Docking Scoring Functions Research Articles

OpenDock: a pytorch-based open-source framework for protein-ligand docking and modelling.

Molecular docking is an invaluable computational tool with broad applications in computer-aided drug design and enzyme engineering. However, current molecular docking tools are typically implemented in languages such as C++ for calculation speed, which lack flexibility and user-friendliness for further development. Moreover, validating the effectiveness of external scoring functions for molecular docking and screening within these frameworks is challenging, and implementing more efficient sampling strategies is not straightforward. To address these limitations, we have developed an open-source molecular docking framework, OpenDock, based on Python and PyTorch. This framework supports the integration of multiple scoring functions; some can be utilized during molecular docking and pose optimization, while others can be used for post-processing scoring. In terms of sampling, the current version of this framework supports simulated annealing and Monte Carlo optimization. Additionally, it can be extended to include methods such as genetic algorithms and particle swarm optimization for sampling docking poses and protein side chain orientations. Distance constraints are also implemented to enable covalent docking, restricted docking or distance map constraints guided pose sampling. Overall, this framework serves as a valuable tool in drug design and enzyme engineering, offering significant flexibility for most protein-ligand modelling tasks. OpenDock is publicly available at: https://github.com/guyuehuo/opendock.

Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs.

Protein three-dimensional (3D) structure prediction is one of the most challenging issues in the field of computational biochemistry, which has overwhelmed scientists for almost half a century. A significant breakthrough in structural biology has been established by developing the artificial intelligence (AI) system AlphaFold2 (AF2). The AF2 system provides a state-of-the-art prediction of protein structures from nearly all known protein sequences with high accuracy. This study examined the reliability of AF2 models compared to the experimental structures in drug discovery, focusing on one of the most common protein drug-targeted classes known as G protein-coupled receptors (GPCRs) class A. A total of 32 representative protein targets were selected, including experimental structures of X-ray crystallographic and Cryo-EM structures and their corresponding AF2 models. The quality of AF2 models was assessed using different structure validation tools, including the pLDDT score, RMSD value, MolProbity score, percentage of Ramachandran favored, QMEAN Z-score, and QMEANDisCo Global. The molecular docking was performed using the Genetic Optimization for Ligand Docking (GOLD) software. The AF2 models' reliability in virtual drug screening was determined by their ability to predict the ligand binding poses closest to the native binding pose by assessing the Root Mean Square Deviation (RMSD) metric and docking scoring function. The quality of the docking and scoring function was evaluated using the enrichment factor (EF). Furthermore, the capability of using AF2 models in molecular docking to identify hits with key protein-ligand interactions was analyzed. The posing power results showed that the AF2 models successfully predicted ligand binding poses (RMSD < 2 Å). However, they exhibited lower screening power, with average EF values of 2.24, 2.42, and 1.82 for X-ray, Cryo-EM, and AF2 structures, respectively. Moreover, our study revealed that molecular docking using AF2 models can identify competitive inhibitors. In conclusion, this study found that AF2 models provided docking results comparable to experimental structures, particularly for certain GPCR targets, and could potentially significantly impact drug discovery.

Machine Learning Guided AQFEP: A Fast and Efficient Absolute Free Energy Perturbation Solution for Virtual Screening.

Structure-based methods in drug discovery have become an integral part of the modern drug discovery process. The power of virtual screening lies in its ability to rapidly and cost-effectively explore enormous chemical spaces to select promising ligands for further experimental investigation. Relative free energy perturbation (RFEP) and similar methods are the gold standard for binding affinity prediction in drug discovery hit-to-lead and lead optimization phases, but have high computational cost and the requirement of a structural analog with a known activity. Without a reference molecule requirement, absolute FEP (AFEP) has, in theory, better accuracy for hit ID, but in practice, the slow throughput is not compatible with VS, where fast docking and unreliable scoring functions are still the standard. Here, we present an integrated workflow to virtually screen large and diverse chemical libraries efficiently, combining active learning with a physics-based scoring function based on a fast absolute free energy perturbation method. We validated the performance of the approach in the ranking of structurally related ligands, virtual screening hit rate enrichment, and active learning chemical space exploration; disclosing the largest reported collection of free energy simulations to date.

