Genetic Algorithm-Based Optimization of In Silico prediction Models for Oral Absorption

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In recent years, the rate of drug discovery has been much accelerated by introduction of combinatorial chemistry and high-throughput screening. However, the hit compounds found in such new technologies mostly have serious problems in pharmacokinetics. Therefore, it is now being desired to develop the methods of predicting the pharmacokinetic properties of compounds. The purpose of this investigation was to establish a prediction method for predicting oral absorption of drugs, considering that a majority of drugs are in oral dosage forms. First, we developed a genetic algorithm-combined partial least squares method for exploring the optimal combination of molecular descriptors for prediction. In brief, assuming that necessary and unnecessary descriptors were depicted as "1" and "0", respectively, the combination of descriptors was expressed as a binary sequence. The optimal binary sequence, which possesses highest predictability, was explored by the use of genetic algorithm. As molecular descriptors, we selected topological indices that can be easily calculated based on 2-dimensinal molecular structure. Based on the approach, we could obtain good prediction models for aqueous solubility and epithelial cell permeability, that are two major factors determining oral bioavailability