Classifying shape coverage in fragment libraries using a fingerprinting approach.

Fragment screening is one approach to hit identification for early stage drug discovery projects. Like any screening library, diversity is needed in fragment libraries. This includes coverage of shape and electrostatic space, as well as chemotype diversity. A new, easily interpretable shape-based fingerprint is described and its utility in probing fragment library content is demonstrated using a Rule of Three library from Maybridge. This method explicitly considers size as a component of shape. It allows interrogation of shape space on both a per conformer and a per molecule level, and includes a measure of flexibility. This allows for the identification of highly flexible compounds and their exclusion from the analysis. A comparison with two literature methods, the triangle plot approach of Sauer and Schwarz and the plane of best fit method of Firth et al., is also included.

Strategies for small molecule library design.

Compilation of an appropriate set of compounds is essential for the success of a small molecule screen. When very little is known about the target and when no or few ligands have been identified, the screening file is often made as diverse as possible. When structural information on the target or target family is available or ligands of the target are known, it is more efficient to apply a ligand- or target-focused bias, so as to predominantly screen compounds that can be expected to modulate the target. One way to achieve this is to select subsets of existing collections; another is to specifically design and synthesize libraries focused on a particular target, target family or mechanism of action. Despite the number of success stories, designing such libraries is still challenging and requires specialized knowledge, especially in emerging target areas such as protein-protein interactions (PPI), epigenetics and the ubiquitin proteasome pathway. BioFocus has successfully produced so-called SoftFocus(®) libraries for many years, evolving their targets from kinases to GPCRs and ion channels to difficult targets in the epigenetics and PPI fields. This article outlines several of the principles applied to SoftFocus library design, showcasing successes achieved by BioFocus' clients. In addition, screening results for a comprehensive set of BioFocus' kinase libraries against 20 kinase targets are used to demonstrate the power of the SoftFocus approach in delivering both selective and less-selective compounds and libraries against these targets. Trademarks: BioFocus(®), SoftFocus(®), HDRA™, FieldFocus™, Thematic Analysis™, ThemePair™ and ThemePair Fragment™ are trademarks of Galapagos NV and/or its affiliates.

How large does a compound screening collection need to be?

Increasingly, chemical libraries are being produced which are focused on a biological target or group of related targets, rather than simply being constructed in a combinatorial fashion. A screening collection compiled from such libraries will contain multiple analogues of a number of discrete series of compounds. The question arises as to how many analogues are necessary to represent each series in order to ensure that an active series will be identified. Based on a simple probabilistic argument and supported by in-house screening data, guidelines are given for the number of compounds necessary to achieve a "hit", or series of hits, at various levels of certainty. Obtaining more than one hit from the same series is useful since this gives early acquisition of SAR (structure-activity relationship) and confirms a hit is not a singleton. We show that screening collections composed of only small numbers of analogues of each series are sub-optimal for SAR acquisition. Based on these studies, we recommend a minimum series size of about 200 compounds. This gives a high probability of confirmatory SAR (i.e. at least two hits from the same series). More substantial early SAR (at least 5 hits from the same series) can be gained by using series of about 650 compounds each. With this level of information being generated, more accurate assessment of the likely success of the series in hit-to-lead and later stage development becomes possible.