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Binding free energy calculations predict the potency of compounds to protein binding sites in a physically rigorous manner and see broad application in prioritizing the synthesis of novel drug candidates. Relative binding free energy (RBFE) calculations have emerged as an industry-standard approach to achieve highly accurate rank-order predictions of the potency of related compounds; however, this approach requires that the ligands share a common scaffold and a common binding mode, restricting the methods domain of applicability. This is a critical limitation since complex modifications to the ligands, especially core hopping, are very common in drug design. Absolute binding free energy (ABFE) calculations are an alternate method that can be used for ligands that are not congeneric. However, ABFE suffers from a known problem of long convergence times due to the need to sample additional degrees of freedom within each system, such as sampling rearrangements necessary to open and close the binding site. Here, we report on an alternative method for RBFE, called Separated Topologies (SepTop), which overcomes the issues in both of the aforementioned methods by enabling large scaffold changes between ligands with a convergence time comparable to traditional RBFE. Instead of only mutating atoms that vary between two ligands, this approach performs two absolute free energy calculations at the same time in opposite directions, one for each ligand. Defining the two ligands independently allows the comparison of the binding of diverse ligands without the artificial constraints of identical poses or a suitable atom-atom mapping. This approach also avoids the need to sample the unbound state of the protein, making it more efficient than absolute binding free energy calculations. Here, we introduce an implementation of SepTop. We developed a general and efficient protocol for running SepTop, and we demonstrated the method on four diverse, pharmaceutically relevant systems. We report the performance of the method, as well as our practical insights into the strengths, weaknesses, and challenges of applying this method in an industrial drug design setting. We find that the accuracy of the approach is sufficiently high to rank order ligands with an accuracy comparable to traditional RBFE calculations while maintaining the additional flexibility of SepTop.

Prostate cancer (PCa) is a major world-wide health concern. Current diagnostic methods involve Prostate-Specific Antigen (PSA) blood tests, biopsies, and Magnetic Resonance Imaging (MRI) to assess cancer aggressiveness and guide treatment decisions. MRI aligns with in silico medicine, as patient-specific image biomarkers can be obtained, contributing towards the development of digital twins for clinical practice. This work presents a novel framework to create a personalized PCa model by integrating clinical MRI data, such as the prostate and tumour geometry, the initial distribution of cells and the vasculature, so a full representation of the whole prostate is obtained. On top of the personalized model construction, our approach simulates and predicts temporal tumour growth in the prostate through the Finite Element Method, coupling the dynamics of tumour growth and the transport of oxygen, and incorporating cellular processes such as proliferation, differentiation, and apoptosis. In addition, our approach includes the simulation of the PSA dynamics, which allows to evaluate tumour growth through the PSA patient’s levels. To obtain the model parameters, a multi-objective optimization process is performed to adjust the best parameters for two patients simultaneously. This framework is validated by means of data from four patients with several MRI follow-ups. The diagnosis MRI allows the model creation and initialization, while subsequent MRI-based data provide additional information to validate computational predictions. The model predicts prostate and tumour volumes growth, along with serum PSA levels. This work represents a preliminary step towards the creation of digital twins for PCa patients, providing personalized insights into tumour growth.

Botrytis species cause gray mold disease in more than 200 crops worldwide. To control this disease, chemical fungicides are usually applied. However, more sustainable control alternatives should be explored, such as the use of hypovirulent mycovirus-infected fungal strains. To determine the mycovirome of two Botrytis species, B. cinerea and B. prunorum, we reanalyzed RNA-Seq and small RNA-Seq data using different assembly programs and an updated viral database, aiming to identify new mycoviruses that were previously not described in the same dataset. New mycoviruses were identified, including those previously reported to infect or be associated with B. cinerea and Plasmopara viticola, such as Botrytis cinerea alpha-like virus 1 and Plasmopara viticola lesion-associated ourmia-like virus 80. Additionally, two novel narnaviruses, not previously identified infecting Botrytis species, have been characterized, tentatively named Botrytis cinerea narnavirus 1 and Botrytis narnavirus 1. The analysis of small RNAs suggested that all identified mycoviruses were targeted by the antiviral fungal mechanism, regardless of the viral genome type. In conclusion, the enlarged list of newly found viruses and the application of different bioinformatics approaches have enabled the identification of novel mycoviruses not previously described in Botrytis species, expanding the already extensive list.