Discovery of Natural Compounds as SARS-CoV-2’s Main Protease Inhibitors by Docking-based Virtual Screening

Introduction: The novel coronavirus disease (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SAR-CoV-2). The development of antiviral drugs has enhaced treatment of COVID-19. SARS-CoV-2 main protease (Mpro) is a key enzyme responsible for viral replication and transcription. This study aimed to identify new natural structures for the design of SARS-CoV-2 Mpro inhibitors. Methods: In this present work, The CDOCKER protocol and scoring functions were validated. The validated docking-based virtual screening approach was then employed to search the in-house database of natural compounds for potential lead compounds as SARS-CoV-2 Mpro inhibitors. The top 3 compounds were further biologically evaluated in vitro. Results and Discussion: Docking studies of the known ligand GC-376 led to results consistent with cocrystallized data (PDB ID: 7D1M). Additionally, the effectiveness of docking scoring functions was validated by using the training set consisting of 15 active compounds and 15 inactive compounds. Then, the in-house database of natural compounds (overall 34,439 natural compounds) was subjected to dockingbased virtual screening resulting in the identification of the top 100 compounds having relatively better docking scores. Among them, the highest ranking 3 compounds (W-1, W-2, and W-3) were biologically evaluated in vitro for their inhibitory activity against SARS-CoV-2 Mpro, and compound W-1 was identified as the most potent SARS-CoV-2 Mpro inhibitor with an IC50 value of 63 ± 3 μM. Interestingly, it appeared that the in vitro activities of compounds W-1, W-2, and W-3 were in agreement with their molecular modeling data. Conclusion: Our results provided a useful reference for the discovery of novel natural SARS-CoV-2 Mpro inhibitors by virtual screening.

Shedding Light on Dark Chemical Matter: The Discovery of a SARS-CoV-2 Mpro Main Protease Inhibitor through Intensive Virtual Screening and In Vitro Evaluation.

The development of specific antiviral therapies targeting SARS-CoV-2 remains fundamental because of the continued high incidence of COVID-19 and limited accessibility to antivirals in some countries. In this context, dark chemical matter (DCM), a set of drug-like compounds with outstanding selectivity profiles that have never shown bioactivity despite being extensively assayed, appears to be an excellent starting point for drug development. Accordingly, in this study, we performed a high-throughput screening to identify inhibitors of the SARS-CoV-2 main protease (Mpro) using DCM compounds as ligands. Multiple receptors and two different docking scoring functions were employed to identify the best molecular docking poses. The selected structures were subjected to extensive conventional and Gaussian accelerated molecular dynamics. From the results, four compounds with the best molecular behavior and binding energy were selected for experimental testing, one of which presented inhibitory activity with a Ki value of 48 ± 5 μM. Through virtual screening, we identified a significant starting point for drug development, shedding new light on DCM compounds.

Design and functional characterization of Salmo salar TLR5 agonist peptides derived from high mobility group B1 acidic tail

Toll-like receptor 5 (TLR5) responds to the monomeric form of flagellin and induces the MyD88-depending signaling pathway, activating proinflammatory transcription factors such as NF-κB and the consequent induction of cytokines. On the other hand, HMGB1 is a highly conserved non-histone chromosomal protein shown to interact with and activate TLR5. The present work aimed to design and characterize TLR5 agonist peptides derived from the acidic tail of Salmo salar HMGB1 based on the structural knowledge of the TLR5 surface using global molecular docking platforms. Peptide binding poses complexed on TLR5 ectodomain model from each algorithm were filtrated based on docking scoring functions and predicted theoretical binding affinity of the complex. Circular dichroism spectra were recorded for each peptide selected for synthesis. Only intrinsically disordered peptides (6W, 11W, and SsOri) were selected for experimental functional assay. The functional characterization of the peptides was performed by NF-κB activation assays, RT-qPCR gene expression assays, and Piscirickettsia salmonis challenge in SHK-1 cells. The 6W and 11W peptides increased the nuclear translation of p65 and phosphorylation. In addition, the peptides induced the expression of genes related to the TLR5 pathway activation, pro- and anti-inflammatory response, and differentiation and activation of T lymphocytes towards phenotypes such as TH1, TH17, and TH2. Finally, it was shown that the 11W peptide protects immune cells against infection with P. salmonis bacteria. Overall, the results indicate the usefulness of novel peptides as potential immunostimulants in salmonids.

