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How do we further enhance 2D fingerprint similarity searching for novel drug discovery?

The similarity principle in medicinal chemistry postulates that structurally similar compounds exhibit similar bioactivities. For several decades, molecular fingerprints have served as an insilico molecular representation of choice for early small molecule drug discovery-related tasks grounded in compound similarity such as virtual screening, assessing activity probabilities, analyzing chemical space, designing focused compound libraries, and providing features for predictive models describing quantitative structure activity or property relationships (QSAR or QSPR) [1]. 2D fingerprints have been particularly popular because they are easy to generate and fast to compare using binary representations. Often the focus of 2D fingerprint similarity searches is the identification of molecules with activity against a specific target; however, conversely, poly-pharmacology can be explored as well searching for targets a compound may show activity against [2]. Although new 2D fingerprints have been emerging that refine existing concepts such as the topological description of small molecules through characterizing its atomic environment [3], no fundamentally new concepts have found broad acceptance in the past few years. Remaining most popular for similarity searches, 2D fingerprints still fall mostly into the classes of topological fingerprints capturing atomic features and connectivity of varying depths, circular fingerprints, structural keys capturing substructures, and pharmacophore fingerprints hashed into bit strings. A number of similarity measures can be employed to calculate similarities between compounds represented by bitstring fingerprints. While the Tanimoto coefficient calculating the ratio of the intersection and union of bits set to ‘1’ is arguably the most frequently used pairwise similarity measure, there are many others that have been established [4]. What has changed in recent years are the ease of access to fingerprint-based similarity search methods, the increased use of 2D fingerprints in powerful machine learning and artificial intelligence methods, and the speed of performing similarity searches in large datasets (Figure 1). Although more complex and information-rich molecular representations such as 3D fingerprints exist, they rarely outperform 2D fingerprints [5]. 3D fingerprints allow for encoding molecular features that are mostly inaccessible to 2D methods, such as describing non-bonding atoms close in space but topologically distant, stereochemistry, and conformational ensemble characteristics. These 3D features can add important additional molecular information and therefore have the potential to provide better similarity prediction performance. However, the often difficult identification and weighting of relevant conformations adds noise and complexity, often without performance gain. Comparing directly related extended 3D circular fingerprints (E3FP) and 2D circular fingerprints (E2FP) demonstrated better performance of the 3D method in bioactivity predictions; however, this example is a rare exception in 3D fingerprint superiority [6]. 2D fingerprints remain largely popular due to their low sensitivity to feature noise and their proven success in finding diverse compounds with desired properties and activities. Of note are alternative molecular fingerprint representations such as biological or affinity fingerprints consisting of compound potencies against a panel of protein targets in combination with QSAR training that have generated promising results in scaffold hopping and virtual screening [7].