We have applied the two most commonly used methods for automatic matched pair identification, obtained the optimum settings, and discovered that the two methods are synergistic. A turbocharging approach to matched pair analysis is advocated in which a first round (a conservative categorical approach that uses an analogy with coin flips, heads corresponding to an increase in a measured property, tails to a decrease, and a biased coin to a structural change that reliably causes a change in that property) provides the settings for a second round (which uses the magnitude of the change in properties). Increased chemical specificity allows reliable knowledge to be extracted from smaller sets of pairs, and an assay-specific upper limit can be placed on the number of pairs required before adequate sampling of variability has been achieved.

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Many of the recently developed methods to study the shape of molecules permit one conformation of one molecule to be compared to another conformation of the same or a different molecule: a relative shape. Other methods provide an absolute description of the shape of a conformation that does not rely on comparisons or overlays. Any absolute description of shape can be used to generate a self-organizing map (shape map) that places all molecular shapes relative to one another; in the studies reported here, the shape fingerprint and ultrafast shape recognition methods are employed to create such maps. In the shape maps, molecules that are near one another have similar shapes, and the maps for the 102 targets in the DUD-E set have been generated. By examining the distribution of actives in comparison with their physical-property-matched decoys, we show that the proteins of key-in-lock type (relatively rigid receptor and ligand) can be distinguished from those that are more of a hand-in-glove type (more flexible receptor and ligand). These are linked to known differences in protein flexibility and binding-site size.

The ongoing COVID-19 pandemic, and constant demand for new therapies in unmet clinical needs, necessitates strategies to identify drug candidates for rapid clinical deployment. Over the years, fragment-based drug design (FBDD) has emerged as a mainstream lead discovery strategy in academia, biotechnology start-ups, and large pharma. Chemical building block libraries are the fundamental component of virtually any FBDD campaign. Current trends focus on smaller and smarter libraries that offer synthetically amenable starting points for rational lead generation. Therefore, there remains an ever-increasing need for new methods to generate fragment libraries to seed early-stage drug discovery programs. Here, we present FRAGMENTISE-a new user-friendly, cross-platform tool for user-tunable retrosynthetic small-molecule fragmentation. FRAGMENTISE allows for visualization, similarity search, annotation, and in-depth analysis of the fragment databases in the medicinal chemistry context. FRAGMENTISE is available as standalone software for Linux, Windows, and macOS users, with a graphical interface or command-line version.

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