Virtual discovery of melatonin receptor ligands to modulate circadian rhythms

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.

The impact of Library Size and Scale of Testing on Virtual Screening

Virtual libraries for ligand discovery have recently increased 10,000-fold, and this is thought to have improved hit rates and potencies from library docking. This idea has not, however, been experimentally tested in direct comparisons of larger-vs-smaller libraries. Meanwhile, though libraries have exploded, the scale of experimental testing has little changed, with often only dozens of high-ranked molecules investigated, making interpretation of hit rates and affinities uncertain. Accordingly, we docked a 1.7 billion molecule virtual library against the model enzyme AmpC β-lactamase, testing 1,521 new molecules and comparing the results to the same screen with a library of 99 million molecules, where only 44 molecules were tested. Encouragingly, the larger screen outperformed the smaller one: hit rates improved by two-fold, more new scaffolds were discovered, and potency improved. Overall, 50-fold more inhibitors were found, supporting the idea that there are many more compounds to be discovered than are being tested. With so many compounds evaluated, we could ask how the results vary with number tested, sampling smaller sets at random from the 1521. Hit rates and affinities were highly variable when we only sampled dozens of molecules, and it was only when we included several hundred molecules that results converged. As docking scores improved, so too did the likelihood of a molecule binding; hit rates improved steadily with docking score, as did affinities. This also appeared true on re-analysis of large-scale results against the σ2 and dopamine D4 receptors. It may be that as the scale of both the virtual libraries and their testing grows, not only are better ligands found but so too does our ability to rank them.

G Protein-Coupled Receptors: A Century of Research and Discovery

GPCRs (G protein-coupled receptors), also known as 7 transmembrane domain receptors, are the largest receptor family in the human genome, with ≈800 members. GPCRs regulate nearly every aspect of human physiology and disease, thus serving as important drug targets in cardiovascular disease. Sharing a conserved structure comprised of 7 transmembrane α-helices, GPCRs couple to heterotrimeric G-proteins, GPCR kinases, and β-arrestins, promoting downstream signaling through second messengers and other intracellular signaling pathways. GPCR drug development has led to important cardiovascular therapies, such as antagonists of β-adrenergic and angiotensin II receptors for heart failure and hypertension, and agonists of the glucagon-like peptide-1 receptor for reducing adverse cardiovascular events and other emerging indications. There continues to be a major interest in GPCR drug development in cardiovascular and cardiometabolic disease, driven by advances in GPCR mechanistic studies and structure-based drug design. This review recounts the rich history of GPCR research, including the current state of clinically used GPCR drugs, and highlights newly discovered aspects of GPCR biology and promising directions for future investigation. As additional mechanisms for regulating GPCR signaling are uncovered, new strategies for targeting these ubiquitous receptors hold tremendous promise for the field of cardiovascular medicine.

AlphaFold2 structures guide prospective ligand discovery

AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models of the σ2 and serotonin 2A (5-HT2A) receptors, testing hundreds of new molecules and comparing results with those obtained from docking against the experimental structures. Hit rates were high and similar for the experimental and AF2 structures, as were affinities. Success in docking against the AF2 models was achieved despite differences between orthosteric residue conformations in the AF2 models and the experimental structures. Determination of the cryo-electron microscopy structure for one of the more potent 5-HT2A ligands from the AF2 docking revealed residue accommodations that resembled the AF2 prediction. AF2 models may sample conformations that differ from experimental structures but remain low energy and relevant for ligand discovery, extending the domain of structure-based drug design.

Mining key circadian biomarkers for major depressive disorder by integrating bioinformatics and machine learning

OBJECTIVE

This study aimed to identify key clock genes closely associated with major depressive disorder (MDD) using bioinformatics and machine learning approaches.

METHODS

Gene expression data of 128 MDD patients and 64 healthy controls from blood samples were obtained. Differentially expressed were identified and weighted gene co-expression network analysis (WGCNA) was first performed to screen MDD-related key genes. These genes were then intersected with 1475 known circadian rhythm genes to identify circadian rhythm genes associated with MDD. Finally, multiple machine learning algorithms were applied for further selection, to determine the most critical 4 circadian rhythm biomarkers.

RESULTS

Four key circadian rhythm genes (ABCC2, APP, HK2 and RORA) were identified that could effectively distinguish MDD samples from controls. These genes were significantly enriched in circadian pathways and showed strong correlations with immune cell infiltration. Drug target prediction suggested that small molecules like melatonin and escitalopram may target these circadian rhythm proteins.

CONCLUSION

This study revealed discovered 4 key circadian rhythm genes closely associated with MDD, which may serve as diagnostic biomarkers and therapeutic targets. The findings highlight the important roles of circadian disruptions in the pathogenesis of MDD, providing new insights for precision diagnosis and targeted treatment of MDD.

Utility of the Morgan Fingerprint in Structure-Based Virtual Ligand Screening

In modern drug discovery, virtual ligand screening (VLS) is frequently applied to identify possible hits before experimental testing and refinement due to its cost-effective nature for large compound libraries. For decades, efforts have been devoted to developing VLS methods with high accuracy. These include the state-of-the-art FINDSITE suite of approaches FINDSITEcomb2.0, FRAGSITE, and FRAGSITE2 and the meta version FRAGSITEcomb that were developed in our lab. These methods combine ligand homology modeling (LHM), traditional ligand similarity methods, and more recently machine learning approaches to rank ligands and have proven to be superior to most recent deep learning and large language model-based approaches. Here, we describe further improvements to our previous best methods by combining the Morgan fingerprint (MF) with the originally used PubChem fingerprint and FP2 fingerprint. We then benchmarked FINDSITEcomb2.0M, FRAGSITEM, FRAGSITE2M, and the composite meta-approach FRAGSITEcombM. On the 102 target DUD-E set, the 1% enrichment factor (EF1%) and area under the precision-recall curve (AUPR) of FRAGSITEcomb increased from 42.0/0.59 to 47.6/0.72. This 0.72 AUPR is significantly better than that of the state-of-the-art deep learning-based method DenseFS's AUPR of 0.443. An independent test on the 81 targets DEKOIS2.0 set shows that EF1%/AUPR increases from 18.3/0.520 to 23.1/0.683. An ablation investigation shows that the MF contributes to most of the improvement of all four approaches. Thus, the MF is a useful addition to structure-based VLS.

Screening of Mpro Protease (SARS-CoV-2) Covalent Inhibitors from an Anthocyanin-Rich Blueberry Extract Using an HRMS-Based Analytical Platform

BACKGROUND

The viral main protease (Mpro) of SARS-CoV-2 has been recently proposed as a key target to inhibit virus replication in the host. Therefore, molecules that can bind the catalytic site of Mpro could be considered as potential drug candidates in the treatment of SARS-CoV-2 infections. Here we proposed the application of a state-of-the-art analytical platform which combines metabolomics and protein structure analysis to fish-out potential active compounds deriving from a natural matrix, i.e., a blueberry extract.

