Bespoke library docking for 5-HT2A receptor agonists with antidepressant activity

Improved Prediction of Drug-Protein Interactions through Physics-Based Few-Shot Learning

Accurate prediction of drug-protein interactions is crucial for drug discovery. Due to the bottleneck of traditional scoring functions, many machine learning scoring functions (MLSFs) have been proposed for structure-based drug screening. However, existing MLSFs face two challenges: small data limitations and poor interpretability. To address these challenges, we have proposed a physics-based small data machine learning framework for interpretable and generalizable prediction of drug-protein interactions on the target with scarce positive data through a strategy of three training phases with three (score, weight, and ranking) loss functions, named DrugBaiter. DrugBaiter has been extensively evaluated on the 102 targets of DUD-E and 81 targets of DEKOIS 2.0 for drug screening, and compared with 14 other MLSFs. It is shown that our DrugBaiter model can significantly improve the drug screening performance even if few actives are known for a target. In addition, DrugBaiter is interpretable in describing the interactions at the atomic level. The power of DrugBaiter is also confirmed by a drug screening application on the SARS-Cov-2 main protease target. It is anticipated that DrugBaiter will serve as a general machine learning scoring model for screening novel drugs on new targets with scarce known actives. DrugBaiter is freely available at http://huanglab.phys.hust.edu.cn/DrugBaiter.

A large-scale curated and filterable dataset for cryo-EM foundation model pre-training

Cryo-electron microscopy (cryo-EM) is a transformative imaging technology that enables near-atomic resolution 3D reconstruction of target biomolecule, playing a critical role in structural biology and drug discovery. Cryo-EM faces significant challenges due to its extremely low signal-to-noise ratio (SNR) where the complexity of data processing becomes particularly pronounced. To address this challenge, foundation models have shown great potential in other biological imaging domains. However, their application in cryo-EM has been limited by the lack of large-scale, high-quality datasets. To fill this gap, we introduce CryoCRAB, the first large-scale dataset for cryo-EM foundation models. CryoCRAB includes 746 proteins, comprising 152,385 sets of raw movie frames (116.8 TB in total). To tackle the high-noise nature of cryo-EM data, each movie is split into odd and even frames to generate paired micrographs for denoising tasks. The dataset is stored in HDF5 chunked format, significantly improving random sampling efficiency and training speed. CryoCRAB offers diverse data support for cryo-EM foundation models, enabling advancements in image denoising and general-purpose feature extraction for downstream tasks.

Discovery and characterization of novel FAK inhibitors for breast cancer therapy via hybrid virtual screening, biological evaluation and molecular dynamics simulations

Focal adhesion kinase (FAK) is a critical drug target implicated in various disease pathways, including hematological malignancies and breast cancer. Therefore, identifying FAK inhibitors with novel scaffolds could offer new opportunities for developing effective therapeutic compounds. Herein, we disclosed the discovery of a new backbone inhibitor of FAK using an "internal" database, employing a structure-based high-transparency permeability virtual screening (HTVS) and a DeepDock algorithm based on geometric deep learning. Subsequently, molecular docking was conducted at different precisions to identify 10 compounds for further evaluation of biological activity. Ultimately, compound 4, a pyrimidin-4-amine derivative, demonstrated inhibitory activity against FAK and breast cancer cells, further supporting its potential as a FAK inhibitor. Moreover, molecular dynamics simulations were carried out to gain more detailed insights into the binding mechanism between compound 4 and FAK to guide subsequent structural optimization.

Psilocybin's lasting action requires pyramidal cell types and 5-HT2A receptors

Psilocybin is a serotonergic psychedelic with therapeutic potential for treating mental illnesses1-4. At the cellular level, psychedelics induce structural neural plasticity5,6, exemplified by the drug-evoked growth and remodelling of dendritic spines in cortical pyramidal cells7-9. A key question is how these cellular modifications map onto cell-type-specific circuits to produce the psychedelics' behavioural actions10. Here we use in vivo optical imaging, chemogenetic perturbation and cell-type-specific electrophysiology to investigate the impact of psilocybin on the two main types of pyramidal cells in the mouse medial frontal cortex. We find that a single dose of psilocybin increases the density of dendritic spines in both the subcortical-projecting, pyramidal tract (PT) and intratelencephalic (IT) cell types. Behaviourally, silencing the PT neurons eliminates psilocybin's ability to ameliorate stress-related phenotypes, whereas silencing IT neurons has no detectable effect. In PT neurons only, psilocybin boosts synaptic calcium transients and elevates firing rates acutely after administration. Targeted knockout of 5-HT2A receptors abolishes psilocybin's effects on stress-related behaviour and structural plasticity. Collectively, these results identify that a pyramidal cell type and the 5-HT2A receptor in the medial frontal cortex have essential roles in psilocybin's long-term drug action.

Docking 14 Million Virtual Isoquinuclidines against the μ and κ Opioid Receptors Reveals Dual Antagonists-Inverse Agonists with Reduced Withdrawal Effects

Large library docking of tangible molecules has revealed potent ligands across many targets. While make-on-demand libraries now exceed 75 billion enumerated molecules, their synthetic routes are dominated by a few reaction types, reducing diversity and inevitably leaving many interesting bioactive-like chemotypes unexplored. Here, we investigate the large-scale enumeration and targeted docking of isoquinuclidines. These "natural-product-like" molecules are rare in current libraries and are functionally congested, making them interesting as receptor probes. Using a modular, four-component reaction scheme, we built and docked a virtual library of over 14.6 million isoquinuclidines against both the μ- and κ-opioid receptors (MOR and KOR, respectively). Synthesis and experimental testing of 18 prioritized compounds found nine ligands with low μM affinities. Structure-based optimization revealed low- and sub-nM antagonists and inverse agonists targeting both receptors. Cryo-electron microscopy structures illuminate the origins of activity on each target. In mouse behavioral studies, a potent joint MOR-antagonist and KOR-inverse-agonist reversed morphine-induced analgesia, phenocopying the MOR-selective antioverdose agent naloxone. Encouragingly, the isoquinuclidine induced less severe opioid-withdrawal symptoms versus naloxone and did not induce conditioned-place aversion, reflecting reduced dysphoria, consistent with its KOR-inverse agonism. The strengths and weaknesses of bespoke library docking and of docking for opioid receptor polypharmacology will be considered.

Neuropsychopharmacology of hallucinogenic and non-hallucinogenic 5-HT2A receptor agonists

Psychedelic drugs such as LSD and psilocin were once relegated to the fringes of medical research because of their association with counterculture movements and a perceived concern about harm through recreational use, and their consequent legal prohibition in the early 1970s. However, these drugs are now experiencing a renaissance in the field of psychiatry based on increasing evidence that they can produce long-lasting improvements in health across a wide variety of mental illnesses, including major depression, addictions and anxiety disorders. These drugs interact with many different 5-HT receptor subtypes but the powerful psychedelic experience, which (depending on set and setting) includes profound alterations in perception, mood and cognition, accompanied by vivid hallucinations, is now widely considered mediated by an agonist action at 5-HT2A receptors. However, the link between the psychedelic experience, 5-HT2A receptor agonism and therapeutic effects is currently uncertain. Indeed, recent research has revealed a new class of 5-HT2A receptor agonists which appear to retain the therapeutic potential of psychedelics drugs without inducing disorienting hallucinatory experiences. Biased signalling, partial agonism and non-selectivity at the 5-HT2A receptor are amongst the possible explanations for the differential properties of these drugs, whereas increased neuroplasticity offers a likely account of their common therapeutic effects. This article explores the neuropsychopharmacological properties of hallucinogenic and non-hallucinogenic 5-HT2A receptor agonists in the context of their promise as novel drug treatments in psychiatry.