Breast cancer is a major health concern worldwide. Mammography, a cost-effective and accurate tool, is crucial in combating this issue. However, low contrast, noise, and artifacts can limit the diagnostic capabilities of radiologists. Computer-Aided Diagnosis (CAD) systems have been developed to overcome these challenges, with the accurate outlining of the breast being a critical step for further analysis. This study introduces the SAM-breast model, an adaptation of the Segment Anything Model (SAM) for segmenting the breast region in mammograms. This method enhances the delineation of the breast and the exclusion of the pectoral muscle in both medio lateral-oblique (MLO) and cranio-caudal (CC) views. We trained the models using a large, multi-center proprietary dataset of 2492 mammograms. The proposed SAM-breast model achieved the highest overall Dice Similarity Coefficient (DSC) of 99.22% ± 1.13 and Intersection over Union (IoU) 98.48% ± 2.10 over independent test images from five different datasets (two proprietary and three publicly available). The results are consistent across the different datasets, regardless of the vendor or image resolution. Compared with other baseline and deep learning-based methods, the proposed method exhibits enhanced performance. The SAM-breast model demonstrates the power of the SAM to adapt when it is tailored to specific tasks, in this case, the delineation of the breast in mammograms. Comprehensive evaluations across diverse datasets—both private and public—attest to the method’s robustness, flexibility, and generalization capabilities.

Abstract The cultivation of edible mushroom is an emerging sector with a potential yet to be discovered. Unlike plants, it is a less developed agriculture where many studies are lacking to optimize the cultivation. In this work we have employed high-throughput techniques by next generation sequencing to screen the microbial structure of casing soil employed in mushroom cultivation (Agaricus bisporus) while sequencing V3-V4 of the 16S rRNA gene for bacteria and the ITS2 region of rRNA for. In addition, the microbiota dynamics and evolution (bacterial and fungal communities) in peat-based casing along the process of incubation of A. bisporus have been studied, while comparing the effect of fungicide treatment (chlorothalonil and metrafenone). Statistically significant changes in populations of bacteria and fungi were observed. Microbial composition differed significantly based on incubation day, changing radically from the original communities in the raw material to a specific microbial composition driven by the A. bisporus mycelium growth. Chlorothalonil treatment seems to delay casing colonization by A. bisporus. Proteobacteria and Bacteroidota appeared as the most dominant bacterial phyla. We observed a great change in the structure of the bacteria populations between day 0 and the following days. Fungi populations changed more gradually, with A. bisporus displacing the rest of the species as the cultivation cycle progresses. A better understanding of the microbial communities in the casing will hopefully allow us to increase the biological efficiency of the crop.

Prostate cancer (PCa) is a major world-wide health concern. Current diagnostic methods involve Prostate-Specific Antigen (PSA) blood tests, biopsies, and Magnetic Resonance Imaging (MRI) to assess cancer aggressiveness and guide treatment decisions. MRI aligns with in silico medicine, as patient-specific image biomarkers can be obtained, contributing towards the development of digital twins for clinical practice. This work presents a novel framework to create a personalized PCa model by integrating clinical MRI data, such as the prostate and tumour geometry, the initial distribution of cells and the vasculature, so a full representation of the whole prostate is obtained. On top of the personalized model construction, our approach simulates and predicts temporal tumour growth in the prostate through the Finite Element Method, coupling the dynamics of tumour growth and the transport of oxygen, and incorporating cellular processes such as proliferation, differentiation, and apoptosis. In addition, our approach includes the simulation of the PSA dynamics, which allows to evaluate tumour growth through the PSA patient's levels. To obtain the model parameters, a multi-objective optimization process is performed to adjust the best parameters for two patients simultaneously. This framework is validated by means of data from four patients with several MRI follow-ups. The diagnosis MRI allows the model creation and initialization, while subsequent MRI-based data provide additional information to validate computational predictions. The model predicts prostate and tumour volumes growth, along with serum PSA levels. This work represents a preliminary step towards the creation of digital twins for PCa patients, providing personalized insights into tumour growth. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic represents a valuable opportunity to carry out cohort studies that allow us to advance our knowledge on pathophysiological mechanisms of neuropsychiatric diseases. One of these opportunities is the study of the relationships between inflammation, brain development and an increased risk of suffering neuropsychiatric disorders. Based on the hypothesis that neuroinflammation during early stages of life is associated with neurodevelopmental disorders and confers a greater risk of developing neuropsychiatric disorders, we propose a cohort study of SARS-CoV-2-infected pregnant women and their newborns. The main objective of SIGNATURE project is to explore how the presence of prenatal SARS-CoV-2 infection and other non-infectious stressors generates an abnormal inflammatory activity in the newborn. The cohort of women during the COVID-19 pandemic will be psychological and biological monitored during their pregnancy, delivery, childbirth and postpartum. The biological information of the umbilical cord (foetus blood) and peripheral blood from the mother will be obtained after childbirth. These samples and the clinical characterisation of the cohort of mothers and newborns, are tremendously valuable at this time. This is a protocol report and no analyses have been conducted yet, being currently at, our study is in the recruitment process step. At the time of this publication, we have identified 1,060 SARS-CoV-2 infected mothers and all have already given birth. From the total of identified mothers, we have recruited 537 SARS-COV-2 infected women and all of them have completed the mental health assessment during pregnancy. We have collected biological samples from 119 mothers and babies. Additionally, we have recruited 390 non-infected pregnant women.

