Rethinking the 'best method' paradigm: The effectiveness of hybrid and multidisciplinary approaches in chemoinformatics
In Chemoinformatics, as in many other computational-related disciplines, it is a common practice to identify the “single best” approach or methodology, for instance, identify the best fingerprint representation, the best single virtual screening approach or protocol, the optimal representation of th...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
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| Series: | Artificial Intelligence in the Life Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667318524000242 |
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| Summary: | In Chemoinformatics, as in many other computational-related disciplines, it is a common practice to identify the “single best” approach or methodology, for instance, identify the best fingerprint representation, the best single virtual screening approach or protocol, the optimal representation of the chemical space, the best predictive model, to name a few. In molecular modeling, a typical example is finding the best docking program. However, it is also known that each approach has its advantages and limitations. There are examples of benchmark studies comparing different approaches to find the most appropriate solution, and it is common to find that there are no single best programs in such studies. Yet, searching for the “best” methods is still common. The main goal of this work is to survey hybrid methodologies recently developed in Chemoinformatics. The list of approaches is not exhaustive, but it aims to cover several representative applications. One of the major outcomes of the survey is that, for various purposes, individual methods do not perform as well as the combination of approaches because single methods have inherent limitations with advantages and disadvantages. |
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| ISSN: | 2667-3185 |