Intracellular GPCR modulators enable precision pharmacology

G-protein-coupled receptors (GPCRs) have proven to be the most successful target class for drug discovery but their complicated signal transduction pathways cause difficulties for drug development. Recently, ligands have been identified that engage an intracellular binding site which promotes pathway biased signal in cooperation with orthosteric ligands. Here, we explore the topic of biased signaling and intracellular modulators to understand their application for precision pharmacology of Class A or Rhodopsin-Like GPCRs.

5-HT2A receptors: Pharmacology and functional selectivity

Serotonin 5-HT2A receptors were one of the first serotonin receptors to be pharmacologically characterized. In mammals, they are expressed throughout the body in nearly every cell and tissue type, with the highest density in cortical layer V of the brain. They are involved in several aspects of normal physiological processes and behaviors and have been implicated in the etiology of neuropsychiatric diseases such as schizophrenia. Atypical antipsychotics have targeted blockade of 5-HT2A receptors as part of their therapeutic mechanism. More recently, 5-HT2A receptors have come to prominence for their role as the primary target for psychedelic drugs, which activate this receptor subtype to produce their characteristic behavioral effects. 5-HT2A receptor agonists like psilocybin, dimethyltryptamine, and lysergic acid diethylamide have each demonstrated long-lasting therapeutic efficacy in clinical trials for psychiatric disorders such as major depression and substance use disorders. There is a significant effort in both academia and industry to develop new agonists of 5-HT2A receptors with therapeutic efficacy. There are 3 primary scaffolds for agonists: tryptamines, ergolines, and phenylalkylamines, each engaging different subsets of amino acid residues in the receptor binding pocket. Differences can lead to differential responses between ligands for functionally selective outcomes. Here, we provide a historical perspective on 5-HT2A receptors, their key structural features and motifs involved in ligand-receptor interactions, and how these interactions can affect signaling pathways downstream of the receptor. Understanding how ligands interact with the 5-HT2A receptor will fundamentally inform future drug discovery to optimize therapeutics for a variety of disorders. SIGNIFICANCE STATEMENT: Psychedelic drugs have demonstrated long-lasting therapeutic efficacy for several conditions in multiple clinical trials. Their target, serotonin 5-HT2A receptors, are GPCRs with complex pharmacology. Having knowledge of how ligands interact with 5-HT2A receptors in the orthosteric binding pocket at the structural level to induce specific signal transduction pathways will inform on efforts to design and develop functionally selective drugs to potentially treat a variety of diseases.

Molecular design of a therapeutic LSD analogue with reduced hallucinogenic potential

Decreased dendritic spine density in the cortex is a key pathological feature of neuropsychiatric diseases including depression, addiction, and schizophrenia (SCZ). Psychedelics possess a remarkable ability to promote cortical neuron growth and increase spine density; however, these compounds are contraindicated for patients with SCZ or a family history of psychosis. Here, we report the molecular design and de novo total synthesis of (+)-JRT, a structural analogue of lysergic acid diethylamide (LSD) with lower hallucinogenic potential and potent neuroplasticity-promoting properties. In addition to promoting spinogenesis in the cortex, (+)-JRT produces therapeutic effects in behavioral assays relevant to depression and cognition without exacerbating behavioral and gene expression signatures relevant to psychosis. This work underscores the potential of nonhallucinogenic psychoplastogens for treating diseases where the use of psychedelics presents significant safety concerns.

Proton perception and activation of a proton-sensing GPCR

Maintaining pH at cellular, tissular, and systemic levels is essential for human health. Proton-sensing GPCRs regulate physiological and pathological processes by sensing the extracellular acidity. However, the molecular mechanism of proton sensing and activation of these receptors remains elusive. Here, we present cryoelectron microscopy (cryo-EM) structures of human GPR4, a prototypical proton-sensing GPCR, in its inactive and active states. Our studies reveal that three extracellular histidine residues are crucial for proton sensing of human GPR4. The binding of protons induces substantial conformational changes in GPR4's ECLs, particularly in ECL2, which transforms from a helix-loop to a β-turn-β configuration. This transformation leads to the rearrangements of H-bond network and hydrophobic packing, relayed by non-canonical motifs to accommodate G proteins. Furthermore, the antagonist NE52-QQ57 hinders human GPR4 activation by preventing hydrophobic stacking rearrangement. Our findings provide a molecular framework for understanding the activation mechanism of a human proton-sensing GPCR, aiding future drug discovery.

The structural diversity of psychedelic drug actions revealed

There is currently a resurgence in exploring the utility of classical psychedelics to treat depression, addiction, anxiety disorders, cluster headaches, and many other neuropsychiatric disorders. A biological target of these compounds, and a hypothesized target for their therapeutic actions, is the 5-HT2A serotonin receptor. Here, we present 7 cryo-EM structures covering all major compound classes of psychedelic and non-psychedelic agonists, including a β-arrestin-biased compound RS130-180. Identifying the molecular interactions between various psychedelics and the 5-HT2A receptor reveals both common and distinct motifs among the examined psychedelic chemotypes. These findings lead to a broader mechanistic understanding of 5-HT2A activation, which can catalyze the development of novel chemotypes with potential therapeutic utility and fewer side effects.

Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery

Understanding protein structures can facilitate the development of therapeutic drugs. Traditionally, protein structures have been determined through experimental approaches such as X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy. While these methods are effective and are considered the gold standard, they are very resource-intensive and time-consuming, ultimately limiting their scalability. However, with recent developments in computational biology and artificial intelligence (AI), the field of protein prediction has been revolutionized. Innovations like AlphaFold and RoseTTAFold enable protein structure predictions to be made directly from amino acid sequences with remarkable speed and accuracy. Despite the enormous enthusiasm associated with these newly developed AI-approaches, their true potential in structure-based drug discovery remains uncertain. In fact, although these algorithms generally predict overall protein structures well, essential details for computational ligand docking, such as the exact location of amino acid side chains within the binding pocket, are not predicted with the necessary accuracy. Additionally, docking methodologies are considered more as a hypothesis generator rather than a precise predictor of ligand-target interactions, and thus, usually identify many false-positive hits among only a few correctly predicted interactions. In this paper, we are reviewing the latest development in this cutting-edge field with emphasis on the GPCR target class to assess the potential role of AI approaches in structure-based drug discovery.