Agonistic effect of peptides derived from a truncated HMGB1 acidic tail sequence in TLR5 from Salmo salar

Based on the structural knowledge of TLR5 surface and using blind docking platforms, peptides derived from a truncated HMGB1 acidic tail from Salmo salar was designed as TLR5 agonistic. Additionally, a template peptide with the native N-terminal of the acidic tail sequence as a reference was included (SsOri). Peptide binding poses complexed on TLR5 ectodomain model from each algorithm were filtrated based on docking scoring functions and predicted theoretical binding affinity of the complex. The best peptides, termed 6WK and 5LWK, were selected for chemical synthesis and experimental functional assay. The agonist activity by immunoblotting and immunocytochemistry was determined following the NF-κBp65 phosphorylation (p–NF–κBp65) and the nuclear translocation of the NF-κBp65 subunit from the cytosol, respectively. HeLa cells stably expressing a S. salar TLR5 chimeric form (TLR5c7) showed increased p–NF–κBp65 levels regarding extracts from flagellin-treated cells. No statistically significant differences (p > 0.05) were found in the detected p–NF–κBp65 levels between cellular extracts treated with peptides or flagellin by one-way ANOVA. The image analysis of NF-κBp65 immunolabeled cells obtained by confocal microscopy showed increased nuclear NF-κBp65 co-localization in cells both 5LWK and flagellin stimulated, while 6WK and SsOri showed less effect on p65 nuclear translocation (p < 0.05). Also, an increased transcript expression profile of proinflammatory cytokines such as TNFα, IL-1β, and IL-8 in HKL cells isolated from Salmo salar was evidenced in 5LWK - stimulated by RT-PCR analysis. Overall, the result indicates the usefulness of novel peptides as a potential immunostimulant in S. salar.

Moving out of CF3 -Land: Synthesis, Receptor Affinity, and in silico Studies of NK1 Receptor Ligands Containing a Pentafluorosulfanyl (SF5 ) Group.

The NK1 receptor (NK1R) is a molecular target for both approved and experimental drugs intended for a variety of conditions, including emesis, pain, and cancers. While contemplating modifications to the typical NK1R pharmacophore, we wondered whether the CF3 groups common for many NK1R ligands, could be replaced with some other moiety. Our attention was drawn by the SF5 group, and so we designed, synthesized, and tested ten novel SF5 -containing compounds for NK1R affinity. All analogues exhibit detectable NK1R binding, with the best of them, compound 5 a, (3-bromo-5-(pentafluoro-λ6 -sulfanyl)benzyl acetyl-L-tryptophanate) binding only slightly worse (IC50 =34.3 nM) than the approved NK1R-targeting drug, aprepitant (IC50 =27.7 nM). Molecular docking provided structural explanation of SAR. According to our analysis, the SF5 group in our compounds occupies a position similar to that of one of the CF3 groups of aprepitant as found in the crystal structure. Additionally, we checked whether the docking scoring function or energies derived from Fragment Molecular Orbital quantum chemical calculations may be helpful in explaining and predicting the experimental receptor affinities for our analogues. Both these methods produce moderately good results. Overall, this is the first demonstration of the utility of the SF5 group in the design of NK1R ligands.

ViTScore: a novel three-dimensional Vision Transformer method for accurate prediction of protein-ligand docking poses.

Protein-ligand interactions (PLIs) are essential for cellular activities and drug discovery, and due to the complexity and high cost of experimental methods, there is a great demand for computational approaches, such as protein-ligand docking, to decipher PLI patterns. One of the most challenging aspects of protein-ligand docking is to identify near-native conformations from a set of poses, but traditional scoring functions still have limited accuracy. Therefore, new scoring methods are urgently needed for methodological and/or practical implications. We present a novel deep learning-based scoring function for ranking protein-ligand docking poses based on Vision Transformer (ViT), named ViTScore. To recognize near-native poses from a set of poses, ViTScore voxelizes the protein-ligand interactional pocket into a 3D grid labeled by the occupancy contribution of atoms in different physicochemical classes. This allows ViTScore to capture the subtle differences between spatially and energetically favorable near-native poses and unfavorable non-native poses without needing extra information. After that, ViTScore will output the prediction of the root mean square deviation (rmsd) of a docking pose with reference to the native binding pose. ViTScore is extensively evaluated on diverse test sets including PDBbind2019 and CASF2016, and obtains significant improvements over existing methods in terms of RMSE, R and docking power. Moreover, the results demonstrate that ViTScore is a promising scoring function for protein-ligand docking, and it can be used to accurately identify near-native poses from a set of poses. Furthermore, the results suggest that ViTScore is a powerful tool for protein-ligand docking, and it can be used to accurately identify near-native poses from a set of poses. Additionally, ViTScore can be used to identify potential drug targets and to design new drugs with improved efficacy and safety.

