Multi-Criteria Decision Analysis in Drug Discovery
Drug discovery is inherently a multi-criteria optimization problem. In the first instance, it involves a tremendously large chemical space, where each compound can be characterized by multiple molecular and biological properties. Modern computational approaches try to efficiently explore the chemica...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Applied Biosciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2813-0464/4/1/2 |
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| Summary: | Drug discovery is inherently a multi-criteria optimization problem. In the first instance, it involves a tremendously large chemical space, where each compound can be characterized by multiple molecular and biological properties. Modern computational approaches try to efficiently explore the chemical space in search of molecules with the desired combination of properties. For example, Pareto optimizers identify a so-called “Pareto front”, a set of non-dominated solutions. From a qualitative perspective, all solutions on the front are potentially equally desirable, each expressing a trade-off between the goals. However, often there is a need to weight the objectives differently, depending on their perceived importance. To address this, we recently implemented a new Multi-Criteria Decision Analysis (MCDA) method as part of the AI-powered Drug Design (AIDD<sup>TM</sup>) technology initiative. This allows the user to weight various objective functions differently, which, in turn, efficiently directs the generative chemistry process toward the desired areas in chemical space. |
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| ISSN: | 2813-0464 |