GPCR drug discovery: new agents, targets and indications

G protein-coupled receptors (GPCRs) form one of the largest drug target families, reflecting their involvement in numerous pathophysiological processes. In this Review, we analyse drug discovery trends for the GPCR superfamily, covering compounds, targets and indications that have reached regulatory approval or that are being investigated in clinical trials. We find that there are 516 approved drugs targeting GPCRs, making up 36% of all approved drugs. These drugs act on 121 GPCR targets, one-third of all non-sensory GPCRs. Furthermore, 337 agents targeting 133 GPCRs, including 30 novel targets, are being investigated in clinical trials. Notably, 165 of these agents are approved drugs being tested for additional indications and novel agents are increasingly allosteric modulators and biologics. Remarkably, diabetes and obesity drugs targeting GPCRs had sales of nearly US $30 billion in 2023 and the numbers of clinical trials for GPCR modulators in the metabolic diseases, oncology and immunology areas are increasing strongly. Finally, we highlight the potential of untapped target-disease associations and pathway-biased signalling. Overall, this Review provides an up-to-date reference for the drugged and potentially druggable GPCRome to inform future GPCR drug discovery and development.

A Molecular Representation to Identify Isofunctional Molecules

The challenges of drug discovery from hit identification to clinical development sometimes involves addressing scaffold hopping issues, in order to optimise molecular biological activity or ADME properties, or mitigate toxicology concerns of a drug candidate. Docking is usually viewed as the method of choice for identification of isofunctional molecules, i. e. highly dissimilar molecules that share common binding modes with a protein target. However, the structure of the protein may not be suitable for docking because of a low resolution, or may even be unknown. This problem is frequently encountered in the case of membrane proteins, although they constitute an important category of the druggable proteome. In such cases, ligand-based approaches offer promise but are often inadequate to handle large-step scaffold hopping, because they usually rely on molecular structure. Therefore, we propose the Interaction Fingerprints Profile (IFPP), a molecular representation that captures molecules binding modes based on docking experiments against a panel of diverse high-quality proteins structures. Evaluation on the LH benchmark demonstrates the interest of IFPP for identification of isofunctional molecules. Nevertheless, computation of IFPPs is expensive, which limits its scalability for screening very large molecular libraries. We propose to overcome this limitation by leveraging Metric Learning approaches, allowing fast estimation of molecules IFPP similarities, thus providing an efficient pre-screening strategy that in applicable to very large molecular libraries. Overall, our results suggest that IFPP provides an interesting and complementary tool alongside existing methods, in order to address challenging scaffold hopping problems effectively in drug discovery.

Molecular insights into the modulation of the 5HT2A receptor by serotonin, psilocin, and the G protein subunit Gqα

5HT2AR is a G-protein-coupled receptor that drives many neuronal functions and is a target for psychedelic drugs. Understanding ligand interactions and conformational transitions is essential for developing effective pharmaceuticals, but mechanistic details of 5HT2AR activation remain poorly understood. We utilized all-atom molecular dynamics simulations and free-energy calculations to investigate 5HT2AR's conformational dynamics upon binding to serotonin and psilocin. We show that the active state of 5HT2AR collapses to a closed state in the absence of Gqα, underscoring the importance of G-protein coupling. We discover an intermediate "partially-open" receptor conformation. Both ligands have higher binding affinities for the orthosteric than the extended binding pocket. These findings enhance our understanding of 5HT2AR's activation and may aid in developing novel therapeutics. Impact statement This study sheds light on 5HT2AR activation, revealing intermediate conformations and ligand dynamics. These insights could enhance drug development for neurological and psychiatric disorders, benefiting researchers and clinicians in pharmacology and neuroscience.

Ligand-Induced Biased Activation of GPCRs: Recent Advances and New Directions from In Silico Approaches

G-protein coupled receptors (GPCRs) are the largest family of membrane proteins engaged in transducing signals from the extracellular environment into the cell. GPCR-biased signaling occurs when two different ligands, sharing the same binding site, induce distinct signaling pathways. This selective signaling offers significant potential for the design of safer and more effective drugs. Although its molecular mechanism remains elusive, big efforts are made to try to explain this mechanism using a wide range of methods. Recent advances in computational techniques and AI technology have introduced a variety of simulations and machine learning tools that facilitate the modeling of GPCR signal transmission and the analysis of ligand-induced biased signaling. In this review, we present the current state of in silico approaches to elucidate the structural mechanism of GPCR-biased signaling. This includes molecular dynamics simulations that capture the main interactions causing the bias. We also highlight the major contributions and impacts of transmembrane domains, loops, and mutations in mediating biased signaling. Moreover, we discuss the impact of machine learning models on bias prediction and diffusion-based generative AI to design biased ligands. Ultimately, this review addresses the future directions for studying the biased signaling problem through AI approaches.

Molecular Glues: A New Approach to Modulating GPCR Signaling Bias

G-protein-coupled receptors (GPCRs) transmit an extracellular chemical/biological signal across the cell membrane, stimulating an array of intracellular signaling cascades. Canonically, these extracellular signaling molecules bind to the endogenous ligand pocket (orthosteric pocket), which stabilizes either an active or inactive conformational ensemble of the receptor. However, recent structural evidence indicates that small molecules can mediate the protein-protein interactions between the GPCR and their intracellular transducers. These small molecules are reminiscent of molecular glues and can be powerful tools for modulating GPCR signaling bias. In this Perspective, we will investigate the current structural information available on molecular glues and how they modulate GPCR signaling bias. We also examine the prospects of molecular glues and GPCR drug/probe design.

Classification of psychedelics and psychoactive drugs based on brain-wide imaging of cellular c-Fos expression

Psilocybin, ketamine, and MDMA are psychoactive compounds that exert behavioral effects with distinguishable but also overlapping features. The growing interest in using these compounds as therapeutics necessitates preclinical assays that can accurately screen psychedelics and related analogs. We posit that a promising approach may be to measure drug action on markers of neural plasticity in native brain tissues. We therefore developed a pipeline for drug classification using light sheet fluorescence microscopy of immediate early gene expression at cellular resolution followed by machine learning. We tested male and female mice with a panel of drugs, including psilocybin, ketamine, 5-MeO-DMT, 6-fluoro-DET, MDMA, acute fluoxetine, chronic fluoxetine, and vehicle. In one-versus-rest classification, the exact drug was identified with 67% accuracy, significantly above the chance level of 12.5%. In one-versus-one classifications, psilocybin was discriminated from 5-MeO-DMT, ketamine, MDMA, or acute fluoxetine with >95% accuracy. We used Shapley additive explanation to pinpoint the brain regions driving the machine learning predictions. Our results suggest a unique approach for characterizing and validating psychoactive drugs with psychedelic properties.

Ketamine induces plasticity in a norepinephrine-astroglial circuit to promote behavioral perseverance

Transient exposure to ketamine can trigger lasting changes in behavior and mood. We found that brief ketamine exposure causes long-term suppression of futility-induced passivity in larval zebrafish, reversing the "giving-up" response that normally occurs when swimming fails to cause forward movement. Whole-brain imaging revealed that ketamine hyperactivates the norepinephrine-astroglia circuit responsible for passivity. After ketamine washout, this circuit exhibits hyposensitivity to futility, leading to long-term increased perseverance. Pharmacological, chemogenetic, and optogenetic manipulations show that norepinephrine and astrocytes are necessary and sufficient for ketamine's long-term perseverance-enhancing aftereffects. In vivo calcium imaging revealed that astrocytes in adult mouse cortex are similarly activated during futility in the tail suspension test and that acute ketamine exposure also induces astrocyte hyperactivation. The cross-species conservation of ketamine's modulation of noradrenergic-astroglial circuits and evidence that plasticity in this pathway can alter the behavioral response to futility hold promise for identifying new strategies to treat affective disorders.

SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction

Accurately predicting protein-ligand interactions is crucial for understanding cellular processes. We introduce SurfDock, a deep-learning method that addresses this challenge by integrating protein sequence, three-dimensional structural graphs and surface-level features into an equivariant architecture. SurfDock employs a generative diffusion model on a non-Euclidean manifold, optimizing molecular translations, rotations and torsions to generate reliable binding poses. Our extensive evaluations across various benchmarks demonstrate SurfDock's superiority over existing methods in docking success rates and adherence to physical constraints. It also exhibits remarkable generalizability to unseen proteins and predicted apo structures, while achieving state-of-the-art performance in virtual screening tasks. In a real-world application, SurfDock identified seven novel hit molecules in a virtual screening project targeting aldehyde dehydrogenase 1B1, a key enzyme in cellular metabolism. This showcases SurfDock's ability to elucidate molecular mechanisms underlying cellular processes. These results highlight SurfDock's potential as a transformative tool in structural biology, offering enhanced accuracy, physical plausibility and practical applicability in understanding protein-ligand interactions.

Active Learning to Select the Most Suitable Reagents and One-Step Organic Chemistry Reactions for Prioritizing Target-Specific Hits from Ultralarge Chemical Spaces

Designing chemically novel and synthesizable ligands from the largest possible chemical space is a major issue in modern drug discovery to identify early hits that are easily amenable to medicinal chemistry optimization. Starting from the sole three-dimensional structure of a protein binding site, we herewith describe a fully automated active learning protocol to propose the commercial chemical reagents and one-step organic chemistry reactions necessary to enumerate target-specific primary hits from ultralarge chemical spaces. When applied in different scenarios (single transform and multiple transforms) addressing chemical spaces of various sizes (from 670 million to 4.5 billion compounds), the method was able to recover up to 98% of virtual hits discovered by an exhaustive docking-based approach while scanning only 5% of the full chemical space. It is therefore applicable to the structure-based screening of trillion-sized chemical spaces at a very high throughput with minimal computational resources.

Docking 14 million virtual isoquinuclidines against the mu and kappa opioid receptors reveals dual antagonists-inverse agonists with reduced withdrawal effects

Large library docking of tangible molecules has revealed potent ligands across many targets. While make-on-demand libraries now exceed 75 billion enumerated molecules, their synthetic routes are dominated by a few reaction types, reducing diversity and inevitably leaving many interesting bioactive-like chemotypes unexplored. Here, we investigate the large-scale enumeration and targeted docking of isoquinuclidines. These "natural-product-like" molecules are rare in the current libraries and are functionally congested, making them interesting as receptor probes. Using a modular, four-component reaction scheme, we built and docked a virtual library of over 14.6 million isoquinuclidines against both the μ- and κ-opioid receptors (MOR and KOR, respectively). Synthesis and experimental testing of 18 prioritized compounds found nine ligands with low μM affinities. Structure-based optimization revealed low- and sub-nM antagonists and inverse agonists targeting both receptors. Cryo-electron microscopy (cryoEM) structures illuminate the origins of activity on each target. In mouse behavioral studies, a potent member of the series with joint MOR-antagonist and KOR-inverse-agonist activity reversed morphine-induced analgesia, phenocopying the MOR-selective anti-overdose agent naloxone. Encouragingly, the new molecule induced less severe opioid-induced withdrawal symptoms compared to naloxone during withdrawal precipitation, and did not induce conditioned-place aversion, likely reflecting a reduction of dysphoria due to the compound's KOR-inverse agonism. The strengths and weaknesses of bespoke library docking, and of docking for opioid receptor polypharmacology, will be considered.

Therapeutic Potential of Psychedelic Drugs: Navigating High Hopes, Strong Claims, Weak Evidence, and Big Money

Therapeutic claims about many psychedelic drugs have not been evaluated in any studies of even modest rigor. The science of psychedelic drugs is strengthening, however, making it easier to differentiate some promising findings amid the hype that suffuses this research area. Ketamine has risks of adverse side effects (e.g., addiction and cystitis), but multiple studies suggest it can benefit individuals with treatment-resistant depression. Other therapeutic signals from psychedelic drug research that merit rigorous replication studies include 3,4-Methylenedioxymethamphetamine (MDMA) for post-traumatic stress disorder (PTSD) and psilocybin for depression, end of life dysphoria, and alcohol use disorder. The precise mechanisms through which psychedelic drugs can produce benefit and harm are not fully understood. Rigorous research is the best path forward for evaluating the therapeutic potential and mechanisms of psychedelic drugs. Policies governing the clinical use of these drugs should be informed by evidence and prioritize the protection of public health over the profit motive.

A novel heuristic of rigid docking scores positively correlates with full-length nuclear receptor LRH-1 regulation

The nuclear receptor Liver Receptor Homolog-1 (LRH-1, NR5A2) is a ligand-regulated transcription factor and validated drug target for several human diseases. LRH-1 activation is regulated by small molecule ligands, which bind to the ligand binding domain (LBD) within the full-length LRH-1. We recently identified 57 compounds that bind LRH-1, and unexpectedly found these compounds regulated either the isolated LBD, or the full-length LRH-1 in cells, with little overlap. Here, we correlated compound binding energy from a single rigid-body scoring function with full-length LRH-1 activity in cells. Although docking scores of the 57 hit compounds did not correlate with LRH-1 regulation in wet lab assays, a subset of the compounds had large differences in binding energy docked to the isolated LBD vs. full-length LRH-1, which we used to empirically derive a new metric of the docking scores we call "ΔΔG". Initial regressions, correlations and contingency analyses all suggest compounds with high ΔΔG values more frequently regulated LRH-1 in wet lab assays. We then docked all 57 compounds to 18 separate crystal structures of LRH-1 to obtain averaged ΔΔG values for each compound, which robustly and reproducibly associated with full-length LRH-1 activity in cells. Network analyses on the 18 crystal structures of LRH-1 suggest unique communication paths exist between the subsets of LRH-1 crystal structures that produced high vs. low ΔΔG values, identifying a structural relationship between ΔΔG and the position of Helix 6, a previously established regulatory helix important for LRH-1 regulation. Together, these data suggest rigid-body computational docking can be used to quickly calculate ΔΔG, which positively correlated with the ability of these 57 hit compounds to regulate full-length LRH-1 in cell-based assays. We propose ΔΔG as a novel computational tool that can be applied to LRH-1 drug screens to prioritize compounds for resource-intense secondary screening.

