Virtual Screening Campaign Research Articles

Computational Approach to Identifying New Chemical Entities as Elastase Inhibitors with Potential Antiaging Effects.

In the aging process, skin morphology might be affected by wrinkle formation due to the loss of elasticity and resilience of connective tissues linked to the cleavage of elastin by the enzymatic activity of elastase. Little information is available about the structural requirements to efficiently inhibit elastase 1 (EC 3.4.21.36) expressed in skin keratinocytes. In this study, a structure-based approach led to the identification to the pharmacophoric hypotheses that described the main structural requirements for binding to porcine pancreatic elastase as a valuable tool for the development of skin therapeutic agents due to its similarity with human elastase 1. The obtained models were subsequently refined through the application of computational alanine-scanning mutagenesis to evaluate the effect of single residues on the binding affinity and protein stability; in turn, molecular dynamic simulations were carried out; these procedures led to a simplified model bearing few essential features, enabling a reliable collection of chemical features for their interactions with elastase. Then, a virtual screening campaign on the in-house library of synthetic compounds led to the identification of a nonpeptide-based inhibitor (IC50 = 60.4 µM) belonging to the class of N-substituted-1H-benzimidazol-2-yl]thio]acetamides, which might be further exploited to obtain more efficient ligands of elastase for therapeutic applications.

An Open-Source Implementation of the Scaffold Identification and Naming System (SCINS) and Example Applications.

Organizing and partitioning sets of chemical structures is of considerable practical significance, e.g., in compound library analysis and the postprocessing of screening hit lists. Approaches such as unsupervised clustering are computationally demanding and dataset-dependent; on the other hand, rule-based methods, such as those based on Murcko scaffolds, have linear time complexity but are often too fine-grained, leading to a large number of singletons or sparsely populated classes. An alternative rule-based method that seeks to achieve an optimal balance when grouping compounds into sets is the 'Scaffold Identification and Naming System' (SCINS). To facilitate public use of this previously published method, here, we provide an open-source Python implementation of SCINS, dependent only on RDKit. We show that SCINS can be useful in identifying sparsely and densely populated regions in chemical space in large databases, here exemplified with Enamine REAL Diverse and ChEMBL. We find that Enamine REAL Diverse covers a much smaller SCINS space relative to ChEMBL, whereas the opposite is true when Murcko and generic Murcko scaffolds are considered. Additionally, we show that SCINS can result in chemically intuitive grouping of medium-sized sets of bioactive compounds, which can be useful in compound selection from virtual screening campaigns as well as postprocessing of experimental hit lists. Hence, in this work, we provide both an open-source implementation of SCINS and its characterization with relevant use cases.

An ANI-2 enabled open-source protocol to estimate ligand strain after docking.

In protein-ligand docking, the score assigned to a protein-ligand complex is approximate. Especially, the internal energy of the ligand is difficult to compute precisely using a molecular mechanics based force-field, introducing significant noise in the rank-ordering of ligands. We propose an open-source protocol (https://github.com/UnixJunkie/MMO), using two quantum mechanics (QM) single point energy calculations, plus a Monte Carlo (Monte Carlo) based ligand minimization procedure in-between, to estimate ligand strain after docking. The MC simulation uses the ANI-2x (QM approximating) force field and is performed in the dihedral space. On some protein targets, using strain filtering after docking allows to significantly improve hit rates. We performed a structure-based virtual screening campaign on nine protein targets from the Laboratoire d'Innovation Thérapeutique-PubChem assays dataset using Cambridge crystallographic data centre genetic optimization for ligand docking. Then, docked ligands were submitted to the strain estimation protocol and the impact on hit rate was analyzed. As for docking, the method does not always work. However, if sufficient active and inactive molecules are known for a given protein target, its efficiency can be evaluated.

Discovery of Potent Covalent CRM1 Inhibitors Via a Customized Structure-Based Virtual Screening Pipeline and Bioassays.

