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Epilepsy is a common disorder of the Central Nervous System (CNS). The rational design of small-molecule drugs for disorders of the CNS is a difficult process because the majority of small molecules are unable to cross the Blood-Brain-Barrier. An efficient method for the design of inhibitors that have high permeability through the Blood-Brain-Barrier has the potential for application in drug design for CNS disorders such as Addiction, Alzheimer’s disease, Bipolar disorder, Depression, Epilepsy, Gliomas, and Tuberculous meningitis.
Supervised learning was used to model the Blood-Brain-Barrier permeability of drugs like small organic molecules. This information was utilized to guide an evolutionary algorithm for the design of inhibitors with increased affinity for the target as well as higher Blood-Brain-Barrier permeability.
The ligands designed with guided evolution were predicted to have higher binding affinity for the target as well as higher permeability across the Blood-Brain-Barrier compared to an evolutionary algorithm without the guidance. The guided evolutionary method was applied to design a set of drug-like ligands that were predicted to bind to GABA-T with high affinity, to be BBB permeable, and to be chemically synthesizable.
Despite the availability of several drugs that are approved for the treatment of epilepsy, there are many cases that do not respond to available drugs or experience adverse effects. The novel ligands designed as part of this work have the potential to address the limitations of available drugs.
Guided evolution is an efficient computational approach for the design of CNS drugs. The de novo design of drugs by application of the guided evolution algorithm, developed as part of this work, has resulted in the generation of ligands that are potential drugs for the cure of epilepsy. However, the effectiveness of these drugs for the cure of epilepsy has to be validated experimentally.
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