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Modern drug discovery employs a 'screening funnel' to pick compounds worthy of advancing to the clinic, a multi-step process linking a series of assays. Molecules which are active in in vitro assays are passed to a cell-based assay, etc. Each pair of assays may be discordant, due to their measuring similar but not identical properties. This can create an enormous potential to overlook the best molecules, which we highlight here through an understanding of relationships we call 'hit diffusion'. Understanding hit diffusion has important implications for structure-based design, and drug discovery overall. The biophysical bases for assay discordance are outlined, and some strategies for ameliorating the hit diffusion problem are described., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

This perspectives article has been taken from a talk the author gave at the symposium in honor of Yvonne C. Martin's retirement, held at the American Chemical Society spring meeting in Chicago on March 25, 2007. The talk was intended as a somewhat lighthearted attempt to gaze into the future; inevitably, in print, things will come across more seriously than was intended. As we all know-the past is rarely predictive of the future.

Strategies are described for constructing pharmacophoric 3D database queries, based on a series of active and inactive analogs. The results are highly selective database queries, which are consistent with the generally accepted pharmacophore for a number of systems. The foundation of these strategies is the method of Mayer, Naylor, Motoc and Marshall [J. Comput.-Aided Mol. Design, 1 (1987) 3] for inferring a unique binding geometry for angiotensin-converting enzyme (ACE) inhibitors. The strategies described here generalize their approach to cases where the chemical features responsible for binding are not a priori apparent, and to cases where the binding geometry deduced by that method is not unique. The key new insight, the selectivity principle, is to rank the multiple solutions produced by the method of Mayer et al. by their selectivity, a value that is related to the proportion of a database that is returned as a database hit list. Retrospective analyses are described for D2-antagonists, ACE inhibitors, fibrinogen antagonists, and beta 2-antagonists.

A mathematical formula is introduced for predicting the number of hits that should be observed in a flexible 3D database search, based on the results of a set of related queries. The projected number of hits is always greater than or equal to the actual number of hits, the discrepancy being due to imperfect treatment of conformational flexibility of the molecules. Hence, the difference between the projected and actual number of hits, delta, serves to measure how well conformational flexibility is being treated, in a manner that is objective, easy for a user to quickly verify, and independent of the particular algorithm for flexible 3D database search. It is shown that delta is a function both of how well conformational flexibility is treated and of the precision of the query. When the distance constraint is defined only to a precision of +/- 2.0 A, in a single-conformer database of drug-like molecules delta values of only 0.03 are found, while in a single-conformer database of di- and tripeptides, delta is 0.15. At increased precision, a flexible 3D database search becomes critical. For a single-conformer database, using a query of precision +/- 0.2 A, applied to a database of drug-like molecules, delta is 0.97; applied to a database of di- and tripeptides, delta is 2.21. By contrast, treating conformational flexibility by storing up to 100 conformers per molecule, at this precision, applied to a database of drug-like molecules, delta is 0.002; applied to a database of di- and tripeptides, delta is 0.07. This inequality, and hence delta, is defined only for database queries containing a single distance constraint; how the inequality may generalize to higher-dimensional queries is still unclear.

ALADDIN is a computer program for the design or recognition of compounds that meet geometric, steric, and substructural criteria. ALADDIN searches a database of three-dimensional structures, marks atoms that meet substructural criteria, evaluates geometric criteria, and prepares a number of files that are input for molecular modification and coordinate generation as well as for molecular graphics. Properties calculated from the three-dimensional structure are described by either properties calculated from the molecule itself or from the molecule as compared to a reference molecule and associated surfaces. ALADDIN was used to design analogues to probe a bioactive conformation of a small molecule and a peptide, to test alternative superposition rules for receptor mapping of the D2 dopamine receptor, to recognize unexpected D2 dopamine agonist activity of existing compounds, and to design compounds to fit a binding site on a protein of known structure. We have found that series designed by ALADDIN show much more subtle variation in shape than do those designed by traditional methods and that compounds can be designed to be very close matches to the objective.