METHODS

The experiments focus on finding MS covalent inhibitors of Mpro that contain in their structure a catechol/pyrogallol moiety capable of binding to the nucleophilic amino acids of the enzyme's catalytic site.

RESULTS

Among the potential candidates identified, the delphinidin-3-glucoside showed the most promising results. Its antiviral activity has been confirmed in vitro on Vero E6 cells infected with SARS-CoV-2, showing a dose-dependent inhibitory effect almost comparable to the known Mpro inhibitor baicalin. The interaction of delphinidin-3-glucoside with the Mpro pocket observed was also evaluated by computational studies.

CONCLUSIONS

The HRMS analytical platform described proved to be effective in identifying compounds that covalently bind Mpro and are active in the inhibition of SARS-CoV-2 replication, such as delphinidin-3-glucoside.

ERNEST COST action overview on the (patho)physiology of GPCRs and orphan GPCRs in the nervous system

G protein-coupled receptors (GPCRs) are a large family of cell surface receptors that play a critical role in nervous system function by transmitting signals between cells and their environment. They are involved in many, if not all, nervous system processes, and their dysfunction has been linked to various neurological disorders representing important drug targets. This overview emphasises the GPCRs of the nervous system, which are the research focus of the members of ERNEST COST action (CA18133) working group 'Biological roles of signal transduction'. First, the (patho)physiological role of the nervous system GPCRs in the modulation of synapse function is discussed. We then debate the (patho)physiology and pharmacology of opioid, acetylcholine, chemokine, melatonin and adhesion GPCRs in the nervous system. Finally, we address the orphan GPCRs, their implication in the nervous system function and disease, and the challenges that need to be addressed to deorphanize them.

Identification of 1,3,8-Triazaspiro[4.5]Decane-2,4-Dione Derivatives as a Novel δ Opioid Receptor-Selective Agonist Chemotype

δ opioid receptors (DORs) hold potential as a target for neurologic and psychiatric disorders, yet no DOR agonist has proven efficacious in critical phase II clinical trials. The exact reasons for the failure to produce quality drug candidates for the DOR are unclear. However, it is known that certain DOR agonists can induce seizures and exhibit tachyphylaxis. Several studies have suggested that those adverse effects are more prevalent in delta agonists that share the (+)-4-[(αR)-α-((2S,5R)-4-allyl-2,5-dimethyl-1-piperazinyl)-3-methoxybenzyl]-N,N-diethylbenzamide (SNC80)/4-[(αR*)-α-((2S*,5R*)-4-allyl-2,5-dimethyl-1-piperazinyl)-3-hydroxybenzyl]-N,N-diethylbenzamide chemotype. There is a need to find novel lead candidates for drug development that have improved pharmacological properties to differentiate them from the current failed delta agonists. Our objective in this study was to identify novel DOR agonists. We used a β-arrestin assay to screen a small G-protein coupled receptors (GPCR)-focused chemical library. We identified a novel chemotype of DOR agonists that appears to bind to the orthosteric site based of docking and molecular dynamic simulation. The most potent agonist hit compound is selective for the DOR over a panel of 167 other GPCRs, is slightly biased toward G-protein signaling and has anti-allodynic efficacy in a complete Freund's adjuvant model of inflammatory pain in C57BL/6 male and female mice. The newly discovered chemotype contrasts with molecules like SNC80 that are highly efficacious β-arrestin recruiters and may suggest this novel class of DOR agonists could be expanded on to develop a clinical candidate drug. SIGNIFICANCE STATEMENT: δ opioid receptors are a clinical target for various neurological disorders, including migraine and chronic pain. Many of the clinically tested delta opioid agonists share a single chemotype, which carries risks during drug development. Through a small-scale high-throughput screening assay, this study identified a novel δ opioid receptor agonist chemotype, which may serve as alternative for the current analgesic clinical candidates.

Quantum mechanics insights into melatonin and analogs binding to melatonin MT1 and MT2 receptors

Melatonin receptors MT1 and MT2 are G protein-coupled receptors that mediate the effects of melatonin, a hormone involved in circadian rhythms and other physiological functions. Understanding the molecular interactions between these receptors and their ligands is crucial for developing novel therapeutic agents. In this study, we used molecular docking, molecular dynamics simulations, and quantum mechanics calculation to investigate the binding modes and affinities of three ligands: melatonin (MLT), ramelteon (RMT), and 2-phenylmelatonin (2-PMT) with both receptors. Based on the results, we identified key amino acids that contributed to the receptor-ligand interactions, such as Gln181/194, Phe179/192, and Asn162/175, which are conserved in both receptors. Additionally, we described new meaningful interactions with Gly108/Gly121, Val111/Val124, and Val191/Val204. Our results provide insights into receptor-ligand recognition's structural and energetic determinants and suggest potential strategies for designing more optimized molecules. This study enhances our understanding of receptor-ligand interactions and offers implications for future drug development.

Effects of intraoperative sodium oxybate infusion on post-operative sleep quality in patients undergoing gynecological laparoscopic surgery: A randomized clinical trial

STUDY OBJECTIVE

Post-operative sleep quality is an important factor that influences post-operative recovery. Sodium oxybate has been used to treat sleep disturbances associated with various pathological conditions. However, whether intraoperative intravenous infusion of sodium oxybate improves post-operative sleep quality is unknown. This study aimed to examine the effects of sodium oxybate on the post-operative sleep quality of patients who underwent gynecological laparoscopic surgery.

DESIGN

A single-center, prospective, two-arm, double-blinded randomized controlled trial.

SETTING

The Shengjing Hospital of China Medical University in Liaoning, China.

PATIENTS

We enrolled 180 adult patients (90 for each group) undergoing elective gynecological laparoscopic surgery, and 178 patients (89 for each group) were included in the final analysis.

INTERVENTIONS

Patients were randomly allocated in a 1:1 ratio to receive either sodium oxybate (30 mg kg-1) or an equivalent volume of saline after intubation. The patients, anesthetists, and follow-up staff were blinded to group assignment.

MEASUREMENTS

The primary outcome was sleep quality measured using the Richards-Campbell Sleep Questionnaire (RCSQ) on post-operative days (PODs) one and three. Secondary outcomes included post-operative pain measured using the visual analog scale, sleep quality at one and three months post-operatively measured using the Pittsburgh Sleep Quality Index, and factors associated with post-operative sleep quality.

