From Hits to Leads: Improving Early Drug Discovery With Better Tools

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.

The integration of HTS and AI is improving precision in early-stage drug discovery. HTS has long provided the ability to test millions of compounds in a short time frame; however, the approach is often associated with noisy datasets and high false-positive rates. By contrast, AI enhances data quality by recognizing assay-specific artifacts, identifying frequent hitters and reprioritizing compounds for validation. This combination results in smaller and more reliable hit lists, thereby reducing downstream attrition and accelerating progression into lead optimization.

While traditional HTS efforts are constrained by the diversity and physical availability of chemical collections, AI-driven virtual screening explores vast in silico chemical spaces, predicting structures with a higher likelihood of binding to relevant targets and guiding the enrichment of physical libraries. The combination of virtual prescreens with targeted HTS validation has given rise to hybrid campaigns that expand chemical space coverage while simultaneously reducing both cost and time.

AI-enabled systems can also dynamically adjust compound selection, assay conditions and follow-up testing in real time. In the context of phenotypic screening, AI also addresses a major challenge: the identification of molecular targets underlying bioactive compounds. By integrating phenotypic signatures with omics data and chemical descriptors, AI can accelerate the transition from phenotype-based observations to validated, tractable therapeutic targets.

Another critical advance lies in the integration of early absorbed, distributed, metabolized and eliminate (ADME)/Tox considerations. Many promising HTS hits ultimately fail due to poor solubility, permeability or adverse toxicity profiles. Incorporating AI-driven predictive models into the screening workflow enables the simultaneous assessment of pharmacokinetic and safety liabilities, allowing for more effective triage of compounds prior to costly in vivo studies. This early filtering reduces late-stage failures and improves the efficiency of the hit-to-lead process.

We have a dedicated team that continuously tracks newly reported molecules and collects multidimensional information on them, including activity data, structural information and other relevant chemical and biological information. These molecules are then annotated with different tags, allowing them to be organized into specific compound libraries according to defined rules. This process steadily expands the size of our libraries and enhances the novelty of our products. In addition, we closely monitor emerging research directions and establish corresponding compound libraries to meet diverse screening needs. With regard to quality control, we perform rigorous testing on all products, assessing their stability and solubility, and exclude compounds with low purity, poor solution stability or poor solubility. This ensures the overall quality and reliability of our compound libraries.

For different research projects, researchers often have diverse screening needs. Although we currently offer more than 200 distinct libraries, it is not possible to meet all requirements with existing collections. Therefore, we provide flexible customized library services, allowing researchers to freely select different products or request specific formatting, layout or packaging options to suit their scientific needs. Importantly, the pricing follows the same rules as our listed libraries, meaning that researchers do not have to pay any additional customization fees when purchasing a customized library.

Phenotypic screening has experienced a notable resurgence over the past decade, driven by advances in cellular models, imaging technologies and data analytics. It now plays a complementary role alongside target-based approaches in drug discovery, offering unique strengths in areas where target-centric methods are limited.

Phenotypic screening has been greatly enhanced by high-content imaging and multiparametric readouts. This expansion has facilitated applications in oncology, neurodegeneration, immunology and rare disease research. Likewise, platforms such as organoids and organ-on-chip systems provide physiologically relevant three-dimensional models and are proving particularly valuable in oncology and central nervous system drug discovery, where traditional two-dimensional assays often fail to capture disease complexity.

Phenotypic screening is also widely used in drug repurposing and indication expansion, as the direct observation of biological effects allows approved or investigational drugs to be screened for new therapeutic uses without requiring prior mechanistic knowledge. Using the MCE FDA-Approved Drug Library for phenotypic screening can save the cost of drug research and reduce the risk of research and development failure caused by drug safety.

Phenotypic screening also remains an important approach for identifying chemical probes and tool compounds, particularly in pathways where validated targets are absent or poorly characterized.

Compared with target-based screening, phenotypic methods continue to offer several distinct advantages:

1. They enable unbiased discovery, allowing compound activity to reveal novel biology and uncover first-in-class mechanisms.
2. Phenotypic assays are especially valuable in complex, multigenic diseases such as cancer, fibrosis or neurodegeneration, where disease mechanisms involve networks of pathways rather than single targets. By capturing integrated cellular outcomes, phenotypic methods reveal therapeutic effects that reductionist assays may miss.
3. Phenotypic approaches often provide greater translational relevance, particularly when conducted in patient-derived cells, organoids or primary tissues, thereby increasing the likelihood of clinical success. Furthermore, they inherently accommodate polypharmacology, an attribute of many successful drugs that act on multiple targets but may be overlooked in target-based screens.
4. Because phenotypic assays measure functional outcomes, they integrate multiple dimensions of compound behavior – such as potency, permeability, stability and even preliminary safety signals – helping to deprioritize molecules that may appear promising in biochemical assays but fail in cellular systems.

Rather than serving as an alternative to target-based screening, phenotypic screening is increasingly recognized as a complementary strategy.