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Successful structure-based drug design (SBDD) requires the optimization of interactions with the target protein and the minimization of ligand strain. Both factors are often modulated by small changes in the chemical structure which can lead to profound changes in the preferred conformation and interaction preferences of the ligand. We draw from examples of a Roche project targeting phosphodiesterase 10 to highlight that details matter in SBDD. Data mining in crystal structure databases can help to identify these sometimes subtle effects, but it is also a great resource to learn about molecular recognition in general and can be used as part of molecular design tools. We illustrate the use of the Cambridge Structural Database for identifying preferred structural motifs for intramolecular hydrogen bonding and of the Protein Data Bank for deriving propensities for protein-ligand interactions.

Lead generation and the associated hit-to-lead process are key strategic elements in modern pharmaceutical research, and most companies have implemented this concept. Efficient lead generation is one of the main attempts to reduce the high attrition rates observed along the drug discovery process by focussing on the early developmental phases. The level of integration of the lead generation activities within the discovery organization, the flexibility in assessing and implementing new chemistries and new technologies, the high-quality standards set for the identification of the best possible chemical lead series will ultimately determine the future success in discovering new medicines.