CRM1 (chromosomal region maintenance 1, also referred to as exportin 1 or XPO1) plays a crucial role in maintaining the appropriate nuclear levels of tumor suppressor proteins (TSPs), growth regulatory proteins (GRPs), and antiapoptotic proteins, thereby contributing significantly to their anticancer effects. Dysregulation of CRM1-mediated nuclear transport, observed in a range of cancers such as colon cancer as well as autoimmune diseases, highlights its significance in various disease processes. In this paper, we employed a customized structure-based virtual screening campaign to search for novel covalent CRM1 inhibitors and purchased 50 potentially active compounds for in vitro bioassays. Among these candidates, AN-988 displayed a notably higher binding affinity (KD = 615 nM) toward CRM1, as determined by the biolayer interferometry (BLI) assay. Furthermore, AN-988 exhibited a strong suppression of colorectal cancer cell proliferation and remarkable anti-inflammatory effects. Notably, AN-988 induced cell apoptosis and cell cycle arrest in a time- and dose-dependent manner by effectively inhibiting the translocation of FOXO3a from the nucleus to the cytosol, thereby preserving the activity of FOXO3a. Collectively, our study identified AN-988 as a promising CRM1 inhibitor, underscoring its potential as a preclinical colon cancer therapy candidate.

Approaching Pharmacological Space: Events and Components.

The pharmacological space comprises all the dynamic events that determine the bioactivity (and/or the metabolism and toxicity) of a given ligand. The pharmacological space accounts for the structural flexibility and property variability of the two interacting molecules as well as for the mutual adaptability characterizing their molecular recognition process. The dynamic behavior of all these events can be described by a set of possible states (e.g., conformations, binding modes, isomeric forms) that the simulated systems can assume. For each monitored state, a set of state-dependent ligand- and structure-based descriptors can be calculated. Instead of considering only the most probable state (as routinely done), the pharmacological space proposes to consider all the monitored states. For each state-dependent descriptor, the corresponding space can be evaluated by calculating various dynamic parameters such as mean and range values.The reviewed examples emphasize that the pharmacological space can find fruitful applications in structure-based virtual screening as well as in toxicity prediction. In detail, in all reported examples, the inclusion of the pharmacological space parameters enhances the resulting performances. Beneficial effects are obtained by combining both different binding modes to account for ligand mobility and different target structures to account for protein flexibility/adaptability.The proposed computational workflow that combines docking simulations and rescoring analyses to enrich the arsenal of docking-based descriptors revealed a general applicability regardless of the considered target and utilized docking engine. Finally, the EFO approach that generates consensus models by linearly combining various descriptors yielded highly performing models in all discussed virtual screening campaigns.

Structural, kinetic, and evolutionary peculiarities of HISN3, a plant 5′-ProFAR isomerase

Histidine biosynthesis is essential for the growth and development of plants, where it occurs within chloroplasts. The eleven reactions are catalyzed by eight enzymes, known as HISN1-8, each acting sequentially. Here, we present the crystal structures of a 5′-ProFAR isomerase (HISN3) from the model legume Medicago truncatula bound to its enzymatically synthesized substrate (ProFAR) and product (PrFAR). The active site of MtHISN3 contains a sodium cation that participates in ligand recognition, a feature not observed in bacterial and fungal structures of homologous enzymes. The steady-state kinetics of wild-type MtHISN3 revealed a slightly higher turnover rate compared to its bacterial homologs. Plant HISN3 sequences contain an unusually elongated Lys60-Ser91 fragment, while deletion of the 74–80 region resulted in a 30-fold loss in catalytic efficiency compared to the wild-type. Molecular dynamics simulations suggested that the fragment facilitates product release, thereby contributing to a higher kcat. Moreover, conservation analyses suggested a non-cyanobacterial origin for plant HISN3 enzymes, which is another instance of a non-cyanobacterial enzyme in the plant histidine biosynthetic pathway. Finally, a virtual screening campaign yielded five molecules, with the energy gains ranging between −13.6 and −13.1 kcal/mol, which provide new scaffolds for the future development of herbicides.

Searching for Novel HDAC6/Hsp90 Dual Inhibitors with Anti-Prostate Cancer Activity: In Silico Screening and In Vitro Evaluation.

