Dopamine Receptor D2: A Computational Case Study with Crucible

Executive Summary

• DRD2 is implicated in depression, schizophrenia, bipolar, and other mental disorders

• We used Crucible to generate 70 optimized drug leads on a desktop computer (no cloud computing needed)

• 75% of leads tested (18 out of 24) bind to the disease target, D2, as well as, or better than on the market drugs, such as aripiprazole

What is Dopamine Receptor D2?

The dopamine receptor D2 (D2) is a central regulator of dopaminergic signaling in brain regions tied to mood, reward, and cognition, making it an outstanding target for drug development in psychiatric disorders. Dysregulation of D2 has been implicated in conditions such as depression, schizophrenia, bipolar disorder, ADHD, and addiction. In major depressive disorder, altered D2 function is linked to anhedonia and motivational deficits that are poorly addressed by traditional serotonin-targeting treatments. Emerging therapies, including partial D2 agonists like aripiprazole, have shown promise as effective adjuncts in treatment-resistant cases. Given its broad role across mental health conditions, D2 represents a high-value target for next-generation therapeutics aimed at diverse neuropsychiatric disorders.

Figure 1: The apo structure of D2 displayed in cartoon format with an outline of the surface mesh shown with transparency.

Lead Generation in Crucible

A cleaned structure of D2 (PDB ID: 6CM4, Figure 1) was used as a disease target for Crucible to produce drug candidates against. In total, 70 leads were optimized with Crucible which required approximately 24 hours of compute time on a desktop computer (equipped with an Intel i9-14900K, one Nvidia RTX 3080, and 64 Gb of memory). A sample of optimized structures are shown in Figure 2.

Figure 2: A selection of four optimized lead compounds that bind as well as or better than aripiprazole (see Figure 3). The total polar surface area (tPSA), an important property for crossing the blood-brain barrier, is less than 90 Ų for all of these compounds.

Molecular Dynamics Validation of Crucible-generated Leads

The top 24 leads were then used in molecular dynamics (MD) simulations for determining how strongly they interact with D2 through the use of mmPBSA analysis (Figure 3) (molecular mechanics Poisson Boltzmann Surface Area). Three on the market drugs, aripiprazole, brexpiprazole, and risperidone, were also used in this procedure to provide a point of reference of the quality of the optimized leads in terms of binding affinity. 18 out of the 24 tested leads bound as well as, if not better than, aripiprazole to D2. This represents a hit rate of 75%. Additionally, all of the top 24 leads had quantitative estimation of drug-likeness (QED) of greater than 0.5 and synthetic accessibility scores (SAS) near or below 2.5.

Figure 3: A bar chart of ΔΔH for each of the top 24 optimized lead compounds as well as on the market drugs: aripiprazole, brexpiprazole, and risperidone. ΔΔH is defined as the difference in ΔH’s as determined by mmPBSA analysis, between a given lead compound and aripiprazole.

Conclusion

In conclusion, Crucible is a powerful molecular optimization platform for drug discovery. It requires relatively little in terms of computational power and yields exceptionally high hit rates with favorable molecular properties like QED, SAS, and tPSA.

To learn more information about Crucible, or if you’d like to test any of the optimized leads in your own lab, please contact us here.