ADME binding affinity prediction

Gunturi, S.B., Narayanan, R. and Khandelwal, A. (2006) In silico ADME modelling 2 Computational models to predict human serum albumin binding affinity using ant colony systems. Bioorganic and Medicinal Chemistry, 14, 4118 129. 

In silico techniques have gained wide acceptance as a tool to support the drug discovery and optimization process. Binding mode predictions via docking, affinity predictions via QSAR and CoMFA, or the prediction of ADME(T) properties are routinely applied [1-3]. 

VolSurfwas initially validated on oral absorption [16, 17] and blood-brain-barrier permeation [18] models (see belovi ). VolSurf has continued to be developed to improve in silico predictions for ADME properties, although its use has also been extended to receptor-based evaluation of binding affinity [19, 20], While other soft-ivare tools for ADME modeling are available (see, e.g., [21]), the MIF-based collection of sofhvare and models available from Molecular Discovery (MD) is both extensive and ivell validated by the private sector. Three programs from MD, VolSurf, MetaSite and Almond, are particularly suited for rapid evaluation of large compound sets [22] in connection ivith ADME/Tox related properties ... 

This section will provide an overview on ADME models from our group to illustrate our approach for building predictive models on structurally diverse training sets. Datasets for intestinal human absorption and human serum albumin binding are discussed, while models for other relevant ADME properties have also been obtained. Those models, however, do not stand alone but are used in combination with those models tailored for affinity and selectivity in the frame of multidimensional lead optimization. 

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