Prostate cancer (PCA) is one of the most prevalent types of male cancers. While current treatments for early-stage PCA are available, their efficacy is limited in advanced PCA, mainly due to drug resistance or low efficacy. In this context, novel valuable therapeutic opportunities may arise from the combined inhibition of histone deacetylase 6 (HDAC6) and heat shock protein 90 (Hsp90). These targets are mutually involved in the regulation of several processes in cancer cells, and their inhibition is demonstrated to provide synergistic effects against PCA. On these premises, we performed an extensive in silico virtual screening campaign on commercial compounds in search of dual inhibitors of HDAC6 and Hsp90. In vitro tests against recombinant enzymes and PCA cells with different levels of aggressiveness allowed the identification of a subset of compounds with inhibitory activity against HDAC6 and antiproliferative effects towards LNCaP and PC-3 cells. None of the candidates showed appreciable Hsp90 inhibition. However, the discovered compounds have low molecular weight and a chemical structure similar to that of potent Hsp90 blockers. This provides an opportunity for structural and medicinal chemistry optimization in order to obtain HDAC6/Hsp90 dual modulators with antiproliferative effects against prostate cancer. These findings were discussed in detail in the study.

Structural analysis and shape-based identification of novel inhibitors targeting the Trypanosoma cruzi proteasome

There is an urgent need to develop new, safer, and more effective drugs against Chagas disease (CD) as well as related kinetoplastid diseases. Targeting and inhibiting the Trypanosoma cruzi proteasome has emerged as a promising therapeutic approach in this context. To expand the chemical space for this class of inhibitors, we performed virtual screening campaigns with emphasis on shape-based similarity and ADMET prioritization. We describe the ideation and application of robustly validated shape queries for these campaigns, which furnished 44 compounds for biological evaluation. Five hit compounds demonstrated in vitro antitrypanosomal activity by potential inhibition of T. cruzi proteasome and notable chemical diversities, particularly, LCQFTC11. Structural insights were achieved by homology modeling, sequence/structure alignment, proteasome-species comparison, docking, molecular dynamics, and MMGBSA binding affinity estimations. These methods confirmed key interactions as well as the stability of LCQFTC11 at the β4/β5 subunits' binding site of the T. cruzi proteasome, consistent with known inhibitors. Our results warrant future assay confirmation of our hit as a T. cruzi proteasome inhibitor. Importantly, we also shed light into dynamic details for a proteasome inhibition mechanism that shall be further investigated. We expect to contribute to the development of viable CD drug candidates through such a relevant approach.

Ensemble docking based virtual screening of SARS-CoV-2 main protease inhibitors.

During the first years of COVID-19 pandemic, X-ray structures of the coronavirus drug targets were acquired at an unprecedented rate, giving hundreds of PDB depositions in less than a year. The main protease (Mpro) of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) is the primary validated target of direct-acting antivirals. The selection of the optimal ensemble of structures of Mpro for the docking-driven virtual screening campaign was thus non-trivial and required a systematic and automated approach. Here we report a semi-automated active site RMSD based procedure of ensemble selection from the SARS-CoV-2 Mpro crystallographic data and virtual screening of its inhibitors. The procedure was compared with other approaches to ensemble selection and validated with the help of hand-picked and peer-reviewed activity-annotated libraries. Prospective virtual screening of non-covalent Mpro inhibitors resulted in a new chemotype of thienopyrimidinone derivatives with experimentally confirmed enzyme inhibition.

Relevance of G protein-coupled receptor (GPCR) dynamics for receptor activation, signalling bias and allosteric modulation.

G protein-coupled receptors (GPCRs) are one of the major drug targets. In recent years, computational drug design for GPCRs has mainly focused on static structures obtained through X-ray crystallography, cryogenic electron microscopy (cryo-EM) or in silico modelling as a starting point for virtual screening campaigns. However, GPCRs are highly flexible entities with the ability to adopt different conformational states that elicit different physiological responses. Including this knowledge in the drug discovery pipeline can help to tailor novel conformation-specific drugs with an improved therapeutic profile. In this review, we outline our current knowledge about GPCR dynamics that is relevant for receptor activation, signalling bias and allosteric modulation. Ultimately, we highlight new technological implementations such as time-resolved X-ray crystallography and cryo-EM as well as computational algorithms that can contribute to a more comprehensive understanding of receptor dynamics and its relevance for GPCR functionality.

