Accumulated research strongly indicates that Janus kinase 3 (JAK3) is intricately involved in the initiation and advancement of a diverse range of human diseases, underscoring JAK3 as a promising target for therapeutic intervention. However, JAK3 shows significant homology with other JAK family isoforms, posing substantial challenges in the development of JAK3 inhibitors. To address these limitations, one strategy is to design selective covalent JAK3 inhibitors. Therefore, this study introduces a virtual screening approach that combines common feature pharmacophore modeling, covalent docking, and consensus scoring to identify novel inhibitors for JAK3. First, common feature pharmacophore models were constructed based on a selection of representative covalent JAK3 inhibitors. The optimal qualitative pharmacophore model proved highly effective in distinguishing active and inactive compounds. Second, 14 crystal structures of the JAK3-covalent inhibitor complex were chosen for the covalent docking studies. Following validation of the screening performance, 5TTU was identified as the most suitable candidate for screening potential JAK3 inhibitors due to its higher predictive accuracy. Finally, a virtual screening protocol based on consensus scoring was conducted, integrating pharmacophore mapping and covalent docking. This approach resulted in the discovery of multiple compounds with notable potential as effective JAK3 inhibitors. We hope that the developed virtual screening strategy will provide valuable guidance in the discovery of novel covalent JAK3 inhibitors.

In drug discovery, virtual screening is crucial for identifying potential hit compounds. This study aims to present a novel pipeline that employs machine learning models that amalgamates various conventional screening methods. A diverse array of protein targets was selected, and their corresponding datasets were subjected to active/decoy distribution analysis prior to scoring using four distinct methods: QSAR, Pharmacophore, docking, and 2D shape similarity, which were ultimately integrated into a single consensus score. The fine-tuned machine learning models were ranked using the novel formula \"w_new\", consensus scores were calculated, and an enrichment study was performed for each target. Distinctively, consensus scoring outperformed other methods in specific protein targets such as PPARG and DPP4, achieving AUC values of 0.90 and 0.84, respectively. Remarkably, this approach consistently prioritized compounds with higher experimental PIC50 values compared to all other screening methodologies. Moreover, the models demonstrated a range of moderate to high performance in terms of R2 values during external validation. In conclusion, this novel workflow consistently delivered superior results, emphasizing the significance of a holistic approach in drug discovery, where both quantitative metrics and active enrichment play pivotal roles in identifying the best virtual screening methodology.Scientific contributionWe presented a novel consensus scoring workflow in virtual screening, merging diverse methods for enhanced compound selection. We also introduced \'w_new\', a groundbreaking metric that intricately refines machine learning model rankings by weighing various model-specific parameters, revolutionizing their efficacy in drug discovery in addition to other domains.

Virtual screening aims to identify and rank compounds with drug/lead-like properties based on their affinity for the protein target. We developed a methodology that integrates structure- and ligand-based screening approaches to enhance hit rates against the TDP1 protein within a database of anthraquinone and chalcone derivatives, followed by evaluation of prioritized compounds through molecular simulations. This technique is particularly useful for training set imbalances. Four screening methods were used: QSAR, pharmacophore, shape similarity, and docking. Each method was individually trained to score compounds, and the scores were fused to create parallel Z-score fusion. The QSAR models exhibited satisfactory R2 values (0.84 to 0.75), whereas the pharmacophoric and shape similarity models demonstrated excellent performance (ROC:0.82-0.88). Docking enrichment analysis identified 6N0D as the optimal TDP1 crystal structure (ROC = 0.73). Remarkably, the consensus scoring method surpassed other screening methods, achieving the highest ROC value of 0.98. Docking screening prioritized compounds with binding modes resembling the co-crystallized ligands, whereas MMGBSA, consensus, and docking produced dynamic simulations that were as stable as the co-crystallized ligands. Additionally, the QSAR-selected compounds exhibited binding modes similar to those of commercially available TDP1 inhibitors. In this study, a strong correlation was found between the inhibitory concentrations and binding energy values of commercialized TDP1 inhibitors, indicating that the top-ranked compounds are expected to have potent inhibitory effects in the nano-/micromolar range. The results of this study establish that consensus scoring can be used as an adaptable mainstay virtual screening methodology, pending subsequent experimental validation for affirmation.Communicated by Ramaswamy H. Sarma.

