The Maximum Dissimilarity algorithm is a computational method used for selecting a subset of items from a larger collection such that the selected subset is as diverse as possible.
Based on common computational algorithms used for selecting compounds, the Maximum Dissimilarity approach operates by maximizing the diversity of the selected subset with respect to a set of descriptors and some associated (dis)-similarity measure.
Understanding the Core Concept
At its heart, the algorithm aims to pick items that are significantly different from each other. Instead of selecting items that are similar or representative of the whole set in a clustered way, it seeks out outliers or unique individuals to ensure broad coverage of the "space" defined by the chosen descriptors.
Key Components
The operation of the Maximum Dissimilarity algorithm relies on several crucial elements:
- Items: The objects from which a subset is to be selected (e.g., chemical compounds, data points).
- Descriptors: Quantitative features or properties that describe each item (e.g., molecular weight, shape, color parameters, statistical features). These descriptors are used to compare items.
- Dissimilarity Measure: A mathematical function that quantifies how different two items are based on their descriptors (e.g., Euclidean distance, Tanimoto distance for molecular fingerprints). A higher value indicates greater dissimilarity.
- Subset Size: The desired number of items to be selected.
The algorithm typically works iteratively, selecting one item at a time that is maximally dissimilar to those already chosen, or by selecting a set of items that collectively maximize some measure of diversity for the entire subset.
How it Works (Simplified)
A common greedy approach to maximum dissimilarity selection proceeds as follows:
- Start by selecting an initial item. This could be the item that is most "representative" or simply chosen at random. Often, the item that is least similar to all others in the initial set is chosen first.
- Iteratively add one item to the selected subset.
- In each step, select the item from the remaining pool that has the greatest minimum dissimilarity to any item already in the selected subset. This ensures the newly added item is as different as possible from everything already chosen.
- Repeat step 3 until the desired subset size is reached.
| Step | Action | Goal |
|---|---|---|
| Initialization | Choose initial item (often one far from others). | Start with a distinct point. |
| Iteration | Find item with maximum minimum dissimilarity to selected. | Ensure new item is maximally different from subset. |
| Repeat | Continue adding items. | Build a diverse subset of desired size. |
Applications
This algorithm is particularly valuable in fields where selecting a diverse but manageable subset from a large collection is necessary for further analysis or experimentation.
- Drug Discovery: Selecting a diverse set of chemical compounds for screening from a large library to cover a wide range of chemical space. (As indicated by the reference).
- Data Analysis: Choosing a representative, diverse subset of data points for model training or visualization when the full dataset is too large.
- Machine Learning: Selecting diverse exemplars for active learning or dataset summarization.
- Materials Science: Identifying a diverse set of candidate materials to explore.
By maximizing dissimilarity, researchers or analysts can explore a broader range of possibilities with a limited number of selections, potentially increasing the chances of discovering novel or unexpected results compared to selecting similar items.