NMR structures of small molecules bound to a model of an RNA CUG repeat expansion.

Trinucleotide repeat expansions fold into long, stable hairpins and cause a variety of incurable RNA gain-of-function diseases such as Huntington's disease, the myotonic dystrophies, and spinocerebellar ataxias. One approach for treating these diseases is to bind small molecules to the structured RNAs. Both Huntington's disease-like 2 (HDL2) and myotonic dystrophy type 1 (DM1) are caused by a r(CUG) repeat expansion, or r(CUG)exp. The RNA folds into a hairpin structure with a periodic array of 1×1 nucleotide UU loops (5'CUG/3'GUC; where the underlined nucleotides indicate the Us in the internal loop) that sequester various RNA-binding proteins (RBP) and hence the source of its gain-of-function. Here, we report NMR-refined structures of single 5'CUG/3'GUC motifs in complex with three different small molecules, a di-guandinobenzoate (1), a derivative of 1 where the guanidino groups have been exchanged for imidazole (2), and a quinoline with improved drug-like properties (3). These structures were determined using nuclear magnetic resonance (NMR) spectroscopy and simulated annealing with restrained molecular dynamics (MD). Compounds 1, 2, and 3 formed stacking and hydrogen bonding interactions with the 5'CUG/3'GUC motif. Compound 3 also formed van der Waals interactions with the internal loop. The global structure of each RNA-small molecule complexes retains an A-form conformation, while the internal loops are still dynamic but to a lesser extent compared to the unbound form. These results aid our understanding of ligand-RNA interactions and enable structure-based design of small molecules with improved binding affinity for and biological activity against r(CUG)exp. As the first ever reported structures of RNA r(CUG) repeats bound to ligands, these structures can enable virtual screening campaigns combined with machine learning assisted de novo design.

Substrate Analogues Entering the Lipoic Acid Salvage Pathway via Lipoate-Protein Ligase 2 Interfere with Staphylococcus aureus Virulence.

Lipoic acid (LA) is an essential cofactor in prokaryotic and eukaryotic organisms, required for the function of several multienzyme complexes such as oxoacid dehydrogenases. Prokaryotes either synthesize LA or salvage it from the environment. The salvage pathway in Staphylococcus aureus includes two lipoate-protein ligases, LplA1 and LplA2, as well as the amidotransferase LipL. In this study, we intended to hijack the salvage pathway by LA analogues that are transferred via LplA2 and LipL to the E2 subunits of various dehydrogenases, thereby resulting in nonfunctional enzymes that eventually impair viability of the bacterium. Initially, a virtual screening campaign was carried out to identify potential LA analogues that bind to LplA2. Three selected compounds affected S. aureus USA300 growth in minimal medium at concentrations ranging from 2.5 to 10 μg/mL. Further analysis of the most potent compound (Lpl-004) revealed its transfer to E2 subunits of dehydrogenase complexes and a negative impact on its functionality. Growth impairment caused by Lpl-004 treatment was restored by adding products of the lipoate-dependent enzyme complexes. In addition, Caenorhabditis elegans infected with LpL-004-treated USA300 demonstrated a significantly expanded lifespan compared to worms infected with untreated bacteria. Our results provide evidence that LA analogues exploiting the LA salvage pathway represent an innovative strategy for the development of novel antimicrobial substances.

ChemoDOTS: a web server to design chemistry-driven focused libraries.