We take advantage of the dual emission properties of up-conversion fluorescence (UCF) and second harmonic generation (SHG) in Er3+-doped perovskite Na0.95Er0.05Nb0.9Ti0.1O3 to fully temporally characterize the ultrashort laser pulse that excites Er3+-ion fluorescence. The chirped pulses from a broadband Ti:Sa oscillator are temporally characterized using the dispersion scan (d-scan) technique by using the SHG signal in the host perovskite at the same point where UCF is being produced by the same pulse. The pulse durations obtained range from ~45 fs to ~8 fs and positive and negative spectral phases are unambiguously identified. The temporal characterization is compared using a standard non-linear crystal and perfect agreement is obtained. These results show that it is possible to temporally characterize in-situ ultrashort laser pulses while they are inducing a UCF process, as long as the host generates second-harmonic signal.

Breast cancer is a major health concern worldwide. Mammography, a cost-effective and accurate tool, is crucial in combating this issue. However, low contrast, noise, and artifacts can limit the diagnostic capabilities of radiologists. Computer-Aided Diagnosis (CAD) systems have been developed to overcome these challenges, with the accurate outlining of the breast being a critical step for further analysis. This study introduces the SAM-breast model, an adaptation of the Segment Anything Model (SAM) for segmenting the breast region in mammograms. This method enhances the delineation of the breast and the exclusion of the pectoral muscle in both medio lateral-oblique (MLO) and cranio-caudal (CC) views. We trained the models using a large, multi-center proprietary dataset of 2492 mammograms. The proposed SAM-breast model achieved the highest overall Dice Similarity Coefficient (DSC) of 99.22% ± 1.13 and Intersection over Union (IoU) 98.48% ± 2.10 over independent test images from five different datasets (two proprietary and three publicly available). The results are consistent across the different datasets, regardless of the vendor or image resolution. Compared with other baseline and deep learning-based methods, the proposed method exhibits enhanced performance. The SAM-breast model demonstrates the power of the SAM to adapt when it is tailored to specific tasks, in this case, the delineation of the breast in mammograms. Comprehensive evaluations across diverse datasets—both private and public—attest to the method's robustness, flexibility, and generalization capabilities. [ABSTRACT FROM AUTHOR]

Botrytis cinerea is a necrotrophic plant pathogenic fungus with a wide host range. Its natural populations are phenotypically and genetically very diverse. A survey of B. cinerea isolates causing gray mold in the vineyards of Castilla y León, Spain, was carried out and as a result eight non-pathogenic natural variants were identified. Phenotypically these isolates belong to two groups. The first group consists of seven isolates displaying a characteristic mycelial morphotype, which do not sporulate and is unable to produce sclerotia. The second group includes one isolate, which sporulates profusely and does not produce sclerotia. All of them are unresponsive to light. Crosses between a representative mycelial non-pathogenic isolate and a highly aggressive field isolate revealed that the phenotypic differences regarding pathogenicity, sporulation and production of sclerotia cosegregated in the progeny and are determined by a single genetic locus. By applying a bulked segregant analysis strategy based on the comparison of the two parental genomes the locus was mapped to a 110 kb region in chromosome 4. Subcloning and transformation experiments revealed that the polymorphism is an SNP affecting gene Bcin04g03490 in the reference genome of B. cinerea. Genetic complementation analysis and sequencing of the Bcin04g03490 alleles demonstrated that the mutations in the mycelial isolates are allelic and informed about the nature of the alterations causing the phenotypes observed. Integration of the allele of the pathogenic isolate into the non-pathogenic isolate fully restored the ability to infect, to sporulate and to produce sclerotia. Therefore, it is concluded that a major effect gene controlling differentiation and developmental processes as well as pathogenicity has been identified in B. cinerea. It encodes a protein with a GAL4-like Zn(II)2Cys6 binuclear cluster DNA binding domain and an acetyltransferase domain, suggesting a role in regulation of gene expression through a mechanism involving acetylation of specific substrates.

