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The trend toward designing large hydrophobic molecules for lead optimization is often associated with poor drug-likeness and high attrition rates in drug discovery and development. Structural simplification is a powerful strategy for improving the efficiency and success rate of drug design by avoiding “molecular obesity”. The structural simplification of large or complex lead compounds by truncating unnecessary groups can not only improve their synthetic accessibility but also improve their pharmacokinetic profiles, reduce side effects and so on. This review will summarize the application of structural simplification in lead optimization. Numerous case studies, particularly those involving successful examples leading to marketed drugs or drug-like candidates, will be introduced and analyzed to illustrate the design strategies and guidelines for structural simplification., Graphical abstract Structural simplification is a powerful strategy for improving the efficiency and success rate of drug design. For large or complex lead compounds, structural simplification is helpful to discover drug-like molecules with improved synthetic accessibility and favorable pharmacodynamic/pharmacokinetic profiles.Image 1

This work presents a set of methods to improve the understandability of process models. Traditionally, simplification methods trade off quality metrics, such as fitness or precision. Conversely, the methods proposed in this paper produce simplified models while preserving or even increasing fidelity metrics. The first problem addressed in the paper is the discovery of duplicate tasks. A new method is proposed that avoids overfitting by working on the transition system generated by the log. The method is able to discover duplicate tasks even in the presence of concurrency and choice. The second problem is the structural simplification of the model by identifying optional and repetitive tasks. The tasks are substituted by annotated events that allow the removal of silent tasks and reduce the complexity of the model. An important feature of the methods proposed in this paper is that they are independent from the actual miner used for process discovery.

International audience; We study the structural simplification of chemical reaction networks preserving the deterministic kinetics. We aim at finding simplification rules that can eliminate intermediate molecules while preserving the dynamics of all others. The rules should be valid even though the network is plugged into a bigger context. An example is Michaelis-Menten's simplification rule for enzymatic reactions. In this paper, we present a large class of structural simplification rules for reaction networks that can eliminate intermediate molecules at equilibrium, without assuming that all molecules are at equilibrium, i.e. in a steady state. We prove the correctness of our simplification rules for all contexts that preserve the equilibrium of the eliminated molecules. Finally, we illustrate at a concrete example network from systems biology that our simplification rules may allow to drastically reduce the size of reaction networks in practice.