Dr. Iain Yisu Wang is a UK-based Product Manager at MedChemExpress, specialising in small-molecule reagents and screening platforms. He leads new product development, market and competitor analysis and technical product support.
A key concern in early-stage drug screening is the balance between biological relevance and experimental throughput. Most primary assays are simplified representations of disease biology, which often limits translational value and reduces the likelihood that hits will progress successfully. To address this gap, there is growing emphasis on the use of more physiologically relevant human-based systems, such as three-dimensional cultures, organoids and organ-on-chip platforms.
Another critical issue involves false positives and assay artifacts, which can inflate hit lists and divert resources toward compounds with little true therapeutic potential. Advances in cheminformatics filters and the use of orthogonal biophysical confirmation methods have helped to mitigate these issues, but such artifacts cannot be fully eliminated.
Library quality and chemical space coverage also remain limiting factors. Libraries that are biased toward reactive, insoluble or highly lipophilic scaffolds reduce discovery efficiency, while redundant chemotypes offer limited novelty. This is where a strategically designed compound library adds significant value. For example, the MCE 50K Diversity Library contains a vast collection of small molecules with high drug-likeness. Its design emphasizes structural novelty and diversity to explore broad chemical space. Additionally, our broad range of target-focused libraries (e.g., kinase, epigenetic, GPCR libraries) enable efficient screening against well-validated target families. Each compound in our libraries is rigorously validated for high purity and comes with detailed mechanistic annotation, which streamlines hit triage.
To counter the limitations of libraries, there is also increasing adoption of chemically diverse and rule-informed collections, fragment-based sets, covalent libraries and DNA-encoded libraries. Each approach carries its own challenges, but the growing application of ML to "denoise" selections is improving the utility of these resources.
Data quality, reproducibility and triage pose additional challenges. HTS generates large, noisy and assay-specific datasets, making hit ranking and prioritization heavily dependent on plate-level quality control, statistical rigor and confirmatory cascades. Although advances in analytics and the adoption of FAIR (findable, accessible, interoperable, reusable) data principles are improving transparency and reusability, variability across laboratories and platforms remains a significant barrier.
Economic and operational constraints further complicate screening campaigns. Despite high throughput, overall efficiency is often limited by campaign design, compound logistics and secondary follow-up assays. To overcome these bottlenecks, laboratories are increasingly employing robotics, acoustic dispensing, assay miniaturization and integrated "self-driving" workflows. Nevertheless, the rising costs of R&D continue to exert pressure on innovation.
AI and ML are being deployed across nearly every stage of the screening process, and while powerful, they remain constrained by incomplete training data, label noise, poor model interpretability and the difficulty of integration with wet-lab experimentation. Regulatory agencies are beginning to define frameworks and guardrails to manage these risks, but ensuring reliability and reproducibility remains a work in progress.