MAIN RESULTS

Analysis with generalized estimating equations showed that sodium oxybate significantly improved post-operative sleep quality, as represented by increased total RCSQ scores (mean difference (95% CI); 9 (2, 16), P = 0.010) over PODs one and three. There was no difference in post-operative pain between the two groups over PODs one and three or in post-operative sleep quality over one and three months post-operatively. Age, surgery type, start time of surgery, and use of sufentanil-based patient-controlled intravenous analgesia were significantly associated with post-operative sleep quality.

CONCLUSIONS

Intraoperative sodium oxybate infusion improved post-operative sleep in patients who underwent gynecological laparoscopic surgery.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, Clinical trial number: ChiCTR2200061460.

Industrial and academic approaches to the search for alternative melatonin receptor ligands: An historical survey

The search for melatonin receptor agonists formed the main part of melatonin medicinal chemistry programs for the last three decades. In this short review, we summarize the two main aspects of these programs: the development of all the necessary tools to characterize the newly synthesized ligands at the two melatonin receptors MT1 and MT2, and the medicinal chemist's approaches to find chemically diverse ligands at these receptors. Both strategies are described. It turns out that the main source of tools were industrial laboratories, while the medicinal chemistry was mainly carried out in academia. Such complete accounts are interesting, as they delineate the spirits in which the teams were working demonstrating their strength and innovative character. Most of the programs were focused on nonselective agonists and few of them reached the market. In contrast, discovery of MT1-selective agonists and melatonergic antagonists with proven in vivo activity and MT1 or MT2-selectivity is still in its infancy, despite the considerable interest that subtype selective compounds may bring in the domain, as the physiological respective roles of the two subtypes of melatonin receptors, is still poorly understood. Poly-pharmacology applications and multitarget ligands have also been considered.

Circadian Clock in Muscle Disease Etiology and Therapeutic Potential for Duchenne Muscular Dystrophy

Circadian clock and clock-controlled output pathways exert temporal control in diverse aspects of skeletal muscle physiology, including the maintenance of muscle mass, structure, function, and metabolism. They have emerged as significant players in understanding muscle disease etiology and potential therapeutic avenues, particularly in Duchenne muscular dystrophy (DMD). This review examines the intricate interplay between circadian rhythms and muscle physiology, highlighting how disruptions of circadian regulation may contribute to muscle pathophysiology and the specific mechanisms linking circadian clock dysregulation with DMD. Moreover, we discuss recent advancements in chronobiological research that have shed light on the circadian control of muscle function and its relevance to DMD. Understanding clock output pathways involved in muscle mass and function offers novel insights into the pathogenesis of DMD and unveils promising avenues for therapeutic interventions. We further explore potential chronotherapeutic strategies targeting the circadian clock to ameliorate muscle degeneration which may inform drug development efforts for muscular dystrophy.

Virtual Screening of a Chemically Diverse "Superscaffold" Library Enables Ligand Discovery for a Key GPCR Target

The advent of ultra-large libraries of drug-like compounds has significantly broadened the possibilities in structure-based virtual screening, accelerating the discovery and optimization of high-quality lead chemotypes for diverse clinical targets. Compared to traditional high-throughput screening, which is constrained to libraries of approximately one million compounds, the ultra-large virtual screening approach offers substantial advantages in both cost and time efficiency. By expanding the chemical space with compounds synthesized from easily accessible and reproducible reactions and utilizing a large, diverse set of building blocks, we can enhance both the diversity and quality of the discovered lead chemotypes. In this study, we explore new chemical spaces using reactions of sulfur(VI) fluorides to create a combinatorial library consisting of several hundred million compounds. We screened this virtual library for cannabinoid type II receptor (CB2) antagonists using the high-resolution structure in conjunction with a rationally designed antagonist, AM10257. The top-predicted compounds were then synthesized and tested in vitro for CB2 binding and functional antagonism, achieving an experimentally validated hit rate of 55%. Our findings demonstrate the effectiveness of reliable reactions, such as sulfur fluoride exchange, in diversifying ultra-large chemical spaces and facilitate the discovery of new lead compounds for important biological targets.

High-frequency neural activity dysregulation is associated with sleep and psychiatric disorders in BMAL1-deficient animal models

Sleep disturbance led by BMAL1-deficiency has been recognized both in rodent and non-human primate models. Yet it remained unclear how their diurnal brain oscillations were affected upon BMAL1 ablation and what caused the discrepancy in the quantity of sleep between the two species. Here, we investigated diurnal electroencephalographs of BMAL1-deficient mice and cynomolgus monkeys at young adult age and uncovered a shared defect of dysregulated high-frequency oscillations by Kullback-Leibler divergence analysis. We found beta and gamma oscillations were significantly disturbed in a day versus night manner in BMAL1-deficient monkeys, while in mice the beta band difference was less evident. Notably, the dysregulation of beta oscillations was particularly associated with psychiatric behaviors in BMAL1-deficient monkeys, including the occurrence of self-injuring and delusion-like actions. As such psychiatric phenotypes were challenging to uncover in rodent models, our results offered a unique method to study the correlation between circadian clock dysregulation and psychiatric disorders.

Discovery of Protease-Activated Receptor 4 (PAR4)-Tethered Ligand Antagonists Using Ultralarge Virtual Screening

Here, we demonstrate a structure-based small molecule virtual screening and lead optimization pipeline using a homology model of a difficult-to-drug G-protein-coupled receptor (GPCR) target. Protease-activated receptor 4 (PAR4) is activated by thrombin cleavage, revealing a tethered ligand that activates the receptor, making PAR4 a challenging target. A virtual screen of a make-on-demand chemical library yielded a one-hit compound. From the single-hit compound, we developed a novel series of PAR4 antagonists. Subsequent lead optimization via simultaneous virtual library searches and structure-based rational design efforts led to potent antagonists of thrombin-induced activation. Interestingly, this series of antagonists was active against PAR4 activation by the native protease thrombin cleavage but not the synthetic PAR4 agonist peptide AYPGKF.

Integrated Computational Pipeline for the High-Throughput Discovery of Cell Adhesion Peptides

Cell adhesion peptides (CAPs) often play a critical role in tissue engineering research. However, the discovery of novel CAPs for diverse applications remains a challenging and time-intensive process. This study presents an efficient computational pipeline integrating sequence embeddings, binding predictors, and molecular dynamics simulations to expedite the discovery of new CAPs. A Pro2vec model, trained on vast CAP data sets, was built to identify RGD-similar tripeptide candidates. These candidates were further evaluated for their binding affinity with integrin receptors using the Mutabind2 machine learning model. Additionally, molecular dynamics simulations were applied to model receptor-peptide interactions and calculate their binding free energies, providing a quantitative assessment of the binding strength for further screening. The resulting peptide demonstrated performance comparable to that of RGD in endothelial cell adhesion and spreading experimental assays, validating the efficacy of the integrated computational pipeline.

