In innovative drug discovery, identifying compounds for "difficult-to-drug" targets remains an ongoing challenge. Recently, Roche Group published a study in ACS Medicinal Chemistry Letters that systematically analyzed 21 projects targeting such difficult-to-drug targets completed within a three-year period. The study evaluated the effectiveness of various hit-finding strategies, with particular emphasis on the role of DNA-encoded library (DEL) technology, providing the industry with a reference framework based on real-world project data.

Key Finding 1: Correlation Between Target Classification and Project Success Rates
The study categorizes difficult-to-drug targets into two classes:

• Difficult-to-Drug (D2D) Targets: Targets with known or identifiable binding pockets, where it remains unclear whether binding will lead to the desired functional modulation (e.g., allosteric modulation, inhibition of protein-protein interactions).
• Difficult-to-Ligand (D2L) Targets: Targets lacking reliable structural information or assessed computationally as having no clearly druggable pocket (D-score < 0.7).

Figure 1. A diverse set of 21 difficult-to-drug targets and modes of action were analyzed.

Data analysis indicates that D2D targets show higher project advancement success rates. Among 7 D2D projects, 86% successfully yielded advanceable lead series, compared to 50% among 14 D2L projects. This suggests that target classification at the project outset can help predict technical success and guide resource allocation.

Key Finding 2: Performance Comparison of Screening Technologies
The study evaluated a total of 70 screening experiments across six common technologies. Figure 2 illustrates the frequency of each method's use across actual projects.

Figure 2. Multiple diverse screening methods were employed for a total of 70 screens on 21 projects. The graphic depicts the number of individual screens, color-coded by method.

In terms of breadth of application (Figure 3), DNA-encoded library technology was used in all 21 projects, high-throughput screening in 76% of projects, fragment screening in approximately half, while other methods were used less frequently.

Figure 3. Different hit finding methods were used with different frequency across the research units. Depicted is the percentage of projects for which a specific screen was used, e.g. a fragment screen was used on almost 50% of all projects.

Significant differences were observed in the performance of each method across two critical stages: "producing validated hits" and "conversion to lead series" (Figure 4 and table below):

Figure 4. Screening methods varied in their ability to produce validated hits and lead series. Plotted is the percentage of screens for each method that delivered validated hits (left columns) and lead series (right columns).

Screening Method	Usage Frequency	Success Rate (Validated Hits)	Success Rate (Lead Series)
DNA-encoded library	100%	62%	38%
High-throughput screen	76%	50%	50%
Covalent screens	43%	44%	50%
Peptide screens	43%	>60%	50%
Fragment-based screens	48%	40%	<10%
Virtual	24%	>60%	<10%

Data analysis reveals:

• DEL is widely applicable and demonstrates predictive value. This technology was used in all projects, achieved the highest success rate in yielding validated hits, and showed high positive predictive value (92%). This indicates that DEL can not only be used for hit identification but also serves as a reference tool for assessing target ligandability and guiding subsequent experimental strategies.
• High-throughput, covalent, and peptide screening show consistent performance in applicable contexts. These three methods each achieved a 50% success rate in advancing to lead series in applicable projects, demonstrating good reproducibility. They respectively offer advantages in functional activity screening, high-affinity binding, and large interface coverage.
• Fragment and virtual screening face challenges in the optimization phase. Although both performed reasonably well in initial hit identification, their success rates in optimizing hits into drug-like, advanceable lead series were low (<10%), suggesting they are more suitable for early exploratory phases.

Key Finding 3: Strategic Choices for Integrated Screening
The study identifies two common strategic approaches:

• "Broad Platform" Parallel Approach: Employing multiple screening technologies simultaneously to expand coverage of chemical space.
• "Focused" Sequential Approach: Concentrating resources on a limited combination of high-potential methods (e.g., DEL combined with HTS).

Data suggest that using more screening methods is not necessarily better. If three or more consecutive different screens on the same target fail to yield advanceable compounds, the likelihood of subsequent success may decrease significantly. Therefore, dynamically adjusting strategy based on target characteristics and early screening results can enhance R&D efficiency.

Conclusion
Roche's study not only provides strong evidence for the effectiveness of DEL technology but, more importantly, establishes an actionable decision-making framework. In an environment of rising drug development costs and increasing target difficulty, "informed integrated hit discovery" represents a strategic shift from experience-driven to data-driven thinking.

Looking ahead, with advancements in DEL technology itself (e.g., integrating machine learning for hit expansion), wider adoption of AI-based structure prediction tools, and the emergence of novel screening paradigms, we can anticipate the development of more intelligent and efficient integrated discovery platforms. These advancements hold the potential to transform more previously "undruggable" targets into revolutionary therapies for patients.

1. Gampe, C. M.; Worsdorfer, B.; Zou, G.; Ricci, A. Analyses of Recent Hit-Finding Campaigns for Difficult Targets Provides Guidance for Informed Integrated Hit Discovery. ACS Med. Chem. Lett. 2026. https://doi.org/10.1021/acsmedchemlett.5c00676