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Jan 1, 2009

Busting the Black Box Myth: Designing Out Unwanted ADMET Properties with Machine Learning Approaches

Introduction

Drug design is usually understood as “an inventive process of finding new medications based on the knowledge of the biological target” – according to the Wikipedia definition – where “drug” is usually defined as a relatively small organic ligand binding to a biomolecule to either inhibit or promote its activity. [1] Methods of thus-defined drug design are well established in the early stages of drug discovery and focus mainly on identifying new chemical entities (NCEs) – molecules with desired biological activity. Along with activity, however, drugs must possess acceptable ADMET (Absorption, Distribution, Metabolism, Elimination, and Toxicity) properties. Although a few recent trends push ADMET optimization into drug design, these properties are still largely neglected until the lead optimization phase occurring later in drug discovery when the available chemical space is already narrowed by affinity and selectivity concerns. Usually one group of drug discovery researchers hands out lead molecules to a separate group of drug development scientists: “Our job is done, here are the active molecules. Now you worry about their other properties.” This separation could, and often does, lead to frustrating failures where all the lead compounds may, for example, turn out to be insoluble in water, impermeable via the intended route of administration, unacceptably metabolized by enzymes common to co-medications, or produce unacceptable adverse effects. All the effort and cost of discovering them would then be wasted.

By Robert Fraczkiewicz, David Miller, Walt S Woltosz, and Michael B Bolger

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