Drug-Likeness, Druggability, and Molecular Developability via Chemometric Evaluation: A Review

Abstract High attrition rates constitute a major bottleneck in small-molecule drug discovery, largely arising from poor molecular developability rather than an insufficient pharmacological activity. Drug-likeness, druggability, and molecular developability represent complementary evaluation concepts that address different aspects of small-molecule drug discovery. Nevertheless, inconsistent terminology, fragmented evaluation frameworks, and inherent drawbacks of traditional empirical workflows impair the reliability and applicability of molecular property assessment. As powerful multivariate analytical tools, chemometric methods overcome these limitations and enable quantitative, systematic, and high-throughput molecular property evaluation. This Review clarifies the definitions and relationships among the three concepts and summarizes their classical evaluation approaches. Following the paradigm shift from empirical rules to chemometric modeling, multiobjective optimization, and large-model prediction, advanced computational pipelines are categorized into direct molecular similarity evaluation and predictive modeling of key molecular properties. We discuss the principles, progress, performance, strengths, and limitations of each technical strategy and highlight their synergistic values in lead discovery, structural modification, multiobjective optimization, and focused library design. Finally, challenges for standardized molecular-evaluation systems are analyzed, and future trends of chemometrics-assisted multiobjective optimization and large-model-driven intelligent molecular profiling are prospected.