directly into biological testing. By shortening the interval between synthesis and biological feedback, D2B enables discovery teams to evaluate a broader range of chemical matter and build a stronger evidence base before selecting the series that will progress.

This approach can help reduce the risk of committing significant resources to suboptimal chemical matter.

Generating high-quality data faster

Drug discovery teams are increasingly expected to deliver high-quality candidates while managing considerable scientific and financial risk. As focus moves into more complex disease biology and harder-to-address targets, the time and money spent pursuing an unproductive chemical series can substantially affect the chances of project success.

This puts greater emphasis on the quality of decisions made during early HtL, when teams decide which starting points justify further investment. Traditional medicinal chemistry workflows limit how broadly teams explore those starting points.

Teams usually purify and analyze newly synthesized compounds before biological testing to ensure that the specific molecule tested, rather than an impurity or side product, causes the observed biological activity. However, in circumstances in which hundreds or thousands of analogs are being generated, purification can become a significant bottleneck between synthesis and biological analysis.

When crude reaction mixtures progress directly from microscale synthesis plates into suitable biological or biophysical assays, D2B can shorten the synthesis-to-feedback cycle. Crude reactions naturally contain the components needed to build the molecule, along with the target compound.

Target assays must be validated with these additional components in mind. Pretesting likely reaction constituents against the target and assay system can determine whether they interfere with the readout and which chemistry is compatible with a D2B workflow.

Well-designed controls are also important because they help distinguish genuine activity from artifacts caused by other components in the reaction mixture. Knowledge of assay sensitivity and reaction conversion also informs how to interpret the results.

Careful assay development and validation therefore underpin the reliability of the biological data used to prioritize compounds.

Parallel profiling of structure and pharmacology early

Broader profiling of crude reaction mixtures can provide crucial data when comparing chemical series, helping to build a more complete picture earlier in the program. Key considerations include:

• Pharmacokinetic insight: Teams can generate preliminary metabolic stability data alongside primary biological data, giving earlier visibility into weaknesses in chemical series than might otherwise emerge later with traditional workflows.
• Physicochemical insight: Early assessment of properties such as polar surface area and logD provides additional context alongside biological activity, helping teams understand compound behavior and support multiparameter optimization earlier in discovery.
• Target engagement insight: Ligand-protein crystal structures can show in more detail how chemical modifications alter target engagement, helping teams interpret why certain compounds perform differently and guiding subsequent design.

Bringing data together provides a deeper understanding of each ligand before teams concentrate resources on one or two series, allowing progression decisions to consider a broader range of biological and developability-related characteristics.

Reducing operational silos to de-risk pipelines and fuel artificial intelligence (AI) discovery

The rapid data generation enabled by D2B is most useful when relevant disciplines are closely integrated. Removing traditional silos lets teams respond quickly to emerging results and adapt strategy before committing significant resources.

Key elements of this connected workflow include:

• Coordinated validation workflows: Early and continued communication allows teams to align reaction chemistry with assay compatibility, build appropriate controls into the program, and ensure biological testing is ready when compound arrays are generated.
• Project-relevant data for AI models: Integrated D2B workflows can generate data sets to power machine-learning models and shorten design-make-test-analyze cycles. Training models on project-specific experimental data can also improve the relevance of subsequent hypothesis and compound generation.

Reducing operational silos in this way can improve both near-term compound prioritization and the quality of the data used to guide future design decisions.

Improving early lead selection for complex disease targets with D2B

The complexity of disease biology and the growing focus on challenging therapeutic targets can complicate drug discovery. Targets such as transcription factors and intrinsically disordered proteins can be particularly difficult to address using conventional discovery strategies, increasing the need to explore diverse chemical starting points and generate reliable biological data early in the program.

By removing the requirement for purification between synthesis and testing, D2B can enable earlier generation of high-quality data. Depending on the scientific question, affinity-based, biochemical, and cell-based assays each play a role, provided the assay is compatible with the chemistry and appropriately validated for crude reaction mixtures.

This flexibility allows D2B to support both the identification of novel chemical starting points and the investigation of focused chemistry questions within established discovery programs. By broadening the evidence available around compound behavior before resources are concentrated on fewer options, D2B can help teams decide which compounds and lead series warrant further investment.

It can also help identify promising directions for subsequent medicinal chemistry. As compatible assays and early profiling endpoints expand, D2B can provide medicinal chemistry teams with additional opportunities to evaluate chemical matter against multiple parameters earlier in discovery.

This can help focus subsequent optimization efforts on the most promising lead series, supporting a more efficient path toward candidate selection.

About the Authors

Euan Fordyce is associate director of chemistry for Sygnature Discovery.

Alexandra Stowell is senior principal scientist at Sygnature Discovery.