The Science and Art of Structure-Based Virtual Screening

Structure-based virtual screening has gained momentum again as the high attrition rate at every stage of drug discovery drives the need to explore a greater chemical space. From the Bayesian perspective, its shortcomings as a viable strategy for sustainable hit discovery are discussed, with regard to the prior hit rates of screening libraries and the performance of computational methods. Lessons are shared in selecting virtual hits for experimental validation learned from a series of eight successful campaigns, one of which impacted the discovery of a drug candidate currently in clinical trials.

Regression-Based Active Learning for Accessible Acceleration of Ultra-Large Library Docking

Structure-based drug discovery is a process for both hit finding and optimization that relies on a validated three-dimensional model of a target biomolecule, used to rationalize the structure-function relationship for this particular target. An ultralarge virtual screening approach has emerged recently for rapid discovery of high-affinity hit compounds, but it requires substantial computational resources. This study shows that active learning with simple linear regression models can accelerate virtual screening, retrieving up to 90% of the top-1% of the docking hit list after docking just 10% of the ligands. The results demonstrate that it is unnecessary to use complex models, such as deep learning approaches, to predict the imprecise results of ligand docking with a low sampling depth. Furthermore, we explore active learning meta-parameters and find that constant batch size models with a simple ensembling method provide the best ligand retrieval rate. Finally, our approach is validated on the ultralarge size virtual screening data set, retrieving 70% of the top-0.05% of ligands after screening only 2% of the library. Altogether, this work provides a computationally accessible approach for accelerated virtual screening that can serve as a blueprint for the future design of low-compute agents for exploration of the chemical space via large-scale accelerated docking. With recent breakthroughs in protein structure prediction, this method can significantly increase accessibility for the academic community and aid in the rapid discovery of high-affinity hit compounds for various targets.

Discovery of novel and selective farnesoid X receptor antagonists through structure-based virtual screening, preliminary structure-activity relationship study, and biological evaluation

Farnesoid X receptor (FXR) is a bile acids receptor and plays a crucial role in regulating bile acids, lipids, and glucose metabolism. Previous research suggests that inhibiting FXR activation can be beneficial in reducing cholesterol and low-density lipoprotein cholesterol (LDL-C) levels, offering potential treatment options for metabolic syndrome with lipid disorders. Herein, we report p-acetylaminobenzene sulfonate derivatives as a novel scaffold of FXR antagonists by multistage screening. Among these derivatives, compound F44-A13 exhibited a half-maximal inhibitory concentration of 1.1 μM. Furthermore, compound F44-A13 demonstrated effective inhibition of FXR activation in cellular assays and exhibited high selectivity over eleven other nuclear receptors. Besides, compound F44-A13 significantly suppressed the regulation of FXR target genes Shp, Besp, and Cyp7a1, while reducing cholesterol levels in human hepatoma HepG2 cells. Pharmacological studies conducted on C57BL/6 mice further confirmed that compound F44-A13 had beneficial effects in reducing cholesterol, triglycerides, and LDL-C levels. These findings highlight that F44-A13 is a highly selective FXR antagonist that might serve as a useful molecule for further FXR studies as well as the development of FXR antagonists for the potential treatment of metabolic diseases with lipid disorders.

Melatonin receptor structure and signaling

Melatonin (5-methoxy-N-acetyltryptamine) binds with high affinity and specificity to membrane receptors. Several receptor subtypes exist in different species, of which the mammalian MT1 and MT2 receptors are the best-characterized. They are members of the G protein-coupled receptor superfamily, preferentially coupling to Gi/o proteins but also to other G proteins in a cell-context-depending manner. In this review, experts on melatonin receptors will summarize the current state of the field. We briefly report on the discovery and classification of melatonin receptors, then focus on the molecular structure of human MT1 and MT2 receptors and highlight the importance of molecular simulations to identify new ligands and to understand the structural dynamics of these receptors. We then describe the state-of-the-art of the intracellular signaling pathways activated by melatonin receptors and their complexes. Brief statements on the molecular toolbox available for melatonin receptor studies and future perspectives will round-up this review.

Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces

Ultralarge chemical spaces describing several billion compounds are revolutionizing hit identification in early drug discovery. Because of their size, such chemical spaces cannot be fully enumerated and require ad-hoc computational tools to navigate them and pick potentially interesting hits. We here propose a structure-based approach to ultralarge chemical space screening in which commercial chemical reagents are first docked to the target of interest and then directly connected according to organic chemistry and topological rules, to enumerate drug-like compounds under three-dimensional constraints of the target. When applied to bespoke chemical spaces of different sizes and chemical complexity targeting two receptors of pharmaceutical interest (estrogen β receptor, dopamine D3 receptor), the computational method was able to quickly enumerate hits that were either known ligands (or very close analogs) of targeted receptors as well as chemically novel candidates that could be experimentally confirmed by in vitro binding assays. The proposed approach is generic, can be applied to any docking algorithm, and requires few computational resources to prioritize easily synthesizable hits from billion-sized chemical spaces.

AlphaFold2 structures template ligand discovery

AlphaFold2 (AF2) and RosettaFold have greatly expanded the number of structures available for structure-based ligand discovery, even though retrospective studies have cast doubt on their direct usefulness for that goal. Here, we tested unrefined AF2 models prospectively, comparing experimental hit-rates and affinities from large library docking against AF2 models vs the same screens targeting experimental structures of the same receptors. In retrospective docking screens against the σ2 and the 5-HT2A receptors, the AF2 structures struggled to recapitulate ligands that we had previously found docking against the receptors' experimental structures, consistent with published results. Prospective large library docking against the AF2 models, however, yielded similar hit rates for both receptors versus docking against experimentally-derived structures; hundreds of molecules were prioritized and tested against each model and each structure of each receptor. The success of the AF2 models was achieved despite differences in orthosteric pocket residue conformations for both targets versus the experimental structures. Intriguingly, against the 5-HT2A receptor the most potent, subtype-selective agonists were discovered via docking against the AF2 model, not the experimental structure. To understand this from a molecular perspective, a cryoEM structure was determined for one of the more potent and selective ligands to emerge from docking against the AF2 model of the 5-HT2A receptor. Our findings suggest that AF2 models may sample conformations that are relevant for ligand discovery, much extending the domain of applicability of structure-based ligand discovery.

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, while 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 novel 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 novel 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.