Protein-Ligand CH-π Interactions: Structural Informatics, Energy Function Development, and Docking Implementation.

Here, we develop an empirical energy function based on quantum mechanical data for the interaction between methane and benzene that captures the contribution from CH-π interactions. Such interactions are frequently observed in protein-ligand crystal structures, particularly for carbohydrate ligands, but have been hard to quantify due to the absence of a model for CH-π interactions in typical molecular mechanical force fields or docking scoring functions. The CH-π term was added to the AutoDock Vina (AD VINA) scoring function enabling its performance to be evaluated against a cohort of more than 1600 occurrences in 496 experimental structures of protein-ligand complexes. By employing a conformational grid search algorithm, inclusion of the CH-π term was shown to improve the prediction of the preferred orientation of flexible ligands in protein-binding sites and to enhance the detection of carbohydrate-binding sites that display CH-π interactions. Last but not least, this term was also shown to improve docking performance for the CASF-2016 benchmark set and a carbohydrate set.

Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-Generated Protein-Ligand Structures: Towards Per-Target Scoring Functions.

In recent years, machine learning has been proposed as a promising strategy to build accurate scoring functions for computational docking finalized to numerically empowered drug discovery. However, the latest studies have suggested that over-optimistic results had been reported due to the correlations present in the experimental databases used for training and testing. Here, we investigate the performance of an artificial neural network in binding affinity predictions, comparing results obtained using both experimental protein-ligand structures as well as larger sets of computer-generated structures created using commercial software. Interestingly, similar performances are obtained on both databases. We find a noticeable performance suppression when moving from random horizontal tests to vertical tests performed on target proteins not included in the training data. The possibility to train the network on relatively easily created computer-generated databases leads us to explore per-target scoring functions, trained and tested ad-hoc on complexes including only one target protein. Encouraging results are obtained, depending on the type of protein being addressed.

GDockScore: a graph-based protein-protein docking scoring function.

Protein complexes play vital roles in a variety of biological processes, such as mediating biochemical reactions, the immune response and cell signalling, with 3D structure specifying function. Computational docking methods provide a means to determine the interface between two complexed polypeptide chains without using time-consuming experimental techniques. The docking process requires the optimal solution to be selected with a scoring function. Here, we propose a novel graph-based deep learning model that utilizes mathematical graph representations of proteins to learn a scoring function (GDockScore). GDockScore was pre-trained on docking outputs generated with the Protein Data Bank biounits and the RosettaDock protocol, and then fine-tuned on HADDOCK decoys generated on the ZDOCK Protein Docking Benchmark. GDockScore performs similarly to the Rosetta scoring function on docking decoys generated using the RosettaDock protocol. Furthermore, state-of-the-art is achieved on the CAPRI score set, a challenging dataset for developing docking scoring functions. The model implementation is available at https://gitlab.com/mcfeemat/gdockscore. Supplementary data are available at Bioinformatics Advances online.

Application of Molecular Dynamics to Expand Docking Program's Exploratory Capabilities and to Evaluate Its Predictions.

Recognition of the growing importance of RNA as a target for therapeutic or diagnostic ligands brings the importance of computational predictions of docking poses to such receptors to the forefront. Most docking programs have been optimized for protein targets, based on a relatively rich pool of known docked protein structures. Unfortunately, despite progress, numbers of known docked RNA complexes are low and the accuracy of the computational predictions trained on those inadequate samples lags behind that achieved for proteins. Compared to proteins, RNA structures generally have fewer docking pockets, have less diverse electrostatic surfaces, and are more flexible, raising the possibility of producing only transiently available good docking targets. We are presenting a docking prediction protocol that adds molecular dynamics simulations before and after the actual docking in order to explore the conformational space of the target RNA and then to reevaluate the stability of the predicted RNA-ligand complex. In this way we are attempting to overcome important limitations of the docking programs: the rigid (fully or mostly) target structure and imperfect nature of the docking scoring functions.