In drug discovery, the successful optimization of an initial hit compound into a lead molecule requires multiple cycles of chemical modification. Consequently, there is a need to efficiently generate synthesizable chemical libraries to navigate the chemical space surrounding the primary hit. To address this need, we introduce ChemoDOTS, an easy-to-use web server for hit-to-lead chemical optimization freely available at https://chemodots.marseille.inserm.fr/. With this tool, users enter an activated form of the initial hit molecule then choose from automatically detected reactive functions. The server proposes compatible chemical transformations via an ensemble of encoded chemical reactions widely used in the pharmaceutical industry during hit-to-lead optimization. After selection of the desired reactions, all compatible chemical building blocks are automatically coupled to the initial hit to generate a raw chemical library. Post-processing filters can be applied to extract a subset of compounds with specific physicochemical properties. Finally, explicit stereoisomers and tautomers are computed, and a 3D conformer is generated for each molecule. The resulting virtual library is compatible with most docking software for virtual screening campaigns. ChemoDOTS rapidly generates synthetically feasible, hit-focused, large, diverse chemical libraries with finely-tuned physicochemical properties via a user-friendly interface providing a powerful resource for researchers engaged in hit-to-lead optimization.

Identification of small molecules affecting the interaction between human hemoglobin and Staphylococcus aureus IsdB hemophore

Human hemoglobin (Hb) is the preferred iron source of Staphylococcus aureus. This pathogenic bacterium exploits a sophisticated protein machinery called Iron-regulated surface determinant (Isd) system to bind Hb, extract and internalize heme, and finally degrade it to complete iron acquisition. IsdB, the surface exposed Hb receptor, is a proven virulence factor of S. aureus and the inhibition of its interaction with Hb can be pursued as a strategy to develop new classes of antimicrobials. To identify small molecules able to disrupt IsdB:Hb protein–protein interactions (PPIs), we carried out a structure-based virtual screening campaign and developed an ad hoc immunoassay to screen the retrieved set of commercially available compounds. Saturation-transfer difference (STD) NMR was applied to verify specific interactions of a sub-set of molecules, chosen based on their efficacy in reducing the amount of Hb bound to IsdB. Among molecules for which direct binding was verified, the best hit was submitted to ITC analysis to measure the binding affinity to Hb, which was found to be in the low micromolar range. The results demonstrate the viability of the proposed in silico/in vitro experimental pipeline to discover and test IsdB:Hb PPI inhibitors. The identified lead compound will be the starting point for future SAR and molecule optimization campaigns.

Identification of selective dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) inhibitors and their effects on tau and microtubule

The overexpression of dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A), commonly observed in neurodegenerative diseases like Alzheimer's disease (AD) and Down syndrome (DS), can induce the formation of neurofibrillary tangles (NFTs) and amyloid plaques. Hence, designing a selective DYRK1A inhibitor would result in a promising small molecule for treating neurodegenerative diseases. Developing selective inhibitors for DYRK1A has been a difficult challenge due to the highly preserved ATP-binding site of protein kinases. In this study, we employed a structure-based virtual screening (SBVS) campaign targeting DYRK1A from a database containing 1.6 million compounds. Enzymatic assays were utilized to verify inhibitory properties, confirming that Y020-3945 and Y020-3957 showed inhibitory activity towards DYRK1A. In particular, the compounds exhibited high selectivity for DYRK1A over a panel of 120 kinases, reduced the phosphorylation of tau, and reversed the tubulin polymerization for microtubule stability. Additionally, treatment with the compounds significantly reduced the secretion of inflammatory cytokines IL-6 and TNF-α activated by DYRK1A-assisted NFTs and Aβ oligomers. These identified inhibitors possess promising therapeutic potential for conditions associated with DYRK1A in neurodegenerative diseases. The results showed that Y020-3945 and Y020-3957 demonstrated structural novelty compared to known DYRK1A inhibitors, making them a valuable addition to developing potential treatments for neurodegenerative diseases.

