What Works, What Doesn't, and Why? An Industrial Perspective on Absorption Modeling

Lead optimization failures are often linked to poor absorption, compounded by efflux transport and low recovery. We report a comprehensive modeling of public and industrial data on organic molecules' absorption. Comparative analysis of an industrial chemical space examined the relationship among key permeability parameters, revealing misconceptions in transport route characterization. We demonstrated the importance of recovery, distribution coefficient, and topological polar surface area in multiparameter optimization (MPO). A multitask learning approach was used for model development. Models built on public data were validated on industrial data, revealing discrepancies driven by variations in the protocols. Our results highlight the value of industrial data to avoid applicability domain issues and ensure standardized measurements. Finally, coupling predictive models with generative topographic mapping offers a visual strategy to explore chemical space and understand optimization challenges. This work proposes an approach to support MPO and improve drug discovery efficiency.