Abstract
The mechanisms behind ADME (absorption, distribution, metabolism, and excretion) related properties and toxicity endpoints are usually complex, and many are not fully understood. As a result, most ADMET predictive models are not based on theoretical principles, but are derived from experimental data. ADMET properties are best analyzed by projecting them onto the compounds of the training set. There are multiple advantages to projecting the ADMET properties from the problem domain to the chemical domain. Projection simplifies the problem, and avoids the entanglement of needing to invoke specific mechanisms. Projection focuses on the most important, and most tractable, aspect of the problem -- the related properties of the compounds themselves. In this review article, the general requirements of the chemical space to be projected are discussed, including the size and diversity of the training set and the accuracy of the biological measurements, and the process is illustrated using an analogue of a real projection. Also, the successes and pitfalls of the projection method in recent ADMET predictions are reviewed.
Keywords: admet prediction, qsar, dataset quality, molecular diversity, logp, solubility, blood-brain barrier, absorption, p-glycoprotein, cytochrome p450
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