Abstract
“Lead molecule” is a chemical compound deemed as a good candidate for drug discovery. Designing a lead molecule for optimization involves a complex phase in which researchers look for compounds that satisfy pharmaceutical properties and can then be investigated for drug development and clinical trials. Finding the optimal lead molecule is a hard problem that commonly requires searching in high dimensional and large experimental spaces. In this paper we propose to discover the optimal lead molecule by developing an evolutionary model-based approach where different classes of statistical models can achieve relevant information. The analysis is conducted comparing two different chemical representations of molecules: the amino-boronic acid representation and the chemical fragment representation. To deal with the high dimensionality of the fragment representation we adopt the Formal Concept Analysis and we then derive the evolutionary path on a reduced number of fragments. This approach has been tested on a particular data set of 2500 molecules and the achieved results show the very good performance of this strategy.
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Acknowledgements
The authors would like to acknowledge Professor Philip J. Brown and the GlaxoSmithKline Medicines Research Centre (UK) for the very fruitful collaboration in developing this research.
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Giovannelli, A., Slanzi, D., Khoroshiltseva, M., Poli, I. (2017). Model-Based Lead Molecule Design. In: Rossi, F., Piotto, S., Concilio, S. (eds) Advances in Artificial Life, Evolutionary Computation, and Systems Chemistry. WIVACE 2016. Communications in Computer and Information Science, vol 708. Springer, Cham. https://doi.org/10.1007/978-3-319-57711-1_9
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DOI: https://doi.org/10.1007/978-3-319-57711-1_9
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