In-silico guided chemical exploration of KDM4A fragments hits

BackgroundLysine demethylase enzymes (KDMs) are an emerging class of therapeutic targets, that catalyse the removal of methyl marks from histone lysine residues regulating chromatin structure and gene expression. KDM4A isoform plays an important role in the epigenetic dysregulation in various cancers and is linked to aggressive disease and poor clinical outcomes. Despite several efforts, the KDM4 family lacks successful specific molecular inhibitors.ResultsHerein, starting from a structure-based fragments virtual screening campaign we developed a synergic framework as a guide to rationally design efficient KDM4A inhibitors. Commercial libraries were used to create a fragments collection and perform a virtual screening campaign combining docking and pharmacophore approaches. The most promising compounds were tested in-vitro by a Homogeneous Time-Resolved Fluorescence-based assay developed for identifying selective substrate-competitive inhibitors by means of inhibition of H3K9me3 peptide demethylation. 2-(methylcarbamoyl)isonicotinic acid was identified as a preliminary active fragment, displaying inhibition of KDM4A enzymatic activity. Its chemical exploration was deeply investigated by computational and experimental approaches which allowed a rational fragment growing process. The in-silico studies guided the development of derivatives designed as expansion of the primary fragment hit and provided further knowledge on the structure–activity relationship.ConclusionsOur study describes useful insights into key ligand-KDM4A protein interaction and provides structural features for the development of successful selective KDM4A inhibitors.

Structural basis for specific inhibition of salicylate synthase from Mycobacterium abscessus

Blocking iron uptake and metabolism has been emerging as a promising therapeutic strategy for the development of novel antimicrobial compounds. Like all mycobacteria, M. abscessus (Mab) has evolved several countermeasures to scavenge iron from host carrier proteins, including the production of siderophores, which play a crucial role in these processes. In this study, we solved, for the first time, the crystal structure of Mab-SaS, the first enzyme involved in the biosynthesis of siderophores. Moreover, we screened a small, focused library and identified a compound exhibiting a potent inhibitory effect against Mab-SaS (IC50 ≈ 2 μM). Its binding mode was investigated by means of Induced Fit Docking simulations, performed on the crystal structure presented herein. Furthermore, cytotoxicity data and pharmacokinetic predictions revealed the safety and drug-likeness of this class of compounds. Finally, the crystallographic data were used to optimize the model for future virtual screening campaigns. Taken together, the findings of our study pave the way for the identification of potent Mab-SaS inhibitors, based on both established and unexplored chemotypes.

GPU-optimized approaches to molecular docking-based virtual screening in drug discovery: A comparative analysis

Finding a novel drug is a very long and complex procedure. Using computer simulations, it is possible to accelerate the preliminary phases by performing a virtual screening that filters a large set of drug candidates to a manageable number. This paper presents the implementations and comparative analysis of two GPU-optimized implementations of a virtual screening algorithm targeting novel GPU architectures. This work focuses on the analysis of parallel computation patterns and their mapping onto the target architecture. The first method adopts a traditional approach that spreads the computation for a single molecule across the entire GPU. The second uses a novel batched approach that exploits the parallel architecture of the GPU to evaluate more molecules in parallel. Experimental results showed a different behavior depending on the size of the database to be screened, either reaching a performance plateau sooner or having a more extended initial transient period to achieve a higher throughput (up to 5x), which is more suitable for extreme-scale virtual screening campaigns.

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.

Design and structural validation of peptide–drug conjugate ligands of the kappa-opioid receptor

Despite the increasing number of GPCR structures and recent advances in peptide design, the development of efficient technologies allowing rational design of high-affinity peptide ligands for single GPCRs remains an unmet challenge. Here, we develop a computational approach for designing conjugates of lariat-shaped macrocyclized peptides and a small molecule opioid ligand. We demonstrate its feasibility by discovering chemical scaffolds for the kappa-opioid receptor (KOR) with desired pharmacological activities. The designed De Novo Cyclic Peptide (DNCP)-β-naloxamine (NalA) exhibit in vitro potent mixed KOR agonism/mu-opioid receptor (MOR) antagonism, nanomolar binding affinity, selectivity, and efficacy bias at KOR. Proof-of-concept in vivo efficacy studies demonstrate that DNCP-β-NalA(1) induces a potent KOR-mediated antinociception in male mice. The high-resolution cryo-EM structure (2.6 Å) of the DNCP-β-NalA–KOR–Gi1 complex and molecular dynamics simulations are harnessed to validate the computational design model. This reveals a network of residues in ECL2/3 and TM6/7 controlling the intrinsic efficacy of KOR. In general, our computational de novo platform overcomes extensive lead optimization encountered in ultra-large library docking and virtual small molecule screening campaigns and offers innovation for GPCR ligand discovery. This may drive the development of next-generation therapeutics for medical applications such as pain conditions.