Breast density assessed from digital mammograms is a known biomarker related to a higher risk of developing breast cancer. Supervised learning algorithms have been implemented to determine this. However, the performance of these algorithms depends on the quality of the ground-truth information, which expert readers usually provide. These expert labels are noisy approximations to the ground truth, as there is both intra- and inter-observer variability among them. Thus, it is crucial to provide a reliable method to measure breast density from mammograms. This paper presents a fully automated method based on deep learning to estimate breast density, including breast detection, pectoral muscle exclusion, and dense tissue segmentation. We propose a novel confusion matrix (CM)—YNet model for the segmentation step. This architecture includes networks to model each radiologist’s noisy label and gives the estimated ground-truth segmentation as well as two parameters that allow interaction with a threshold-based labeling tool. A multi-center study involving 1785 women whose “for presentation” mammograms were obtained from 11 different medical facilities was performed. A total of 2496 mammograms were used as the training corpus, and 844 formed the testing corpus. Additionally, we included a totally independent dataset from a different center, composed of 381 women with one image per patient. Each mammogram was labeled independently by two expert radiologists using a threshold-based tool. The implemented CM-Ynet model achieved the highest DICE score averaged over both test datasets (0.82±0.14) when compared to the closest dense-tissue segmentation assessment from both radiologists. The level of concordance between the two radiologists showed a DICE score of 0.76±0.17. An automatic breast density estimator based on deep learning exhibited higher performance when compared with two experienced radiologists. This suggests that modeling each radiologist’s label allows for better estimation of the unknown ground-truth segmentation. The advantage of the proposed model is that it also provides the threshold parameters that enable user interaction with a threshold-based tool.

Nitric oxide regulates numerous physiological processes in species from all taxonomic groups. Here, its role in the early developmental stages of the fungal necrotroph Botrytis cinerea was investigated. Pharmacological analysis demonstrated that NO modulated germination, germ tube elongation and nuclear division rate. Experimental evidence indicates that exogenous NO exerts an immediate but transitory negative effect, slowing down germination-associated processes, and that this effect is largely dependent on the flavohemoglobin BCFHG1. The fungus exhibited a “biphasic response” to NO, being more sensitive to low and high concentrations than to intermediate levels of the NO donor. Global gene expression analysis in the wild-type and ΔBcfhg1 strains indicated a situation of strong nitrosative and oxidative stress determined by exogenous NO, which was much more intense in the mutant strain, that the cells tried to alleviate by upregulating several defense mechanisms, including the simultaneous upregulation of the genes encoding the flavohemoglobin BCFHG1, a nitronate monooxygenase (NMO) and a cyanide hydratase. Genetic evidence suggests the coordinated expression of Bcfhg1 and the NMO coding gene, both adjacent and divergently arranged, in response to NO. Nitrate assimilation genes were upregulated upon exposure to NO, and BCFHG1 appeared to be the main enzymatic system involved in the generation of the signal triggering their induction. Comparative expression analysis also showed the influence of NO on other cellular processes, such as mitochondrial respiration or primary and secondary metabolism, whose response could have been mediated by NmrA-like domain proteins.

