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Pharmacological modulation of cannabinoid type 2 receptor (CB2R) holds promise for the treatment of numerous conditions, including inflammatory diseases, autoimmune disorders, pain, and cancer. Despite the significance of this receptor, researchers lack reliable tools to address questions concerning the expression and complex mechanism of CB2R signaling, especially in cell-type and tissue-dependent context. Herein, we report for the first time a versatile ligand platform for the modular design of a collection of highly specific CB2R fluorescent probes, used successfully across applications, species and cell types. These include flow cytometry of endogenously expressing cells, real-time confocal microscopy of mouse splenocytes and human macrophages, as well as FRET-based kinetic and equilibrium binding assays. High CB2R specificity was demonstrated by competition experiments in living cells expressing CB2R at native levels. The probes were effectively applied to FACS analysis of microglial cells derived from a mouse model relevant to Alzheimer's disease and to the detection of CB2R in human breast cancer cells.

Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding for drug discovery. Using deep learning for molecular design and a microfluidics platform for on-chip chemical synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space of known LXRα agonists and generate novel molecular candidates. To ensure compatibility with automated on-chip synthesis, the chemical space was confined to the virtual products obtainable from 17 one-step reactions. Twenty-five de novo designs were successfully synthesized in flow. In vitro screening of the crude reaction products revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch resynthesis, purification, and retesting of 14 of these compounds confirmed that 12 of them were potent LXR agonists. These results support the suitability of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis.

Despite the broad implications of the cannabinoid type 2 receptor (CB2) in neuroinflammatory processes, a suitable CB2-targeted probe is currently lacking in clinical routine. In this work, we synthesized 15 fluorinated pyridine derivatives and tested their binding affinities toward CB2 and CB1. With a sub-nanomolar affinity (K i for CB2) of 0.8 nM and a remarkable selectivity factor of >12,000 over CB1, RoSMA-18-d 6 exhibited outstanding in vitro performance characteristics and was radiofluorinated with an average radiochemical yield of 10.6 ± 3.8% (n = 16) and molar activities ranging from 52 to 65 GBq/μmol (radiochemical purity > 99%). [18F]­RoSMA-18-d 6 showed exceptional CB2 attributes as demonstrated by in vitro autoradiography, ex vivo biodistribution, and positron emission tomography (PET). Further, [18F]­RoSMA-18-d 6 was used to detect CB2 upregulation on postmortem human ALS spinal cord tissues. Overall, these results suggest that [18F]­RoSMA-18-d 6 is a promising CB2 PET radioligand for clinical translation.

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Machine learning approaches in drug discovery, as well as in other areas of the chemical sciences, benefit from curated datasets of physical molecular properties. However, there currently is a lack of data collections featuring large bioactive molecules alongside first-principle quantum chemical information. The open-access QMugs (Quantum-Mechanical Properties of Drug-like Molecules) dataset fills this void. The QMugs collection comprises quantum mechanical properties of more than 665 k biologically and pharmacologically relevant molecules extracted from the ChEMBL database, totaling ~2 M conformers. QMugs contains optimized molecular geometries and thermodynamic data obtained via the semi-empirical method GFN2-xTB. Atomic and molecular properties are provided on both the GFN2-xTB and on the density-functional levels of theory (DFT, ωB97X-D/def2-SVP). QMugs features molecules of significantly larger size than previously-reported collections and comprises their respective quantum mechanical wave functions, including DFT density and orbital matrices. This dataset is intended to facilitate the development of models that learn from molecular data on different levels of theory while also providing insight into the corresponding relationships between molecular structure and biological activity.

The cannabinoid type 2 (CB2) receptor has emerged as a valuable target for therapy and imaging of immune-mediated pathologies. With the aim to find a suitable radiofluorinated analogue of the previously reported CB2 positron emission tomography (PET) radioligand [11C]­RSR-056, 38 fluorinated derivatives were synthesized and tested by in vitro binding assays. With a K i (hCB2) of 6 nM and a selectivity factor of nearly 700 over cannabinoid type 1 receptors, target compound 3 exhibited optimal in vitro properties and was selected for evaluation as a PET radioligand. [18F]3 was obtained in an average radiochemical yield of 11 ± 4% and molar activities between 33 and 114 GBq/μmol. Specific binding of [18F]3 to CB2 was demonstrated by in vitro autoradiography and in vivo PET experiments using the CB2 ligand GW-405 833. Metabolite analysis revealed only intact [18F]3 in the rat brain. [18F]3 detected CB2 upregulation in human amyotrophic lateral sclerosis spinal cord tissue and may thus become a candidate for diagnostic use in humans.

Generative chemical language models (CLMs) can be used for de novo molecular structure generation by learning from a textual representation of molecules. Here, we show that hybrid CLMs can additionally leverage the bioactivity information available for the training compounds. To computationally design ligands of phosphoinositide 3-kinase gamma (PI3Kγ), a collection of virtual molecules was created with a generative CLM. This virtual compound library was refined using a CLM-based classifier for bioactivity prediction. This second hybrid CLM was pretrained with patented molecular structures and fine-tuned with known PI3Kγ ligands. Several of the computer-generated molecular designs were commercially available, enabling fast prescreening and preliminary experimental validation. A new PI3Kγ ligand with sub-micromolar activity was identified, highlighting the method’s scaffold-hopping potential. Chemical synthesis and biochemical testing of two of the top-ranked de novo designed molecules and their derivatives corroborated the model’s ability to generate PI3Kγ ligands with medium to low nanomolar activity for hit-to-lead expansion. The most potent compounds led to pronounced inhibition of PI3K-dependent Akt phosphorylation in a medulloblastoma cell model, demonstrating efficacy of PI3Kγ ligands in PI3K/Akt pathway repression in human tumor cells. The results positively advocate hybrid CLMs for virtual compound screening and activity-focused molecular design.