Machine learning approaches and their applications in drug discovery and design.

This review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to improve drug discovery. Many physicochemical properties of drugs like toxicity, absorption, drug-drug interaction, carcinogenesis, and distribution have been effectively modeled by QSAR techniques. Machine learning is a subset of artificial intelligence, and this technique has shown tremendous potential in the field of drug discovery. Techniques discussed in this review are capable of modeling non-linear datasets, as well as big data of increasing depth and complexity. Various machine learning-based approaches are being used for drug target prediction, modeling the structure of drug target, binding site prediction, ligand-based similarity searching, de novo designing of ligands with desired properties, developing scoring functions for molecular docking, building QSAR model for biological activity prediction, and prediction of pharmacokinetic and pharmacodynamic properties of ligands. In recent years, these predictive tools and models have achieved good accuracy. By the use of more related input data, relevant parameters, and appropriate algorithms, the accuracy of these predictions can be further improved.

CSM-carbohydrate: protein-carbohydrate binding affinity prediction and docking scoring function.

Protein-carbohydrate interactions are crucial for many cellular processes but can be challenging to biologically characterise. To improve our understanding and ability to model these molecular interactions, we used a carefully curated set of 370 protein-carbohydrate complexes with experimental structural and biophysical data in order to train and validate a new tool, cutoff scanning matrix (CSM)-carbohydrate, using machine learning algorithms to accurately predict their binding affinity and rank docking poses as a scoring function. Information on both protein and carbohydrate complementarity, in terms of shape and chemistry, was captured using graph-based structural signatures. Across both training and independent test sets, we achieved comparable Pearson's correlations of 0.72 under cross-validation [root mean square error (RMSE) of 1.58 Kcal/mol] and 0.67 on the independent test (RMSE of 1.72 Kcal/mol), providing confidence in the generalisability and robustness of the final model. Similar performance was obtained across mono-, di- and oligosaccharides, further highlighting the applicability of this approach to the study of larger complexes. We show CSM-carbohydrate significantly outperformed previous approaches and have implemented our method and make all data freely available through both a user-friendly web interface and application programming interface, to facilitate programmatic access at http://biosig.unimelb.edu.au/csm_carbohydrate/. We believe CSM-carbohydrate will be an invaluable tool for helping assess docking poses and the effects of mutations on protein-carbohydrate affinity, unravelling important aspects that drive binding recognition.

CSM-AB: graph-based antibody-antigen binding affinity prediction and docking scoring function.

Understanding antibody-antigen interactions is key to improving their binding affinities and specificities. While experimental approaches are fundamental for developing new therapeutics, computational methods can provide quick assessment of binding landscapes, guiding experimental design. Despite this, little effort has been devoted to accurately predicting the binding affinity between antibodies and antigens and to develop tailored docking scoring functions for this type of interaction. Here, we developed CSM-AB, a machine learning method capable of predicting antibody-antigen binding affinity by modelling interaction interfaces as graph-based signatures. CSM-AB outperformed alternative methods achieving a Pearson's correlation of up to 0.64 on blind tests. We also show CSM-AB can accurately rank near-native poses, working effectively as a docking scoring function. We believe CSM-AB will be an invaluable tool to assist in the development of new immunotherapies. CSM-AB is freely available as a user-friendly web interface and API at http://biosig.unimelb.edu.au/csm_ab/datasets. Supplementary data are available at Bioinformatics online.

Machine-learning scoring functions trained on complexes dissimilar to the test setalready outperform classical counterparts on a blind benchmark.

The superior performance of machine-learning scoring functions for docking has caused a series of debates on whether it is due to learning knowledge from training data that are similar in some sense to the test data. With a systematically revised methodology and a blind benchmark realistically mimicking the process of prospective prediction of binding affinity, we have evaluated three broadly used classical scoring functions and five machine-learning counterparts calibrated with both random forest and extreme gradient boosting using both solo and hybrid features, showing for the first time that machine-learning scoring functions trained exclusively on a proportion of as low as 8% complexes dissimilar to the test set already outperform classical scoring functions, a percentage that is far lower than what has been recently reported on all the three CASF benchmarks. The performance of machine-learning scoring functions is underestimated due to the absence of similar samples in some artificially created training sets that discard the full spectrum of complexes to be found in a prospective environment. Given the inevitability of any degree of similarity contained in a large dataset, the criteria for scoring function selection depend on which one can make the best use of all available materials. Software code and data are provided at https://github.com/cusdulab/MLSF for interested readers to rapidly rebuild the scoring functions and reproduce our results, even to make extended analyses on their own benchmarks.