NEMA characterization of PET systems is generally based on 18F because it is the most relevant radioisotope for the clinical use of PET. 18F has a half-life of 109.7 min and decays into stable 18O via β+ emission with a probability of over 96% and a maximum positron energy of 0.633 MeV. Other commercially available PET radioisotopes, such as 82Rb and 68Ga have more complex decay schemes with a variety of prompt gammas, which can directly fall into the energy window and induce false coincidence detections by the PET scanner.MethodsAim of this work was three-fold: (A) Develop a GATE model of the GE Signa PET/MR to perform realistic and relevant Monte Carlo simulations (B) Validate this model with published sensitivity and Noise Equivalent Count Rate (NECR) data for 18F and 68Ga (C) Use the validated GATE-model to predict the system performance for other PET isotopes including 11C, 15O, 13N, 82Rb, and 68Ga and to evaluate the effect of a 3T magnetic field on the positron range.ResultsSimulated sensitivity and NECR tests performed with the GATE-model for different radioisotopes were in line with literature values. Simulated sensitivities for 18F and 68Ga were 21.2 and 19.0/kBq, respectively, for the center position and 21.1 and 19.0 cps/kBq, respectively, for the 10 cm off-center position compared to the corresponding measured values of 21.8 and 20.0 cps/kBq for the center position and 21.1 and 19.6 cps/kBq for the 10 cm off-center position. In terms of NECR, the simulated peak NECR was 216.8 kcps at 17.40 kBq/ml for 18F and 207.1 kcps at 20.10 kBq/ml for 68Ga compared to the measured peak NECR of 216.8 kcps at 18.60 kBq/ml and 205.6 kcps at 20.40 kBq/ml for18F and 68Ga, respectively. For 11C, 13N, and 15O, results confirmed a peak NECR similar to 18F with the effective activity concentration scaled by the inverse of the positron fraction. For 82Rb, and 68Ga, the peak NECR was lower than for 18F while the corresponding activity concentrations were higher. For the higher energy positron emitters, the positron range was confirmed to be tissue-dependent with a reduction of the positron range by a factor of 3 to 4 in the plane perpendicular to the magnetic field and an increased positron range along the direction of the magnetic field.ConclusionMonte-Carlo simulations were used to predict sensitivity and NECR performance of GE Signa PET/MR for 18F, 15O, 13N, 11C, 82Rb, and 68Ga radioisotopes and were in line with literature data. Simulations confirmed that sensitivity and NECR were influenced by the particular decay scheme of each isotope. As expected, the positron range decreased in the direction perpendicular to the 3T magnetic field. However, this will be only partially improving the resolution properties of a clinical PET/MR system due to the limiting spatial resolution of the PET detector.

Abstract Background G-protein-coupled receptor (GPCR) heteromeric complexes have distinct properties from homomeric GPCRs, giving rise to new receptor functionalities. Adenosine receptors (A1R or A2AR) can form A1R-A2AR heteromers (A1-A2AHet), and their activation leads to canonical G-protein-dependent (adenylate cyclase mediated) and -independent (β-arrestin mediated) signaling. Adenosine has different affinities for A1R and A2AR, allowing the heteromeric receptor to detect its concentration by integrating the downstream Gi- and Gs-dependent signals. cAMP accumulation and β-arrestin recruitment assays have shown that, within the complex, activation of A2AR impedes signaling via A1R. Results We examined the mechanism by which A1-A2AHet integrates Gi- and Gs-dependent signals. A1R blockade by A2AR in the A1-A2AHet is not observed in the absence of A2AR activation by agonists, in the absence of the C-terminal domain of A2AR, or in the presence of synthetic peptides that disrupt the heteromer interface of A1-A2AHet, indicating that signaling mediated by A1R and A2AR is controlled by both Gi and Gs proteins. Conclusions We identified a new mechanism of signal transduction that implies a cross-communication between Gi and Gs proteins guided by the C-terminal tail of the A2AR. This mechanism provides the molecular basis for the operation of the A1-A2AHet as an adenosine concentration-sensing device that modulates the signals originating at both A1R and A2AR.

A model based on local graphs to classify pixels coming from at or detail regions of an image is presented. For each pixel a local graph is dened. Its structure will depend on the similarity between neighbouring pixels. Its features allow us to classify each image pixel as belonging to one type of region or the other. This classication is an essential pre-processing technique for many Computer Vision tools, such as smoothingor sharpening of digital color images.

It is still a challenge to improve the efficiency and effectiveness of image denoising and enhancement methods. There exists denoising and enhancement methods that are able to improve visual quality of images. This is usually obtained by removing noise while sharpening details and improving edges contrast. Smoothing refers to the case of denoising when noise follows a Gaussian distribution.