CACHE Challenge #1: Targeting the WDR Domain of LRRK2, A Parkinson's Disease Associated Protein

The CACHE challenges are a series of prospective benchmarking exercises to evaluate progress in the field of computational hit-finding. Here we report the results of the inaugural CACHE challenge in which 23 computational teams each selected up to 100 commercially available compounds that they predicted would bind to the WDR domain of the Parkinson's disease target LRRK2, a domain with no known ligand and only an apo structure in the PDB. The lack of known binding data and presumably low druggability of the target is a challenge to computational hit finding methods. Of the 1955 molecules predicted by participants in Round 1 of the challenge, 73 were found to bind to LRRK2 in an SPR assay with a KD lower than 150 μM. These 73 molecules were advanced to the Round 2 hit expansion phase, where computational teams each selected up to 50 analogs. Binding was observed in two orthogonal assays for seven chemically diverse series, with affinities ranging from 18 to 140 μM. The seven successful computational workflows varied in their screening strategies and techniques. Three used molecular dynamics to produce a conformational ensemble of the targeted site, three included a fragment docking step, three implemented a generative design strategy and five used one or more deep learning steps. CACHE #1 reflects a highly exploratory phase in computational drug design where participants adopted strikingly diverging screening strategies. Machine learning-accelerated methods achieved similar results to brute force (e.g., exhaustive) docking. First-in-class, experimentally confirmed compounds were rare and weakly potent, indicating that recent advances are not sufficient to effectively address challenging targets.

Classification of psychedelics and psychoactive drugs based on brain-wide imaging of cellular c-Fos expression

Psilocybin, ketamine, and MDMA are psychoactive compounds that exert behavioral effects with distinguishable but also overlapping features. The growing interest in using these compounds as therapeutics necessitates preclinical assays that can accurately screen psychedelics and related analogs. We posit that a promising approach may be to measure drug action on markers of neural plasticity in native brain tissues. We therefore developed a pipeline for drug classification using light sheet fluorescence microscopy of immediate early gene expression at cellular resolution followed by machine learning. We tested male and female mice with a panel of drugs, including psilocybin, ketamine, 5-MeO-DMT, 6-fluoro-DET, MDMA, acute fluoxetine, chronic fluoxetine, and vehicle. In one-versus-rest classification, the exact drug was identified with 67% accuracy, significantly above the chance level of 12.5%. In one-versus-one classifications, psilocybin was discriminated from 5-MeO-DMT, ketamine, MDMA, or acute fluoxetine with >95% accuracy. We used Shapley additive explanation to pinpoint the brain regions driving the machine learning predictions. Our results support a novel approach for characterizing and validating psychoactive drugs with psychedelic properties.

vScreenML v2.0: Improved Machine Learning Classification for Reducing False Positives in Structure-Based Virtual Screening

The enthusiastic adoption of make-on-demand chemical libraries for virtual screening has highlighted the need for methods that deliver improved hit-finding discovery rates. Traditional virtual screening methods are often inaccurate, with most compounds nominated in a virtual screen not engaging the intended target protein to any detectable extent. Emerging machine learning approaches have made significant progress in this regard, including our previously described tool vScreenML. The broad adoption of vScreenML was hindered by its challenging usability and dependencies on certain obsolete or proprietary software packages. Here, we introduce vScreenML 2.0 to address each of these limitations with a streamlined Python implementation. Through careful benchmarks, we show that vScreenML 2.0 outperforms other widely used tools for virtual screening hit discovery.

Isolation of psychedelic-responsive neurons underlying anxiolytic behavioral states

Psychedelics hold promise as alternate treatments for neuropsychiatric disorders. However, the neural mechanisms by which they drive adaptive behavioral effects remain unclear. We isolated the specific neurons modulated by a psychedelic to determine their role in driving behavior. Using a light- and calcium-dependent activity integrator, we genetically tagged psychedelic-responsive neurons in the medial prefrontal cortex (mPFC) of mice. Single-nucleus RNA sequencing revealed that the psychedelic drove network-level activation of multiple cell types beyond just those expressing 5-hydroxytryptamine 2A receptors. We labeled psychedelic-responsive mPFC neurons with an excitatory channelrhodopsin to enable their targeted manipulation. We found that reactivation of these cells recapitulated the anxiolytic effects of the psychedelic without driving its hallucinogenic-like effects. These findings reveal essential insight into the cell-type-specific mechanisms underlying psychedelic-induced behavioral states.

Pyramidal cell types and 5-HT2A receptors are essential for psilocybin's lasting drug action

Psilocybin is a serotonergic psychedelic with therapeutic potential for treating mental illnesses1-4. At the cellular level, psychedelics induce structural neural plasticity5,6, exemplified by the drug-evoked growth and remodeling of dendritic spines in cortical pyramidal cells7-9. A key question is how these cellular modifications map onto cell type-specific circuits to produce psychedelics' behavioral actions10. Here, we use in vivo optical imaging, chemogenetic perturbation, and cell type-specific electrophysiology to investigate the impact of psilocybin on the two main types of pyramidal cells in the mouse medial frontal cortex. We find that a single dose of psilocybin increased the density of dendritic spines in both the subcortical-projecting, pyramidal tract (PT) and intratelencephalic (IT) cell types. Behaviorally, silencing the PT neurons eliminates psilocybin's ability to ameliorate stress-related phenotypes, whereas silencing IT neurons has no detectable effect. In PT neurons only, psilocybin boosts synaptic calcium transients and elevates firing rates acutely after administration. Targeted knockout of 5-HT2A receptors abolishes psilocybin's effects on stress-related behavior and structural plasticity. Collectively these results identify a pyramidal cell type and the 5-HT2A receptor in the medial frontal cortex as playing essential roles for psilocybin's long-term drug action.

Molecular and structural insights into the 5-HT2C receptor as a therapeutic target for substance use disorders

Substance use disorder (SUD) is a chronic condition, with maintained abuse of a substance leading to physiological and psychological alterations and often changes in cognitive and social behaviours. Current therapies include psychotherapy coupled with medication; however, high relapse rates reveal the shortcomings of these therapies. The signalling, expression profile, and neurological function of the serotonin 2C receptor (5-HT2C receptor) make it a candidate of interest for the treatment of SUD. Recently, psychedelics, which broadly act at 5-HT2 receptors, have indicated potential for the treatment of SUD, implicating the 5-HT2C receptor. The modern psychedelic movement has rekindled interest in the 5-HT2C receptor, resulting in many new studies, especially structural analyses. This review explores the structural, molecular and cellular mechanisms governing 5-HT2C receptor function in the context of SUD. This provides the basis of the preclinical and clinical evidence for their role in SUD and highlights the potential for future exploration.

Low-dose psilocybin in short-lasting unilateral neuralgiform headache attacks: results from an open-label phase Ib ascending dose study

BACKGROUND

Short-lasting unilateral neuralgiform headache attacks (SUNHA) are trigeminal autonomic cephalalgias that feature intense and recurrent paroxysms of pain and autonomic symptoms. Many patients are left with debilitating symptoms despite best-available treatment. Psychedelics, such as the serotonin 2A partial agonist psilocybin, have shown promise in related disorders such as migraine and cluster headache. In this open-label phase Ib ascending dose study, we aimed to assess the effects of low-dose oral psilocybin with psychological support in six to 12 patients with chronic SUNHA. Study objectives were to determine effects on cognition, as well as safety, tolerability, and effects on headache severity and frequency.