The design of accurate virtual screening tools is an open challenge in drug discovery. Several structure-based methods have been developed at different levels of approximation. Among them, molecular docking is an established technique with high efficiency, but typically low accuracy. Moreover, docking performances are known to be target-dependent, which makes the choice of the docking program and corresponding scoring function critical when approaching a new protein target. To compare the performances of different docking protocols, we developed ChemFlow_py, an automated tool to perform docking and rescoring. Using four protein systems extracted from DUD-E with 100 known active compounds and 3000 decoys per target, we compared the performances of several rescoring strategies including consensus scoring. We found that the average docking results can be improved by consensus ranking, which emphasizes the relevance of consensus scoring when little or no chemical information is available for a given target. ChemFlow_py is a free toolkit to optimize the performances of virtual high-throughput screening (vHTS). The software is publicly available at https://github.com/IFMlab/ChemFlow_py .

Virtual screening (VS) is a computational strategy that uses in silico automated protein docking inter alia to rank potential ligands, or by extension rank protein-ligand pairs, identifying potential drug candidates. Most docking methods use preferred sets of physicochemical descriptors (PCDs) to model the interactions between host and guest molecules. Thus, conventional VS is often data-specific, method-dependent and with demonstrably differing utility in identifying candidate drugs. This study proposes four universality classes of novel consensus scoring (CS) algorithms that combine docking scores, derived from ten docking programs (ADFR, DOCK, Gemdock, Ledock, PLANTS, PSOVina, QuickVina2, Smina, Autodock Vina and VinaXB), using decoys from the DUD-E repository ( http://dude.docking.org/ ) against 29 MRSA-oriented targets to create a general VS formulation that can identify active ligands for any suitable protein target. Our results demonstrate that CS provides improved ligand-protein docking fidelity when compared to individual docking platforms. This approach requires only a small number of docking combinations and can serve as a viable and parsimonious alternative to more computationally expensive docking approaches. Predictions from our CS algorithm are compared against independent machine learning evaluations using the same docking data, complementing the CS outcomes. Our method is a reliable approach for identifying protein targets and high-affinity ligands that can be tested as high-probability candidates for drug repositioning.

The ADAPT (Assisted Design of Antibody and Protein Therapeutics) platform guides the selection of mutants that improve/modulate the affinity of antibodies and other biologics. Predicted affinities are based on a consensus z-score from three scoring functions. Computational predictions are interleaved with experimental validation, significantly enhancing the robustness of the design and selection of mutants. A key step is an initial exhaustive virtual single-mutant scan that identifies hot spots and the mutations predicted to improve affinity. A small number of proposed single mutants are then produced and assayed. Only the validated single mutants (i.e., having improved affinity) are used to design double and higher-order mutants in subsequent rounds of design, avoiding the combinatorial explosion that arises from random mutagenesis. Typically, with a total of about 30-50 designed single, double, and triple mutants, affinity improvements of 10- to 100-fold are obtained.

Targeting SARS-CoV-2 papain-like protease using inhibitors is a suitable approach for inhibition of virus replication and dysregulation of host anti-viral immunity. Engaging all five binding sites far from the catalytic site of PLpro is essential for developing a potent inhibitor. We developed and validated a structure-based pharmacophore model with 9 features of a potent PLpro inhibitor. The pharmacophore model-aided virtual screening of the comprehensive marine natural product database predicted 66 initial hits. This hit library was downsized by filtration through a molecular weight filter of ≤ 500 g/mol. The 50 resultant hits were screened by comparative molecular docking using AutoDock and AutoDock Vina. Comparative molecular docking enables benchmarking docking and relieves the disparities in the search and scoring functions of docking engines. Both docking engines retrieved 3 same compounds at different positions in the top 1 % rank, hence consensus scoring was applied, through which CMNPD28766, aspergillipeptide F emerged as the best PLpro inhibitor. Aspergillipeptide F topped the 50-hit library with a pharmacophore-fit score of 75.916. Favorable binding interactions were predicted between aspergillipeptide F and PLpro similar to the native ligand XR8-24. Aspergillipeptide F was able to engage all the 5 binding sites including the newly discovered BL2 groove, site V. Molecular dynamics for quantification of Cα-atom movements of PLpro after ligand binding indicated that it exhibits highly correlated domain movements contributing to the low free energy of binding and a stable conformation. Thus, aspergillipeptide F is a promising candidate for pharmaceutical and clinical development as a potent SARS-CoV-2 PLpro inhibitor.