Small vs. Large Library Docking for Positive Allosteric Modulators of the Calcium Sensing Receptor

Drugs acting as positive allosteric modulators (PAMs) to enhance the activation of the calcium sensing receptor (CaSR) and to suppress parathyroid hormone (PTH) secretion can treat hyperparathyroidism but suffer from side effects including hypocalcemia and arrhythmias. Seeking new CaSR modulators, we docked libraries of 2.7 million and 1.2 billion molecules against transforming pockets in the active-state receptor dimer structure. Consistent with simulations suggesting that docking improves with library size, billion-molecule docking found new PAMs with a hit rate that was 2.7-fold higher than the million-molecule library and with hits up to 37-fold more potent. Structure-based optimization of ligands from both campaigns led to nanomolar leads, one of which was advanced to animal testing. This PAM displays 100-fold the potency of the standard of care, cinacalcet, in ex vivo organ assays, and reduces serum PTH levels in mice by up to 80% without the hypocalcemia typical of CaSR drugs. Cryo-EM structures with the new PAMs show that they induce residue rearrangements in the binding pockets and promote CaSR dimer conformations that are closer to the G-protein coupled state compared to established drugs. These findings highlight the promise of large library docking for therapeutic leads, especially when combined with experimental structure determination and mechanism.

Melatonin regulates the circadian rhythm to ameliorate postoperative sleep disorder and neurobehavioral abnormalities in aged mice

BACKGROUND

Postoperative sleep disorder (PSD) and delirium, which may be associated with surgery and inhalational anesthetics, induce adverse effects in old adults. Emerging evidence indicates that circadian rhythm contributes to various neuropathological diseases, including Alzheimer's disease. Thus, we analyzed the potential role of circadian rhythm in PSD and delirium-like behavior in aged mice and determined whether exogenous melatonin could facilitate entrainment of the circadian rhythm after laparotomy under sevoflurane anesthesia.

METHODS

We selected old C57BL/6J mice which receiving laparotomy/sevoflurane anesthesia as model animals. We employed buried food, open field, and Y maze test to assess delirium-like behavior, and electroencephalography/electromyography (EEG/EMG) were used to investigate sleep changes. We analyzed the transcription rhythm of clock genes in superchiasmatic nucleus (SCN) to explore the effects of surgery and melatonin pretreatment on the circadian rhythm. Then, we measured melatonin receptor levels in SCN and ERK/CREB pathway-related proteins in hippocampus and prefrontal cortex to assess their role in PSDs and delirium-like behavior.

RESULTS

Laparotomy under sevoflurane anesthesia had a greater influence than sevoflurane alone, leading to sleep disorder, a shift in sleep-wake rhythm, and delirium-like behavior. Bmal1, Clock, and Cry1 mRNA expression showed a peak shift, MT1 melatonin receptor expression level was increased in the SCN, and p-ERK/ERK and p-CREB/CREB were decreased in hippocampus and prefrontal cortex of aged mice 1 day after laparotomy. Melatonin showed significant efficacy in ameliorating PSD and delirium-like behavior and restoring the circadian rhythm, reversing melatonin receptor and ERK/CREB pathway expression abnormalities. In addition, most of the beneficial effect of melatonin was antagonized by luzindole, a melatonin receptor antagonist.

CONCLUSIONS

Melatonin receptors in SCN, circadian rhythm, and ERK/CREB signaling pathway participate in the pathophysiological processes of PSD and delirium-like behavior. Melatonin intervention could be a potential preventative approach for PSD and delirium.

Large library docking for cannabinoid-1 receptor agonists with reduced side effects

Large library docking can reveal unexpected chemotypes that complement the structures of biological targets. Seeking new agonists for the cannabinoid-1 receptor (CB1R), we docked 74 million tangible molecules, prioritizing 46 high ranking ones for de novo synthesis and testing. Nine were active by radioligand competition, a 20% hit-rate. Structure-based optimization of one of the most potent of these (Ki = 0.7 uM) led to '4042, a 1.9 nM ligand and a full CB1R agonist. A cryo-EM structure of the purified enantiomer of '4042 ('1350) in complex with CB1R-Gi1 confirmed its docked pose. The new agonist was strongly analgesic, with generally a 5-10-fold therapeutic window over sedation and catalepsy and no observable conditioned place preference. These findings suggest that new cannabinoid chemotypes may disentangle characteristic cannabinoid side-effects from their analgesia, supporting the further development of cannabinoids as pain therapeutics.

Shape-Aware Synthon Search (SASS) for Virtual Screening of Synthon-Based Chemical Spaces

Virtual screening of large-scale chemical libraries has become increasingly useful for identifying high-quality candidates for drug discovery. While it is possible to exhaustively screen chemical spaces that number on the order of billions, indirect combinatorial approaches are needed to efficiently navigate larger, synthon-based virtual spaces. We describe Shape-Aware Synthon Search (SASS), a synthon-based virtual screening method that carries out shape similarity searches in the synthon space instead of the enumerated product space. SASS can replicate results from exhaustive searches in ultralarge, combinatorial spaces with high recall on a variety of query molecules while only scoring a small subspace of possible enumerated products, thereby significantly accelerating large-scale, shape-based virtual screening.

Exploring Novel Antidepressants Targeting G Protein-Coupled Receptors and Key Membrane Receptors Based on Molecular Structures

Major Depressive Disorder (MDD) is a complex mental disorder that involves alterations in signal transmission across multiple scales and structural abnormalities. The development of effective antidepressants (ADs) has been hindered by the dominance of monoamine hypothesis, resulting in slow progress. Traditional ADs have undesirable traits like delayed onset of action, limited efficacy, and severe side effects. Recently, two categories of fast-acting antidepressant compounds have surfaced, dissociative anesthetics S-ketamine and its metabolites, as well as psychedelics such as lysergic acid diethylamide (LSD). This has led to structural research and drug development of the receptors that they target. This review provides breakthroughs and achievements in the structure of depression-related receptors and novel ADs based on these. Cryo-electron microscopy (cryo-EM) has enabled researchers to identify the structures of membrane receptors, including the N-methyl-D-aspartate receptor (NMDAR) and the 5-hydroxytryptamine 2A (5-HT2A) receptor. These high-resolution structures can be used for the development of novel ADs using virtual drug screening (VDS). Moreover, the unique antidepressant effects of 5-HT1A receptors in various brain regions, and the pivotal roles of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) and tyrosine kinase receptor 2 (TrkB) in regulating synaptic plasticity, emphasize their potential as therapeutic targets. Using structural information, a series of highly selective ADs were designed based on the different role of receptors in MDD. These molecules have the favorable characteristics of rapid onset and low adverse drug reactions. This review offers researchers guidance and a methodological framework for the structure-based design of ADs.