METHODS

Oral psilocybin in ascending doses of 5, 7.5, and 10 mg (one dose per session; three dosing sessions in total) were administered. Cognition was assessed via the Cambridge Neuropsychological Tests Automated Battery. Headache attacks were assessed via headache diaries and the six-item Headache Impact Test (HIT-6). Subjective dose intensity was assessed via the five-Dimensional Altered States of Consciousness Questionnaire (5D-ASC). The study was terminated early due to recruitment difficulties; four patients were enrolled, three of whom were study completers. Post hoc, we undertook a thematic analysis of the applicable free-text clinical trial notes from the dosing and subsequent visits (n = 22). An inductive method was employed to establish emergent themes.

RESULTS

No significant adverse events were recorded. We were unable to collect data as planned on cognitive function during the acute experience due to high ratings of subjective dose intensity (mean 5D-ASC scores 37.8-45.7). The impact of the headaches remained severe throughout the duration of the trial (HIT-6 mean scores 64.3-65.7). There were limited effects on headache duration and severity based on the diaries; however, mean daily attack frequency decreased by >50% in two participants at final follow-up (22.9 to 11.0 and 56.4 to 28.0, respectively). Completing participants and their clinicians recorded "much" (two participants) or "minimal" improvements (one participant) at final follow-up via the Clinical Global Impression rating scale. Thematic analysis indicated that psychological insights were key features of participants' experience; these insights included re-configured relationships to their headache pain.

CONCLUSION

The study met with recruitment difficulties and cognition could not be assessed during the acute experience due to subjective dose intensity, likely mediated in part by expectancy effects. The clinical results provide no conclusive evidence for the use of psilocybin in SUNHA. We suggest that accounting for psychological factors in chronic SUNHA may be an important facet of treatment.

Preclinical models for evaluating psychedelics in the treatment of major depressive disorder

Psychedelic drugs have seen a resurgence in interest as a next generation of psychiatric medicines with potential as rapid-acting antidepressants (RAADs). Despite promising early clinical trials, the mechanisms which underlie the effects of psychedelics are poorly understood. For example, key questions such as whether antidepressant and psychedelic effects involve related or independent mechanisms are unresolved. Preclinical studies in relevant animal models are key to understanding the pharmacology of psychedelics and translating these findings to explain efficacy and safety in patients. Understanding the mechanisms of action associated with the behavioural effects of psychedelic drugs can also support the identification of novel drug targets and more effective treatments. Here we review the behavioural approaches currently used to quantify the psychedelic and antidepressant effects of psychedelic drugs. We discuss conceptual and methodological issues, the importance of using clinically relevant doses and the need to consider possible sex differences in preclinical psychedelic studies.

Molecular Dynamics (MD) Simulations Provide Insights into the Activation Mechanisms of 5-HT2A Receptors

Recent breakthroughs in the determination of atomic resolution 3-D cryo-electron microscopy structures of membrane proteins present an unprecedented opportunity for drug discovery. Structure-based drug discovery utilizing in silico methods enables the study of dynamic connectivity of stable conformations induced by the drug in achieving its effect. With the ever-expanding computational power, simulations of this type reveal protein dynamics in the nano-, micro-, and even millisecond time scales. In the present study, aiming to characterize the protein dynamics of the 5HT2A receptor stimulated by ligands (agonist/antagonist), we performed 1 µs MD simulations on 5HT2A/DOI (agonist), 5HT2A/GSK215083 (antagonist), and 5HT2A (APO, no ligand) systems. The crystal structure of 5HT2A/zotepine (antagonist) (PDB: 6A94) was used to set up the simulation systems in a lipid bilayer environment. We found the monitoring of the ionic lock residue pair (R3.50-E6.30) of 5HT2A in MD simulations to be a good approximation of the effects of agonists (ionic lock breakage) or antagonists (ionic lock formation) on receptor activation. We further performed analyses of the MD trajectories, including Principal Component Analysis (PCA), hydrogen bond, salt bridge, and hydrophobic interaction network analyses, and correlation between residues to identify key elements of receptor activation. Our results suggest that in order to trigger receptor activation, DOI must interact with 5HT2A through residues V5.39, G5.42, S5.43, and S5.46 on TM5, inducing significant conformational changes in the backbone angles of G5.42 and S5.43. DOI also interacted with residues W6.48 (toggle switch) and F6.51 on TM6, causing major conformational shifts in the backbone angles of F6.44 and V6.45. These structural changes were transmitted to the intracellular ends of TM5, TM6, and ICL3, resulting in the breaking of the ionic lock and subsequent G protein activation. The studies could be helpful in future design of selective agonists/antagonists for various serotonin receptors (5HT1A, 5HT2A, 5HT2B, 5HT2C, and 5HT7) involved in detrimental disorders, such as addiction and schizophrenia.

Recent advances from computer-aided drug design to artificial intelligence drug design

Computer-aided drug design (CADD), a cornerstone of modern drug discovery, can predict how a molecular structure relates to its activity and interacts with its target using structure-based and ligand-based methods. Fueled by ever-increasing data availability and continuous model optimization, artificial intelligence drug design (AIDD), as an enhanced iteration of CADD, has thrived in the past decade. AIDD demonstrates unprecedented opportunities in protein folding, property prediction, and molecular generation. It can also facilitate target identification, high-throughput screening (HTS), and synthetic route prediction. With AIDD involved, the process of drug discovery is greatly accelerated. Notably, AIDD offers the potential to explore uncharted territories of chemical space beyond current knowledge. In this perspective, we began by briefly outlining the main workflows and components of CADD. Then through showcasing exemplary cases driven by AIDD in recent years, we describe the evolving role of artificial intelligence (AI) in drug discovery from three distinct stages, that is, chemical library screening, linker generation, and de novo molecular generation. In this process, we attempted to draw comparisons between the features of CADD and AIDD.

Chemistry/structural biology of psychedelic drugs and their receptor(s)

This brief review highlights some of the structure-activity relationships of classic serotonergic psychedelics. In particular, we discuss structural features of three chemotypes: phenethylamines, ergolines and certain tryptamines, which possess psychedelic activity in humans. Where they are known, we point out the underlying molecular mechanisms utilized by each of the three chemotypes of psychedelic molecules. With a focus on the 5-HT2A receptor subtype, a G-protein coupled receptor known to be the primary target of psychedelics, we refer to several X-ray and cryoEM structures, with a variety of ligands bound, to illustrate the underlying atomistic basis for some of the known pharmacological observations of psychedelic drug actions.

Molecular glues as potential GPCR therapeutics

"Molecular Glues" are defined as small molecules that can either be endogenous or synthetic which promote interactions between proteins at their interface. Allosteric modulators, specifically GPCR allosteric modulators, can promote both the association and the dissociation of a given receptor's transducer but accomplishes this "at a distance" from the interface. However, recent structures of GPCR G protein complexes in the presence of allosteric modulators indicate that some GPCR allosteric modulators can act as "molecular glues" interacting with both the receptor and the transducer at the interface biasing transducer signaling in both a positive and negative manner depending on the transducer. Given these phenomena we discuss the implications for this class of allosteric modulators to be used as molecular tools and for future drug development.