The recent availability of large numbers of GPCR crystal structures has provided an unprecedented opportunity to evaluate their performance in virtual screening protocols using established benchmarking datasets. In this study, we evaluated the ability of MM/GBSA in consensus scoring-based virtual screening enrichment together with nine classical scoring functions, using the GPCR-Bench dataset consisting of 24 GPCR crystal structures and 254,646 actives and decoys. While the performance of consensus scoring was modest overall, combinations which included MM/GBSA performed relatively well compared to combinations of classical scoring functions. Combinations of MM/GBSA and good-performing scoring functions provided the highest proportion of improvements, with improvements observed in 32% and 19% of all combinations across all targets at the EF1% and EF5% levels respectively. Combinations of MM/GBSA and poor-performing scoring functions still outperformed classical scoring functions, with improvements observed in 26% and 17% of all combinations at the EF1% and EF5% levels. In comparison, only 14-22% and 6-11% of combinations of classical scoring functions produced improvements at EF1% and EF5% respectively. Efforts to improve performance by increasing the number of scoring functions in consensus scoring to three were mostly ineffective. We also observed that consensus scoring performed better for individual scoring functions possessing initially low enrichment factors, potentially implying their benefits are more relevant in such scenarios. Overall, this study demonstrated the first implementation of MM/GBSA in consensus scoring using the GPCR-Bench dataset and could provide a valuable benchmark of the performance of MM/GBSA in comparison to classical scoring functions in consensus scoring for GPCRs.

OBJECTIVE: Drug repurposing is a highly popular approach to find new indications for drugs, which greatly reduces time and costs for drug design and discovery. Non-selective inhibitors of histone deacetylase (HDAC) isoforms including sirtuins (SIRTs) are effective against conditions like cancer. In this study, we used molecular docking to screen Food and Drug Administration (FDA)-approved drugs to identify a number of drugs with a potential to be repurposed for pan-HDAC and pan-SIRT inhibitor activity.
METHODS: The library of FDA-approved drugs was optimized using MacroModel. The crystal structures of HDAC1-4, 6-8, SIRT1-3, 5, 6 were prepared before the library was docked to each structure using Glide, FRED, and AutoDock Vina/PyRx. Consensus scores were derived from the docking scores obtained from each software. Pharmacophore modeling was performed using Phase.
RESULTS: Based on the consensus scores, belinostat, bexarotene, and cianidanol emerged as top virtual pan-HDAC inhibitors whereas alosetron, cinacalcet, and indacaterol emerged as virtual pan-SIRT inhibitors. Pharmacophore hypotheses for these virtual inhibitors were also suggested through pharmacophore modeling in agreement with the molecular docking models.
CONCLUSIONS: The consensus approach enabled selection of the best performing drug molecules according to different software, and good scores against isoforms (virtual pan-HDAC and pan-SIRT inhibitors). The study not only proposes potential drugs to be repurposed for HDAC and SIRT-related diseases but also provides insights for designing potent de novo derivatives.

The pandemic of COVID-19 has an unprecedented impact on global health and economy. The novel SARS-CoV-2 is recognized as the etiological agent of current outbreak. Because of its contagious human-to-human transmission, it is an utmost global health emergency at present. To mitigate this threat many scientists and researchers are racing to develop antiviral therapy against the virus. Unfortunately, to date no vaccine or antiviral therapeutic is approved thus there is an urgent need to discover antiviral agent to help the individual who are at high risk. Virus main protease or chymotrypsin-like protease plays a pivotal role in virus replication and transcription; thus, it is considered as an attractive drug target to combat the COVID-19. In this study, multistep structure based virtual screening of CAS antiviral database is performed for the identification of potent and effective small molecule inhibitors against chymotrypsin-like protease of SARS-CoV-2. Consensus scoring strategy combine with flexible docking is used to extract potential hits. As a result of extensive virtual screening, 4 hits were shortlisted for MD simulation to study their stability and dynamic behavior. Insight binding modes demonstrated that the selected hits stabilized inside the binding pocket of the target protein and exhibit complementarity with the active site residues. Our study provides compounds for further in vitro and in vivo studies against SARS-CoV-2.