Improving Docking Power for Short Peptides Using Random Forest.

In recent years, therapeutic peptides have gained a lot interest as demonstrated by the 60 peptides approved as drugs in major markets and 150+ peptides currently in clinical trials. However, while small molecule docking is routinely used in rational drug design efforts, docking peptides has proven challenging partly because docking scoring functions, developed and calibrated for small molecules, perform poorly for these molecules. Here, we present random forest classifiers trained to discriminate correctly docked peptides. We show that, for a testing set of 47 protein-peptide complexes, structurally dissimilar from the training set and previously used to benchmark AutoDock Vina's ability to dock short peptides, these random forest classifiers improve docking power from ∼25% for AutoDock scoring functions to an average of ∼70%. These results pave the way for peptide-docking success rates comparable to those of small molecule docking. To develop these classifiers, we compiled the ProptPep37_2021 data set, a curated, high-quality set of 322 crystallographic protein-peptides complexes annotated with structural similarity information. The data set also provides a collection of high-quality putative poses with a range of deviations from the crystallographic pose, providing correct and incorrect poses (i.e., decoys) of the peptide for each entry. The ProptPep37_2021 data set as well as the classifiers presented here are freely available.

DockingApp RF: A State-of-the-Art Novel Scoring Function for Molecular Docking in a User-Friendly Interface to AutoDock Vina.

Motivation: Bringing a new drug to the market is expensive and time-consuming. To cut the costs and time, computer-aided drug design (CADD) approaches have been increasingly included in the drug discovery pipeline. However, despite traditional docking tools show a good conformational space sampling ability, they are still unable to produce accurate binding affinity predictions. This work presents a novel scoring function for molecular docking seamlessly integrated into DockingApp, a user-friendly graphical interface for AutoDock Vina. The proposed function is based on a random forest model and a selection of specific features to overcome the existing limits of Vina’s original scoring mechanism. A novel version of DockingApp, named DockingApp RF, has been developed to host the proposed scoring function and to automatize the rescoring procedure of the output of AutoDock Vina, even to nonexpert users. Results: By coupling intermolecular interaction, solvent accessible surface area features and Vina’s energy terms, DockingApp RF’s new scoring function is able to improve the binding affinity prediction of AutoDock Vina. Furthermore, comparison tests carried out on the CASF-2013 and CASF-2016 datasets demonstrate that DockingApp RF’s performance is comparable to other state-of-the-art machine-learning- and deep-learning-based scoring functions. The new scoring function thus represents a significant advancement in terms of the reliability and effectiveness of docking compared to AutoDock Vina’s scoring function. At the same time, the characteristics that made DockingApp appealing to a wide range of users are retained in this new version and have been complemented with additional features.

High-Throughput Docking Using Quantum Mechanical Scoring.

Today high-throughput docking is one of the most commonly used computational tools in drug lead discovery. While there has been an impressive methodological improvement in docking accuracy, docking scoring still remains an open challenge. Most docking programs are rooted in classical molecular mechanics. However, to better characterize protein-ligand interactions, the use of a more accurate quantum mechanical (QM) description would be necessary. In this work, we introduce a QM-based docking scoring function for high-throughput docking and evaluate it on 10 protein systems belonging to diverse protein families, and with different binding site characteristics. Outstanding results were obtained, with our QM scoring function displaying much higher enrichment (screening power) than a traditional docking method. It is acknowledged that developments in quantum mechanics theory, algorithms and computer hardware throughout the upcoming years will allow semi-empirical (or low-cost) quantum mechanical methods to slowly replace force-field calculations. It is thus urgently needed to develop and validate novel quantum mechanical-based scoring functions for high-throughput docking toward more accurate methods for the identification and optimization of modulators of pharmaceutically relevant targets.