GJB2 Promotes HCC Progression by Activating Glycolysis Through Cytoplasmic Translocation and Generating a Suppressive Tumor Microenvironment Based on Single Cell RNA Sequencing

Despite substantial breakthroughs in the treatment of hepatocellular carcinoma (HCC) in recent years, many patients are diagnosed in the middle or late stages, denying them the option for surgical excision. Therefore, it is of great importance to find effective therapeutic targets of HCC. In this study, it is found that Gap junction protein beta-2 (GJB2) is highly enriched in malignant cells based on single-cell RNA sequencing and higher expression of GJB2 indicates a worse prognosis. The localization of GJB2 in HCC cancer cells is changed compared with normal liver tissue. In cancer cells, GJB2 tends to be located in the cytoplasm and nucleus, while in normal tissues, GJB2 is mainly located on the cell membrane. GJB2 is related to glycolysis, promoting NF-κB pathway via inducing the ubiquitination degradation of IκBa, and activating HIF-1α/GLUT-1/PD-L1 pathway. In addition, GJB2 knockdown reshapes tumor immune microenvironment and Salvianolic acid B inhibits the activity of GJB2. In conclusion, GJB2 promotes HCC progression by activating glycolysis through cytoplasmic translocation and generating a suppressive tumor microenvironment. Salvianolic acid B inhibits the expression of GJB2 and enhances the sensitivity of anti-PD1 therapy, which may provide insights into the development of novel combination therapeutic strategies for HCC.

Identifying Artifacts from Large Library Docking

While large library docking has discovered potent ligands for multiple targets, as the libraries have grown the hit lists can become dominated by rare artifacts that cheat our scoring functions. Here, we investigate rescoring top-ranked docked molecules with orthogonal methods to identify these artifacts, exploring implicit solvent models and absolute binding free energy perturbation as cross-filters. In retrospective studies, this approach deprioritized high-ranking nonbinders for nine targets while leaving true ligands relatively unaffected. We tested the method prospectively against hits from docking against AmpC β-lactamase. We prioritized 128 high-ranking molecules for synthesis and testing, a mixture of 39 molecules flagged as likely cheaters and 89 that were plausible inhibitors. None of the predicted cheating compounds inhibited AmpC detectably, while 57% of the 89 plausible compounds did so. As our libraries continue to grow, deprioritizing docking artifacts by rescoring with orthogonal methods may find wide use.

Progress on the development of Class A GPCR-biased ligands

Class A G protein-coupled receptors (GPCRs) continue to garner interest for their essential roles in cell signalling and their importance as drug targets. Although numerous drugs in the clinic target these receptors, over 60% GPCRs remain unexploited. Moreover, the adverse effects triggered by the available unbiased GPCR modulators, limit their use and therapeutic value. In this context, the elucidation of biased signalling has opened up new pharmacological avenues holding promise for safer therapeutics. Functionally selective ligands favour receptor conformations facilitating the recruitment of specific effectors and the modulation of the associated pathways. This review surveys the current drug discovery landscape of GPCR-biased modulators with a focus on recent advances. Understanding the biological effects of this preferential coupling is at different stages depending on the Class A GPCR family. Therefore, with a focus on individual GPCR families, we present a compilation of the functionally selective modulators reported over the past few years. In doing so, we dissect their therapeutic relevance, molecular determinants and potential clinical applications.

CCNP Innovations in Neuropsychopharmacology Award: The psychopharmacology of psychedelics: where the brain meets spirituality

For 3000 years, psychedelics have been used in religious contexts to enhance spiritual thinking, well-being, and a sense of community. In the last few years, a renaissance in the use of psychedelic drugs for mental disorders has occurred in Western society; consequently, a pressing scientific need to elucidate the intricate mechanisms underlying their actions has arisen. Psychedelics mainly bind to serotonin (5-HT) receptors, particularly 5-HT2A receptors, but may also bind to other receptors. Unlike conventional psychotropic drugs used in psychiatry, psychedelics introduce a distinctive complexity. They not only engage in receptor activation, but also exert influence over specific neural circuits, thereby facilitating transformative cognitive experiences and fostering what many have identified as a spiritual contemplation or mystical experience. This comprehensive review describes clinical studies that have examined the propensity of psychedelics to enhance spiritual, mystical, and transcendent cognitive states. This multifaceted nature, encompassing diverse components and paradigms, necessitates careful consideration during the investigation of psychedelic mechanisms of action to avoid oversimplification. The present review endeavours to elucidate the mechanisms underlying the actions of 2 principal psychedelic substances, psilocybin and lysergic acid diethylamide (LSD), with a focus on monoamine and glutamate receptor mechanisms; molecular aspects, such as neuroplasticity and epigenetics; as well as the impact of psychedelics on brain circuits, including the default mode network and the cortico-striato-thalamo-cortical network. Given their distinctive and intricate mechanisms of action, psychedelics necessitate a novel conceptual framework in psychiatry, offering insight into the treatment of mental health disorders and facilitating the integration of the realms of brain, mind, and spirituality.

Caught between a ROCK and a hard place: current challenges in structure-based drug design

The discipline of structure-based drug design (SBDD) is several decades old and it is tempting to think that the proliferation of experimental structures for many drug targets might make computer-aided drug design (CADD) straightforward. However, this is far from true. In this review, we illustrate some of the challenges that CADD scientists face every day in their work, even now. We use Rho-associated protein kinase (ROCK), and public domain structures and data, as an example to illustrate some of the challenges we have experienced during our project targeting this protein. We hope that this will help to prevent unrealistic expectations of what CADD can accomplish and to educate non-CADD scientists regarding the challenges still facing their CADD colleagues.

Psilocybin as a lead candidate molecule in preclinical therapeutic studies of psychiatric disorders: A systematic review

Psilocybin is the main psychoactive compound found in hallucinogenic/magic mushrooms and can bind to both serotonergic and tropomyosin receptor kinase b (TrkB) receptors. Psilocybin has begun to show efficacy for a range of neuropsychiatric conditions, including treatment-resistant depression and anxiety disorders; however, neurobiological mechanisms are still being elucidated. Clinical research has found that psilocybin can alter functional connectivity patterns in human brains, which is often associated with therapeutic outcomes. However, preclinical research affords the opportunity to assess the potential cellular mechanisms by which psilocybin may exert its therapeutic effects. Preclinical rodent models can also facilitate a more tightly controlled experimental context and minimise placebo effects. Furthermore, where there is a rationale, preclinical researchers can investigate psilocybin administration in neuropsychiatric conditions that have not yet been researched clinically. As a result, we have systematically reviewed the knowledge base, identifying 82 preclinical studies which were screened based on specific criteria. This resulted in the exclusion of 44 articles, with 34 articles being included in the main review and another 2 articles included as Supporting Information materials. We found that psilocybin shows promise as a lead candidate molecule for treating a variety of neuropsychiatric conditions, albeit showing the most efficacy for depression. We discuss the experimental findings, and identify possible mechanisms whereby psilocybin could invoke therapeutic changes. Furthermore, we critically evaluate the between-study heterogeneity and possible future research avenues. Our review suggests that preclinical rodent models can provide valid and translatable tools for researching novel psilocybin-induced molecular and cellular mechanisms, and therapeutic outcomes.

