The accelerating growth of make-on-demand chemical libraries provides unprecedented opportunities to identify starting points for drug discovery with virtual screening. However, these multi-billion-scale libraries are challenging to screen, even for the fastest structure-based docking methods. Here we explore a strategy that combines machine learning and molecular docking to enable rapid virtual screening of databases containing billions of compounds. In our workflow, a classification algorithm is trained to identify top-scoring compounds based on molecular docking of 1 million compounds to the target protein. The conformal prediction framework is then used to make selections from the multi-billion-scale library, reducing the number of compounds to be scored by docking. The CatBoost classifier showed an optimal balance between speed and accuracy and was used to adapt the workflow for screens of ultralarge libraries. Application to a library of 3.5 billion compounds demonstrated that our protocol can reduce the computational cost of structure-based virtual screening by more than 1,000-fold. Experimental testing of predictions identified ligands of G protein-coupled receptors and demonstrated that our approach enables discovery of compounds with multi-target activity tailored for therapeutic effect.

Schizophrenia is a major mental illness characterized by positive and negative symptoms, and by cognitive deficit. Although cognitive impairment is disabling for patients, it has been largely neglected in the treatment of schizophrenia. There are several reasons for this lack of treatments for cognitive deficit, but the complexity of its etiology—in which neuroanatomic, biochemical and genetic factors concur—has contributed to the lack of effective treatments. In the last few years, there have been several attempts to develop novel drugs for the treatment of cognitive impairment in schizophrenia. Despite these efforts, little progress has been made. The latest findings point to the importance of developing personalized treatments for schizophrenia which enhance neuroplasticity, and of combining pharmacological treatments with non-pharmacological measures.

The soluble epoxide hydrolase (sEH) has been suggested as a pharmacological target for the treatment of several diseases, including pain-related disorders. Herein, we report further medicinal chemistry around new benzohomoadamantane-based sEH inhibitors (sEHI) in order to improve the drug metabolism and pharmacokinetics properties of a previous hit. After an extensive in vitro screening cascade, molecular modeling, and in vivo pharmacokinetics studies, two candidates were evaluated in vivo in a murine model of capsaicin-induced allodynia. The two compounds showed an anti-allodynic effect in a dose-dependent manner. Moreover, the most potent compound presented robust analgesic efficacy in the cyclophosphamide-induced murine model of cystitis, a well-established model of visceral pain. Overall, these results suggest painful bladder syndrome as a new possible indication for sEHI, opening a new range of applications for them in the visceral pain field.

In this study we developed a new translational phenotypic in vitro model for high-throughput screening (HTS) of novel analgesics for treating neuropathic pain, in order to address the poor translation of traditional recombinant models. The immortalized dorsal root ganglia (DRG) neuron-like F11 cell line was selected based on its phenotype after differentiation. The acquisition of neuronal characteristics was evaluated by measuring the expression of TrkA as a DRG neuron marker (p < 0.01) as well as by measuring the global neurite length (p < 0.001). The response of F11 cells to ATP and KCl was obtained by measuring intracellular calcium concentration, dynamic mass redistribution, and membrane potential. A KCl-induced increase of intracellular calcium levels was chosen as the readout because of the better signal quality, higher reproducibility, and greater compatibility with HTS assay requirements compared with other methods. The response to KCl differed significantly between differentiated and undifferentiated cells (p < 0.05), with an EC₅₀ value of 5 mM in differentiated cells. The model was validated by screening the Prestwick Chemical Library. Five hits already proposed for neuropathic-related pain were identified, with IC₅₀ values between 1 and 7 µM. This cell model provides a new tool for screening novel analgesics for the relief of neuropathic pain.

This study presents the design, synthesis, and characterization of a novel series of structurally simple, selective, and functionally biased CB2 receptor (CB2R) agonists with potent anti-inflammatory and neuroprotective properties. These compounds were developed using a conformational restriction strategy to abolish CB1R binding, thereby enhancing CB2R selectivity. Pharmacological profiling identified ligands with distinct bias toward β-arrestin, MAPK, and G-protein signaling pathways. The series exhibits favorable drug-like properties, including high BBB permeability, low P-glycoprotein interaction, and microsomal stability. Representative compounds demonstrated neuroprotective activity in mouse primary neuronal assays and significantly reduced ROS and caspase levels in vitro, indicating mitigation of oxidative stress and apoptosis. In a neuron-like SH-SY5Y model expressing pathogenic mutations, they preserved neurite complexity in a CB2R-dependent manner. Collectively, these findings highlight the advantages of conformational restriction in transforming abused promiscuous, neurotoxic ligands into highly selective and efficacious agents for the treatment of neurodegenerative disorders, without CB1R-mediated psychoactive effects.

Neuropathic pain is one of the foremost adverse effects that worsens quality of life for patients undergoing an antiretroviral treatment. Currently, there are no effective analgesics for relieving it; thus, there is an urgent need to develop novel treatments for neuropathic pain. Previously, we described and validated F11 cells as a model of DRG (dorsal root ganglia) neurons. In the current work, we employed F11 cells to identify regulators of antiretroviral-induced neuropathic pain combining functional and transcriptomic analysis. The antiretroviral zalcitabine (ddC) increased the excitability of differentiated F11 cells associated with calcium signaling without morphological changes in the neuronal phenotype, mimicking the observed increase of painful signaling in patients suffering from antiretroviral-induced neuropathic pain. Employing RNA sequencing, we observed that zalcitabine treatment upregulated genes related with oxidative stress and calcium homeostasis. The functional impact of the transcriptomic changes was explored, finding that the exposure to zalcitabine significantly increased intracellular oxidative stress and reduced store-operated calcium entry (SOCE). Because the functional and transcriptomic evidence points toward fundamental changes in calcium signaling and oxidative stress upon zalcitabine exposure, we identified that NAD(P)H quinone dehydrogenase and the sarcoplasmic/endoplasmic reticulum calcium ATPase 3 were involved in zalcitabine-induced hyperexcitability of F11 cells. Overexpression of those genes increases the calcium-elicited hyperexcitability response and reduces SOCE, as well as increases intracellular ROS levels. These data do not only mimic the effects of zalcitabine but also highlight the relevance of oxidative stress and of calcium-mediated signaling in antiretroviral-induced hyperexcitability of sensory neurons, shedding light on new therapeutic targets for antiviral-induced neuropathic pain.