We present two new methods for simultaneous smoothing and sharpening of color images: the GMS3 (Graph Method for Simultaneous Smoothing and Sharpening) and the NGMS3(Normalized Graph-Method for Simultaneous Smoothing and Sharpening). They are based on analyzing the structure of local graphs computed at every pixel using their respective neighbors. On the one hand, we define a kernel-based filter for smoothing each pixel with the pixels associated to nodes in its same connected component. On the other hand, we modify each pixel by increasing their differences with respect to the pixels in the other connected components of those local graphs. Our approach is shown to be competitive with respect to other state-of-the-art methods that simultaneously manage both processes. • We provide two methods that carry out the process of smoothing and sharpening simultaneously. • The methods are based on the analysis of the structure of a local graph defined from the differences in the RGB space among the pixels in a 3 × 3 window. • The parameters of the method are adjusted using both observers opinion and the well-known reference image quality assessment BRISQUE (Blind/Referenceless images spatial quality Evaluator) score.

ObjectiveTo evaluate the effects of Process-Reengineering interventions on the Electronic Health Records (EHR) of a hospital over 7 years.Materials and methodsTemporal Variability Assessment (TVA) based on probabilistic data quality assessment was applied to the historic monthly-batched admission data of Hospital La Fe Valencia, Spain from 2010 to 2016. Routine healthcare data with a complete EHR was expanded by processed variables such as the Charlson Comorbidity Index.ResultsFour Process-Reengineering interventions were detected by quantifiable effects on the EHR: (1) the hospital relocation in 2011 involved progressive reduction of admissions during the next four months, (2) the hospital services re-configuration incremented the number of inter-services transfers, (3) the care-services re-distribution led to transfers between facilities (4) the assignment to the hospital of a new area with 80,000 patients in 2015 inspired the discharge to home for follow up and the update of the pre-surgery planned admissions protocol that produced a significant decrease of the patient length of stay.DiscussionTVA provides an indicator of the effect of process re-engineering interventions on healthcare practice. Evaluating the effect of facilities' relocation and increment of citizens (findings 1, 3-4), the impact of strategies (findings 2-3), and gradual changes in protocols (finding 4) may help on the hospital management by optimizing interventions based on their effect on EHRs or on data reuse.ConclusionsThe effects on hospitals EHR due to process re-engineering interventions can be evaluated using the TVA methodology. Being aware of conditioned variations in EHR is of the utmost importance for the reliable reuse of routine hospitalization data.

The Pascal triangle is so simple and rich that it has always attracted the interest of professional and amateur mathematicians. Their coefficients satisfy a myriad of properties. Inspired by the work of Shekatkar et al., we study the divisibility patterns within the elements of the Pascal triangle, through its decomposition into Pascal’s matrices, from the perspective of network science. Applying Kolmogorov−Smirnov test, we determine that the degree distribution of the resulting network follows a power-law distribution. We also study degrees, global and local clustering coefficients, stretching graph, averaged path length and the mixing assortative.

We take advantage of the dual emission properties of up-conversion fluorescence (UCF) and second harmonic generation (SHG) in Er3+-doped perovskite Na0.95Er0.05Nb0.9Ti0.1O3 to fully temporally characterize the ultrashort laser pulse that excites Er3+-ion fluorescence. The chirped pulses from a broadband Ti:Sa oscillator are temporally characterized using the dispersion scan (d-scan) technique by using the SHG signal in the host perovskite at the same point where UCF is being produced by the same pulse. The pulse durations obtained range from ~45 fs to ~8 fs and positive and negative spectral phases are unambiguously identified. The temporal characterization is compared using a standard non-linear crystal and perfect agreement is obtained. These results show that it is possible to temporally characterize in-situ ultrashort laser pulses while they are inducing a UCF process, as long as the host generates second-harmonic signal. [ABSTRACT FROM AUTHOR]

Metabotropic glutamate (mGlu) receptors are a family of eight GPCRs that are attractive drug discovery targets to modulate glutamate action and response. Here we review the application of computational methods to the study of this family of receptors. X-ray structures of the extracellular and 7-transmembrane domains have played an important role to enable structure-based modeling approaches, whilst we also discuss the successful application of ligand-based methods. We summarize the literature and highlight the areas where modeling and experiment have delivered important understanding for mGlu receptor drug discovery. Finally, we offer suggestions of future areas of opportunity for computational work.