DockOpt: A Tool for Automatic Optimization of Docking Models

Molecular docking is a widely used technique for leveraging protein structure for ligand discovery, but it remains difficult to utilize due to limitations that have not been adequately addressed. Despite some progress toward automation, docking still requires expert guidance, hindering its adoption by a broader range of investigators. To make docking more accessible, we developed a new utility called DockOpt, which automates the creation, evaluation, and optimization of docking models prior to their deployment in large-scale prospective screens. DockOpt outperforms our previous automated pipeline across all 43 targets in the DUDE-Z benchmark data set, and the generated models for 84% of targets demonstrate sufficient enrichment to warrant their use in prospective screens, with normalized LogAUC values of at least 15%. DockOpt is available as part of the Python package Pydock3 included in the UCSF DOCK 3.8 distribution, which is available for free to academic researchers at https://dock.compbio.ucsf.edu and free for everyone upon registration at https://tldr.docking.org.

Computational drug development for membrane protein targets

The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.

DOCK 6: Incorporating hierarchical traversal through precomputed ligand conformations to enable large-scale docking

To allow DOCK 6 access to unprecedented chemical space for screening billions of small molecules, we have implemented features from DOCK 3.7 into DOCK 6, including a search routine that traverses precomputed ligand conformations stored in a hierarchical database. We tested them on the DUDE-Z and SB2012 test sets. The hierarchical database search routine is 16 times faster than anchor-and-grow. However, the ability of hierarchical database search to reproduce the experimental pose is 16% worse than that of anchor-and-grow. The enrichment performance is on average similar, but DOCK 3.7 has better enrichment than DOCK 6, and DOCK 6 is on average 1.7 times slower. However, with post-docking torsion minimization, DOCK 6 surpasses DOCK 3.7. A large-scale virtual screen is performed with DOCK 6 on 23 million fragment molecules. We use current features in DOCK 6 to complement hierarchical database calculations, including torsion minimization, which is not available in DOCK 3.7.

Targeting ion channels with ultra-large library screening for hit discovery

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.

Molecular mechanism of antihistamines recognition and regulation of the histamine H1 receptor

Histamine receptors are a group of G protein-coupled receptors (GPCRs) that play important roles in various physiological and pathophysiological conditions. Antihistamines that target the histamine H1 receptor (H1R) have been widely used to relieve the symptoms of allergy and inflammation. Here, to uncover the details of the regulation of H1R by the known second-generation antihistamines, thereby providing clues for the rational design of newer antihistamines, we determine the cryo-EM structure of H1R in the apo form and bound to different antihistamines. In addition to the deep hydrophobic cavity, we identify a secondary ligand-binding site in H1R, which potentially may support the introduction of new derivative groups to generate newer antihistamines. Furthermore, these structures show that antihistamines exert inverse regulation by utilizing a shared phenyl group that inserts into the deep cavity and block the movement of the toggle switch residue W4286.48. Together, these results enrich our understanding of GPCR modulation and facilitate the structure-based design of novel antihistamines.

Discovery of 3-oxo-1,2,3,4-tetrahydropyrido[1,2-a]pyrazin derivatives as SARS-CoV-2 main protease inhibitors through virtual screening and biological evaluation

The COVID-19 caused by SARS-CoV-2 has led to a global pandemic that continues to impact societies and economies worldwide. The main protease (Mpro) plays a crucial role in SARS-CoV-2 replication and is an attractive target for anti-SARS-CoV-2 drug discovery. Herein, we report a series of 3-oxo-1,2,3,4-tetrahydropyrido[1,2-a]pyrazin derivatives as non-peptidomimetic inhibitors targeting SARS-CoV-2 Mpro through structure-based virtual screening and biological evaluation. Further similarity search and structure-activity relationship study led to the identification of compound M56-S2 with the enzymatic IC50 value of 4.0 μM. Moreover, the molecular simulation and predicted ADMET properties, indicated that non-peptidomimetic inhibitor M56-S2 might serve as a useful starting point for the further discovery of highly potent inhibitors targeting SARS-CoV-2 Mpro.

HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries

Recent rapid expansion of make-on-demand, purchasable, chemical libraries comprising dozens of billions or even trillions of molecules has challenged the efficient application of traditional structure-based virtual screening methods that rely on molecular docking. We present a novel computational methodology termed HIDDEN GEM (HIt Discovery using Docking ENriched by GEnerative Modeling) that greatly accelerates virtual screening. This workflow uniquely integrates machine learning, generative chemistry, massive chemical similarity searching and molecular docking of small, selected libraries in the beginning and the end of the workflow. For each target, HIDDEN GEM nominates a small number of top-scoring virtual hits prioritized from ultra-large chemical libraries. We have benchmarked HIDDEN GEM by conducting virtual screening campaigns for 16 diverse protein targets using Enamine REAL Space library comprising 37 billion molecules. We show that HIDDEN GEM yields the highest enrichment factors as compared to state of the art accelerated virtual screening methods, while requiring the least computational resources. HIDDEN GEM can be executed with any docking software and employed by users with limited computational resources.

FRAGSITE2: A structure and fragment-based approach for virtual ligand screening

Protein function annotation and drug discovery often involve finding small molecule binders. In the early stages of drug discovery, virtual ligand screening (VLS) is frequently applied to identify possible hits before experimental testing. While our recent ligand homology modeling (LHM)-machine learning VLS method FRAGSITE outperformed approaches that combined traditional docking to generate protein-ligand poses and deep learning scoring functions to rank ligands, a more robust approach that could identify a more diverse set of binding ligands is needed. Here, we describe FRAGSITE2 that shows significant improvement on protein targets lacking known small molecule binders and no confident LHM identified template ligands when benchmarked on two commonly used VLS datasets: For both the DUD-E set and DEKOIS2.0 set and ligands having a Tanimoto coefficient (TC) < 0.7 to the template ligands, the 1% enrichment factor (EF1% ) of FRAGSITE2 is significantly better than those for FINDSITEcomb2.0 , an earlier LHM algorithm. For the DUD-E set, FRAGSITE2 also shows better ROC enrichment factor and AUPR (area under the precision-recall curve) than the deep learning DenseFS scoring function. Comparison with the RF-score-VS on the 76 target subset of DEKOIS2.0 and a TC < 0.99 to training DUD-E ligands, FRAGSITE2 has double the EF1% . Its boosted tree regression method provides for more robust performance than a deep learning multiple layer perceptron method. When compared with the pretrained language model for protein target features, FRAGSITE2 also shows much better performance. Thus, FRAGSITE2 is a promising approach that can discover novel hits for protein targets. FRAGSITE2's web service is freely available to academic users at http://sites.gatech.edu/cssb/FRAGSITE2.