Structure-based virtual screening of vast chemical space as a starting point for drug discovery

Structure-based virtual screening aims to find molecules forming favorable interactions with a biological macromolecule using computational models of complexes. The recent surge of commercially available chemical space provides the opportunity to search for ligands of therapeutic targets among billions of compounds. This review offers a compact overview of structure-based virtual screens of vast chemical spaces, highlighting successful applications in early drug discovery for therapeutically important targets such as G protein-coupled receptors and viral enzymes. Emphasis is placed on strategies to explore ultra-large chemical libraries and synergies with emerging machine learning techniques. The current opportunities and future challenges of virtual screening are discussed, indicating that this approach will play an important role in the next-generation drug discovery pipeline.

Identification of Psychoplastogenic Tropanes Lacking Muscarinic Activity

Tropane-containing small molecules like scopolamine are a promising class of psychoplastogens. However, their potent antagonism of all muscarinic receptor subtypes presents the potential for undesirable anticholinergic side effects. In an effort to decouple their neuroplasticity-promoting effects from their muscarinic activity, we performed phenotypic structure-activity relationship studies across a variety of structurally distinct subclasses of tropanes. We discovered several novel tropanes capable of significantly increasing cortical neuronal growth while exhibiting drastically reduced activity at all muscarinic receptor subtypes compared to scopolamine.

Identifying Artifacts from Large Library Docking

While large library docking has discovered potent ligands for multiple targets, as the libraries have grown, the very top of the hit-lists can become populated with artifacts that cheat our scoring functions. Though these cheating molecules are rare, they become ever-more dominant with library growth. Here, we investigate rescoring top-ranked molecules from docking screens with orthogonal methods to identify these artifacts, exploring implicit solvent models and absolute binding free energy perturbation (AB-FEP) as cross-filters. In retrospective studies, this approach deprioritized high-ranking non-binders for nine targets while leaving true ligands relatively unaffected. We tested the method prospectively against results from large library docking AmpC β-lactamase. From the very top of the docking hit lists, we prioritized 128 molecules for synthesis and experimental testing, a mixture of 39 molecules that rescoring flagged as likely cheaters and another 89 that were plausible true actives. None of the 39 predicted cheating compounds inhibited AmpC up to 200 μ M in enzyme assays, while 57% of the 89 plausible true actives did do so, with 19 of them inhibiting the enzyme with apparentK i values better than 50 μ M . As our libraries continue to grow, a strategy of catching docking artifacts by rescoring with orthogonal methods may find wide use in the field.

Catalytic Asymmetric Construction of Chiral Polysubstituted 3-Azabicyclo[3.1.1]heptanes by Copper-Catalyzed Stereoselective Formal [4π+2σ] Cycloaddition

The direct construction of bioisosteric compounds enriched in Csp3 content represents an attractive and dependable approach to imbuing biologically active molecules with enhanced three-dimensional characteristics, finding wide utility across the synthetic and medicinal chemistry community. Despite recent advancements in the synthesis of (aza)-bicyclo[3.1.1]heptanes (BCHeps and aza-BCHeps), which serve as meta-substituted (aza)-arene bioisosteres, the enantioselective assembly of chiral 3-aza-BCHeps remains a coveted goal yet to be achieved. Here, we disclose an unprecedented copper-catalyzed asymmetric formal [4π+2σ] cycloaddition of bicyclo[1.1.0]butanes (BCBs) and azomethine ylides, furnishing a diverse array of enantioenriched 3-aza-BCHeps with exceptional levels of diastereo- and enantioselectivites (51 examples, all >20:1 dr, mostly 97-99% ee). Both mono- and disubstituted BCBs are well compatible with this protocol, offering an enticing route for the efficient synthesis of challenging tetrasubstituted bicyclic products bearing two quaternary centers. The synthetic significance of this methodology is further demonstrated by the successful preparation of several piperidine drug analogues.

Discovery and in vitro Evaluation of Novel Serotonin 5-HT2A Receptor Ligands Identified Through Virtual Screening

The 5-HT2A receptor is a molecular target of high pharmacological importance. Ligands of this protein, particularly atypical antipsychotics, are useful in the treatment of numerous mental disorders, including schizophrenia and major depressive disorder. Structure-based virtual screening using a 5-HT2A receptor complex was performed to identify novel ligands for the 5-HT2A receptor, serving as potential antidepressants. From the Enamine screening library, containing over 4 million compounds, 48 molecules were selected for subsequent experimental validation. These compounds were tested against the 5-HT2A receptor in radioligand binding assays. From the tested batch, six molecules were identified as ligands of the main molecular target and were forwarded to a more detailed in vitro profiling. This included radioligand binding assays at 5-HT1A, 5-HT7, and D2 receptors and functional studies at 5-HT2A receptors. These compounds were confirmed to show a binding affinity for at least one of the targets tested in vitro. The success rate for the inactive template-based screening reached 17 %, while it was 9 % for the active template-based screening. Similarity and fragment analysis indicated the structural novelty of the identified compounds. Pharmacokinetics for these molecules was determined using in silico approaches.

Structure-based discovery of CFTR potentiators and inhibitors

The cystic fibrosis transmembrane conductance regulator (CFTR) is a crucial ion channel whose loss of function leads to cystic fibrosis, whereas its hyperactivation leads to secretory diarrhea. Small molecules that improve CFTR folding (correctors) or function (potentiators) are clinically available. However, the only potentiator, ivacaftor, has suboptimal pharmacokinetics and inhibitors have yet to be clinically developed. Here, we combine molecular docking, electrophysiology, cryo-EM, and medicinal chemistry to identify CFTR modulators. We docked ∼155 million molecules into the potentiator site on CFTR, synthesized 53 test ligands, and used structure-based optimization to identify candidate modulators. This approach uncovered mid-nanomolar potentiators, as well as inhibitors, that bind to the same allosteric site. These molecules represent potential leads for the development of more effective drugs for cystic fibrosis and secretory diarrhea, demonstrating the feasibility of large-scale docking for ion channel drug discovery.

Psilocybin for the treatment of Alzheimer's disease

Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment and a prominent contributor to dementia. The scarcity of available therapies for AD accentuates the exigency for innovative treatment modalities. Psilocybin, a psychoactive alkaloid intrinsic to hallucinogenic mushrooms, has garnered attention within the neuropsychiatric realm due to its established safety and efficacy in treating depression. Nonetheless, its potential as a therapeutic avenue for AD remains largely uncharted. This comprehensive review endeavors to encapsulate the pharmacological effects of psilocybin while elucidating the existing evidence concerning its potential mechanisms contributing to a positive impact on AD. Specifically, the active metabolite of psilocybin, psilocin, elicits its effects through the modulation of the 5-hydroxytryptamine 2A receptor (5-HT2A receptor). This modulation causes heightened neural plasticity, diminished inflammation, and improvements in cognitive functions such as creativity, cognitive flexibility, and emotional facial recognition. Noteworthy is psilocybin's promising role in mitigating anxiety and depression symptoms in AD patients. Acknowledging the attendant adverse reactions, we proffer strategies aimed at tempering or mitigating its hallucinogenic effects. Moreover, we broach the ethical and legal dimensions inherent in psilocybin's exploration for AD treatment. By traversing these avenues, We propose therapeutic potential of psilocybin in the nuanced management of Alzheimer's disease.