2 research outputs found
Soft Computing Tools for Virtual Drug Discovery
In this paper, we describe how several soft computing tools can be used to assist in high
throughput screening of potential drug candidates. Individual small molecules (ligands)
are assessed for their potential to bind to specific proteins (receptors). Committees of
multilayer networks are used to classify protein-ligand complexes as good binders or bad
binders, based on selected chemical descriptors. The novel aspects of this paper include
the use of statistical analyses on the weights of single layer networks to select the appropriate descriptors, the use of Monte Carlo cross-validation to provide confidence measures
of network performance (and also to identify problems in the data), the addition of new
chemical descriptors to improve network accuracy, and the use of Self Organizing Maps
to analyze the performance of the trained network and identify anomalies. We demonstrate the procedures on a large practical data set, and use them to discover a promising
characteristic of the data. We also perform virtual screenings with the trained networks
on a number of benchmark sets and analyze the results
Soft computing tools for virtual drug discovery
In this paper, we describe how several soft computing tools can be used to assist in high
throughput screening of potential drug candidates. Individual small molecules (ligands)
are assessed for their potential to bind to specific proteins (receptors). Committees of
multilayer networks are used to classify protein-ligand complexes as good binders or bad
binders, based on selected chemical descriptors. The novel aspects of this paper include
the use of statistical analyses on the weights of single layer networks to select the appropriate descriptors, the use of Monte Carlo cross-validation to provide confidence measures
of network performance (and also to identify problems in the data), the addition of new
chemical descriptors to improve network accuracy, and the use of Self Organizing Maps
to analyze the performance of the trained network and identify anomalies. We demonstrate the procedures on a large practical data set, and use them to discover a promising
characteristic of the data. We also perform virtual screenings with the trained networks
on a number of benchmark sets and analyze the results