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Optimizing Drug Design
Description and aim
Drug discovery is inherently a strongly multi-objective problem: Molecules need to be active against the target, have few side-effects (if any), be soluble, have good pharmacokinetic and pharmacodynamic properties, possess good absorption properties and have favorable metabolism to ensure sufficient half-life in the body (just to mention a few of the relevant properties). Apart from the problem of searching the vast search space of drug molecules for (Pareto) optimal solutions, the modeling of decision preferences and constraints and the visualization and assessment of trade-offs among objectives becomes a challenge in the given multicriteria scenario.
Historically, activity on the desired target has been optimized first, in order to ensure potency of the resulting drug, eliminating the multi-disciplinary nature of the problem. However, subsequent optimization of other parameters was often cumbersome and, in many cases, did not lead to the desired outcome (i.e., a new drug on the market, or at the very least a new lead compound). Many time-consuming trial-and-error iterations including constraint refinements or relaxations are needed to find compounds this way. For this reason, more sophisticated techniques of making decisions in drug discovery have been pursued, involving (semi-)automatized iterative compound optimization cycles which already take into account multiple compound properties (or surrogate measures) at an early stage and allows to analyze potential conflicts/trade-offs between objectives by means of Pareto optimization techniques.
The aim of this workshop is to present and discuss ‘best in class’ practices on how algorithmic and software tools and information from multiple disciplines (most prominently life sciences and computer science) can be utilized to make optimal decisions. Secondly, the workshop will discuss the application of algorithms for finding optimal solutions in a drug discovery project. For this purpose, researchers from both multi-objective optimization and decision making, as well as academic and industrial researchers in drug discovery and pharmaceutical research are cordially invited to participate.