The location, physiology, pathology of hippocampus Melatonin MT2 receptor and MT2-selective modulators

Melatonin, a neurohormone secreted by the pineal gland and regulated by the suprachiasmatic nucleus (SCN) of the hypothalamus, is synthesized and directly released into the cerebrospinal fluid (CSF) of the third ventricle (3rdv), where it undergoes rapid absorption by surrounding tissues to exert its physiological function. The hippocampus, a vital structure in the limbic system adjacent to the ventricles, plays a pivotal role in emotional response and memory formation. Melatonin MT1 and MT2 receptors are G protein-coupled receptors (GPCRs) that primarily mediate melatonin's receptor-dependent effects. In comparison to the MT1 receptor, the widely expressed MT2 receptor is crucial for mediating melatonin's biological functions within the hippocampus. Specifically, MT2 receptor is implicated in hippocampal synaptic plasticity and memory processes, as well as neurogenesis and axogenesis. Numerous studies have demonstrated the involvement of MT2 receptors in the pathophysiology and pharmacology of Alzheimer's disease, depression, and epilepsy. This review focuses on the anatomical localization of MT2 receptor in the hippocampus, their physiological function in this region, and their signal transduction and pharmacological roles in neurological disorders. Additionally, we conducted a comprehensive review of MT2 receptor ligands used in psychopharmacology and other MT2-selective ligands over recent years. Ultimately, we provide an outlook on future research for selective MT2 receptor drug candidates.

Docking for EP4R antagonists active against inflammatory pain

The lipid prostaglandin E2 (PGE2) mediates inflammatory pain by activating G protein-coupled receptors, including the prostaglandin E2 receptor 4 (EP4R). Nonsteroidal anti-inflammatory drugs (NSAIDs) reduce nociception by inhibiting prostaglandin synthesis, however, the disruption of upstream prostanoid biosynthesis can lead to pleiotropic effects including gastrointestinal bleeding and cardiac complications. In contrast, by acting downstream, EP4R antagonists may act specifically as anti-inflammatory agents and, to date, no selective EP4R antagonists have been approved for human use. In this work, seeking to diversify EP4R antagonist scaffolds, we computationally dock over 400 million compounds against an EP4R crystal structure and experimentally validate 71 highly ranked, de novo synthesized molecules. Further, we show how structure-based optimization of initial docking hits identifies a potent and selective antagonist with 16 nanomolar potency. Finally, we demonstrate favorable pharmacokinetics for the discovered compound as well as anti-allodynic and anti-inflammatory activity in several preclinical pain models in mice.

Structure-based discovery of novel P-glycoprotein inhibitors targeting the nucleotide binding domains

P-glycoprotein (P-gp), a membrane transport protein overexpressed in certain drug-resistant cancer cells, has been the target of numerous drug discovery projects aimed at overcoming drug resistance in cancer. Most characterized P-gp inhibitors bind at the large hydrophobic drug binding domain (DBD), but none have yet attained regulatory approval. In this study, we explored the potential of designing inhibitors that target the nucleotide binding domains (NBDs), by computationally screening a large library of 2.6 billion synthesizable molecules, using a combination of machine learning-guided molecular docking and molecular dynamics (MD). 14 of the computationally best-scoring molecules were subsequently tested for their ability to inhibit P-gp mediated calcein-AM efflux. In total, five diverse compounds exhibited inhibitory effects in the calcein-AM assay without displaying toxicity. The activity of these compounds was confirmed by their ability to decrease the verapamil-stimulated ATPase activity of P-gp in a subsequent assay. The discovery of these five novel P-gp inhibitors demonstrates the potential of in-silico screening in drug discovery and provides a new stepping point towards future potent P-gp inhibitors.

Artificial light at night, MRI-based measures of brain iron deposition and incidence of multiple mental disorders

BACKGROUND

Epidemiologic evidence on whether iron accumulation in brain modified the association between artificial light at night (ALAN) and incident mental disorders is lacking. The authors aims to investigate modification of brain iron deposition on the associations of ALAN with multiple mental disorders in the middle-aged and older adults.

METHODS

This prospective study used data from the UK Biobank. ALAN was drawn from satellite datasets. Susceptibility-weighted magnetic resonance imaging was used to ascertain iron content of each brain region. T2* signal loss was used as indices of iron deposition. The main outcomes are impacts of ALAN exposure on onset of wide spectrum of physician-diagnosed mental disorders, which was estimated by time-varying Cox proportional hazard model. The authors further conducted stratified analyses by levels of iron brain deposition to examine the potential modifying effects.

RESULTS

Among 298,283 participants followed for a median of 10.91 years, higher ALAN exposure was associated with increased risk of mental disorders. An IQR (11.37 nW/cm2/sr) increase in annual levels of ALAN was associated with an HR of 1.050 (95 % CI: 1.034,1.066) for any mental disorder, 1.076 (95 % CI: 1.053,1.099) for substance use disorder, and 1.036 (95 % CI: 1.004,1.069) for depression disorder in fully adjusted models. The exposure-response curves showed steeper trends at lower ALAN levels and a plateau at higher exposures. The associations were stronger in participants with high iron deposition in left hippocampus, left accumbens and left pallidum.

CONCLUSIONS

ALAN was associated with multiple mental disorders in the middle-aged and older adults, and the findings indicated stricter standards of ALAN is needed and targeted preventive measures are warranted, especially with high brain iron deposition.

Nano-composite system of traditional Chinese medicine for ocular applications: molecular docking and three-dimensional modeling insight for intelligent drug evaluation

The absorption of drugs was impeded in the posterior part of the eye due to the special structure. In addition, it was crucial to comprehend transport laws of molecules in ocular drug delivery for designing effective strategies. However, the current quality evaluation methods of the eye were backward and lack of dynamic monitoring of drug processes in vivo. Herein, nano-drug delivery system and three-dimensional (3D) model were combined to overcome the problems of low bioavailability and diffusion law. The model drugs were screened by molecular docking. The flexible nano-liposome (FNL) and temperature-sensitive gel (TSG) composite formulation was characterized through comprehensive evaluation. COMSOL software was utilized to build 3D eyeball to predict the bioavailability of drugs. The size of the preparation was about 98.34 nm which is relatively optimal for the enhanced permeability of the eyes. The formulation showed a stronger safety and non-irritant. The pharmacokinetics results of aqueous humor showed that the AUC of two drugs in this system increased by 3.79 and 3.94 times, respectively. The results of 3D calculation model proved that the concentrations of drugs reaching the retina were 1.90×10-5 mol/m3 and 6.37×10-6 mol/m3. In conclusion, the FNL-TSG markedly improved the bioavailability of multiple components in the eye. More importantly, a simplified 3D model was developed to preliminarily forecast the bioavailability of the retina after drug infusion, providing technical support for the accurate evaluation of ocular drug delivery. It provided new pattern for the development of intelligent versatile ophthalmic preparations.

