Effects of inductive bias on computational evaluations of ligand-based modeling and on drug discovery |
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Authors: | Ann E Cleves Ajay N Jain |
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Institution: | (1) BioPharmics LLC, 36 Avila Road, San Mateo, CA 94402, USA;(2) University of California, San Francisco, Box 0128, San Francisco, CA 94143-0128, USA |
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Abstract: | Inductive bias is the set of assumptions that a person or procedure makes in making a prediction based on data. Different
methods for ligand-based predictive modeling have different inductive biases, with a particularly sharp contrast between 2D
and 3D similarity methods. A unique aspect of ligand design is that the data that exist to test methodology have been largely
man-made, and that this process of design involves prediction. By analyzing the molecular similarities of known drugs, we
show that the inductive bias of the historic drug discovery process has a very strong 2D bias. In studying the performance
of ligand-based modeling methods, it is critical to account for this issue in dataset preparation, use of computational controls,
and in the interpretation of results. We propose specific strategies to explicitly address the problems posed by inductive
bias considerations. |
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Keywords: | Inductive bias Ligand-based modeling Computational evaluation Molecular similarity Surflex-Sim |
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