On the order of hundreds of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) models have been described in the literature in the past decade which are more often than not inaccessible to anyone but their authors. Public accessibility is also an issue with computational models for bioactivity, and the ability to share such models still remains a major challenge limiting drug discovery. We describe the creation of a reference implementation of a Bayesian model-building software module, which we have released as an open source component that is now included in the Chemistry Development Kit (CDK) project, as well as implemented in the CDD Vault and in several mobile apps. We use this implementation to build an array of Bayesian models for ADME/Tox, in vitro and in vivo bioactivity, and other physicochemical properties. We show that these models possess cross-validation receiver operator curve values comparable to those generated previously in prior publications using alternative tools. We have now described how the implementation of Bayesian models with FCFP6 descriptors generated in the CDD Vault enables the rapid production of robust machine learning models from public data or the user’s own datasets. The current study sets the stage for generating models in proprietary software (such as CDD) and exporting these models in a format that could be run in open source software using CDK components. This work also demonstrates that we can enable biocomputation across distributed private or public datasets to enhance drug discovery.

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The in vitro permeabilities of Caco-2 monolayers and permeabilities in tissue sections from colon of monkey, rabbit, and dog were compared using a series of compounds. The selected compounds differed in their physicochemical properties, such as octanol/water partition coefficient, water solubility, and molecular weight. Their structure included steroids, carboxylic acids, xanthins, alcohols, and polyethylene glycols. A linear permeability relationship was established between Caco-2 and colon tissue from both rabbit and monkey. The results suggest that Caco-2 is twice as permeable as rabbit and five times as permeable as monkey colon. However, no clear relationship could be established between Caco-2 monolayers and dog colon permeability. A relationship between permeability in Caco-2 monolayers and human absorption was found. The results suggest that within certain limits, permeability of Caco-2 monolayers may be used as a predictive tool to estimate human drug absorption.

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