FASTDock: A Pipeline for Allosteric Drug Discovery

Allostery is involved in innumerable biological processes and plays a fundamental role in human disease. Thus, the exploration of allosteric modulation is crucial for research on biological mechanisms and in the development of novel therapeutics. The development of small-molecule allosteric effectors can be used as tools to probe biological mechanisms of interest. One of the main limitations in targeting allosteric sites is the difficulty in uncovering them for specific receptors. Furthermore, upon discovery of novel allosteric modulation, early lead generation is made more difficult as compared to that at orthosteric sites because there is likely no information about the types of molecules that can bind at the site. In the work described here, we present a novel drug discovery pipeline, FASTDock, which allows one to uncover ligandable sites as well as small molecules that target the given site without requiring pre-existing knowledge of ligands that can bind in the targeted site. By using a hierarchical screening strategy, this method has the potential to enable high-throughput screens of an exceptionally large database of targeted ligand space.

Integrated Molecular Modeling and Machine Learning for Drug Design

Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.

A practical guide to machine-learning scoring for structure-based virtual screening

Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in size. Artificial intelligence and, more concretely, its machine-learning (ML) branch, including deep learning, have effectively exploited these data sets to build scoring functions (SFs) for SBVS against targets with an atomic-resolution 3D model (e.g., generated by X-ray crystallography or predicted by AlphaFold2). Often outperforming their generic and non-ML counterparts, target-specific ML-based SFs represent the state of the art for SBVS. Here, we present a comprehensive and user-friendly protocol to build and rigorously evaluate these new SFs for SBVS. This protocol is organized into four sections: (i) using a public benchmark of a given target to evaluate an existing generic SF; (ii) preparing experimental data for a target from public repositories; (iii) partitioning data into a training set and a test set for subsequent target-specific ML modeling; and (iv) generating and evaluating target-specific ML SFs by using the prepared training-test partitions. All necessary code and input/output data related to three example targets (acetylcholinesterase, HMG-CoA reductase, and peroxisome proliferator-activated receptor-α) are available at https://github.com/vktrannguyen/MLSF-protocol , can be run by using a single computer within 1 week and make use of easily accessible software/programs (e.g., Smina, CNN-Score, RF-Score-VS and DeepCoy) and web resources. Our aim is to provide practical guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules within a given compound library.

Circadian biology to advance therapeutics for mood disorders

Mood disorders account for a significant global disease burden, and pharmacological innovation is needed as existing medications are suboptimal. A wide range of evidence implicates circadian and sleep dysfunction in the pathogenesis of mood disorders, and there is growing interest in these chronobiological pathways as a focus for treatment innovation. We review contemporary evidence in three promising areas in circadian-clock-based therapeutics in mood disorders: targeting the circadian system informed by mechanistic molecular advances; time-tailoring of medications; and personalizing treatment using circadian parameters. We also consider the limitations and challenges in accelerating the development of new circadian-informed pharmacotherapies for mood disorders.

Functional screening and rational design of compounds targeting GPR132 to treat diabetes

Chronic inflammation due to islet-residing macrophages plays key roles in the development of type 2 diabetes mellitus. By systematically profiling intra-islet lipid-transmembrane receptor signalling in islet-resident macrophages, we identified endogenous 9(S)-hydroxy-10,12-octadecadienoic acid-G-protein-coupled receptor 132 (GPR132)-Gi signalling as a significant contributor to islet macrophage reprogramming and found that GPR132 deficiency in macrophages reversed metabolic disorders in mice fed a high-fat diet. The cryo-electron microscopy structures of GPR132 bound with two endogenous agonists, N-palmitoylglycine and 9(S)-hydroxy-10,12-octadecadienoic acid, enabled us to rationally design both GPR132 agonists and antagonists with high potency and selectivity through stepwise translational approaches. We ultimately identified a selective GPR132 antagonist, NOX-6-18, that modulates macrophage reprogramming within pancreatic islets, decreases weight gain and enhances glucose metabolism in mice fed a high-fat diet. Our study not only illustrates that intra-islet lipid signalling contributes to islet macrophage reprogramming but also provides a broadly applicable strategy for the identification of important G-protein-coupled receptor targets in pathophysiological processes, followed by the rational design of therapeutic leads for refractory diseases such as diabetes.

Machine Learning-Boosted Docking Enables the Efficient Structure-Based Virtual Screening of Giga-Scale Enumerated Chemical Libraries

The emergence of ultra-large screening libraries, filled to the brim with billions of readily available compounds, poses a growing challenge for docking-based virtual screening. Machine learning (ML)-boosted strategies like the tool HASTEN combine rapid ML prediction with the brute-force docking of small fractions of such libraries to increase screening throughput and take on giga-scale libraries. In our case study of an anti-bacterial chaperone and an anti-viral kinase, we first generated a brute-force docking baseline for 1.56 billion compounds in the Enamine REAL lead-like library with the fast Glide high-throughput virtual screening protocol. With HASTEN, we observed robust recall of 90% of the true 1000 top-scoring virtual hits in both targets when docking only 1% of the entire library. This reduction of the required docking experiments by 99% significantly shortens the screening time. In the kinase target, the employment of a hydrogen bonding constraint resulted in a major proportion of unsuccessful docking attempts and hampered ML predictions. We demonstrate the optimization potential in the treatment of failed compounds when performing ML-boosted screening and benchmark and showcase HASTEN as a fast and robust tool in a growing arsenal of approaches to unlock the chemical space covered by giga-scale screening libraries for everyday drug discovery campaigns.

Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries

Traditional small-molecule drug discovery is a time-consuming and costly endeavor. High-throughput chemical screening can only assess a tiny fraction of drug-like chemical space. The strong predictive power of modern machine-learning methods for virtual chemical screening enables training models on known active and inactive compounds and extrapolating to much larger chemical libraries. However, there has been limited experimental validation of these methods in practical applications on large commercially available or synthesize-on-demand chemical libraries. Through a prospective evaluation with the bacterial protein-protein interaction PriA-SSB, we demonstrate that ligand-based virtual screening can identify many active compounds in large commercial libraries. We use cross-validation to compare different types of supervised learning models and select a random forest (RF) classifier as the best model for this target. When predicting the activity of more than 8 million compounds from Aldrich Market Select, the RF substantially outperforms a naïve baseline based on chemical structure similarity. 48% of the RF's 701 selected compounds are active. The RF model easily scales to score one billion compounds from the synthesize-on-demand Enamine REAL database. We tested 68 chemically diverse top predictions from Enamine REAL and observed 31 hits (46%), including one with an IC50 value of 1.3 μM.