Activity cliffs (ACs) are an important type of structure−activity relationship in medicinal chemistry where small structural changes result in unexpectedly large differences in biological activity. Being able to predict these changes would have a profound impact on lead optimization of drug candidates. Free-energy perturbation is an ideal tool for predicting relative binding energy differences for small structural modifications, but its performance for ACs is unknown. Here, we show that FEP can on average predict ACs to within 1.39 kcal/mol of experiment (∼1 log unit of activity). We performed FEP calculations with two different software methods: Schrodinger−Desmond FEP+ and GROMACS implementations. There was qualitative agreement in the results from the two methods, and quantitatively the error for one data set was identical, 1.43 kcal/mol, but FEP+ performed better in the second, with errors of 1.17 versus 1.90 kcal/mol. The results have far-reaching implications, suggesting well-implemented FEP calculations can have a major impact on computational drug design.

Cannabidiol (CBD), the second most abundant of the active compounds found in the Cannabis sativa plant, is of increasing interest because it is approved for human use and is neither euphorizing nor addictive. Here, we design and synthesize novel compounds taking into account that CBD is both a partial agonist, when it binds to the orthosteric site, and a negative allosteric modulator, when it binds to the allosteric site of the cannabinoid CB2 receptor. Molecular dynamic simulations and site-directed mutagenesis studies have identified the allosteric site near the receptor entrance. This knowledge has permitted to perform structure-guided design of negative and positive allosteric modulators of the CB2 receptor with potential therapeutic utility.

The metabotropic glutamate 5 (mGlu5) receptor is a class C G protein-coupled receptor (GPCR) that is implicated in several CNS disorders making it a popular drug discovery target. Years of research have revealed allosteric mGlu5 ligands showing an unexpected complete switch in functional activity despite only small changes in their chemical structure, resulting in positive allosteric modulators (PAM) or negative allosteric modulators (NAM) for the same scaffold. Up to now, the origins of this effect are not understood, causing difficulties in a drug discovery context. In this work, experimental data was gathered and analyzed alongside docking and Molecular Dynamics (MD) calculations for three sets of PAM and NAM pairs. The results consistently show the role of specific interactions formed between ligand substituents and amino acid side chains that block or promote local movements associated with receptor activation. The work provides an explanation for how such small structural changes lead to remarkable differences in functional activity. While this work can greatly help drug discovery programs avoid these switches, it also provides valuable insight into the mechanisms of class C GPCR allosteric activation. Furthermore, the approach shows the value of applying MD to understand functional activity in drug design programs, even for such close structural analogues.

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