Abstract
Lead Optimization is a complex process, whereby a large number of interacting entities give rise to molecular structures whose properties should be optimized in order to be considered for drug development. We will study molecular systems that are characterized by high dimensionality and dynamically interacting networks with the goal of discovering the optimal molecules with respect to the set of essential properties. Currently, the research involves the screening and the identification of molecule with desirable properties from large molecule libraries. Lead Optimization is a multi-objective optimization problem. The classical approaches involving in-vitro laboratory analysis are time consuming and very expensive. To address this problem, we propose in this paper an in-silico approach: Lead Optimization based on Neural Network (NN) model in order to help the chemist in the lab experimentation by requiring a small set of real laboratory tests. We propose and estimate a predictive network model to derive a simultaneous optimal multi-response property following a single and multi-objective optimization procedure. We adopt different architectures in this study and we compare our procedure with other state-of-the-art method showing the better performance of our approach.
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Dagnew, T.M., Silvestri, C., Slanzi, D., Poli, I. (2021). A Neural Network Model for Lead Optimization of MMP12 Inhibitors. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_23
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DOI: https://doi.org/10.1007/978-3-030-68799-1_23
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