How does consensus scoring work for virtual library screening? An idealized computer experiment

Building shape-focused pharmacophore models for effective docking screening

The performance of molecular docking can be improved by comparing the shape similarity of the flexibly sampled poses against the target proteins' inverted binding cavities. The effectiveness of these pseudo-ligands or negative image-based models in docking rescoring is boosted further by performing enrichment-driven optimization. Here, we introduce a novel shape-focused pharmacophore modeling algorithm O-LAP that generates a new class of cavity-filling models by clumping together overlapping atomic content via pairwise distance graph clustering. Top-ranked poses of flexibly docked active ligands were used as the modeling input and multiple alternative clustering settings were benchmark-tested thoroughly with five demanding drug targets using random training/test divisions. In docking rescoring, the O-LAP modeling typically improved massively on the default docking enrichment; furthermore, the results indicate that the clustered models work well in rigid docking. The C+ +/Qt5-based algorithm O-LAP is released under the GNU General Public License v3.0 via GitHub ( https://github.com/jvlehtonen/overlap-toolkit ). SCIENTIFIC CONTRIBUTION: This study introduces O-LAP, a C++/Qt5-based graph clustering software for generating new type of shape-focused pharmacophore models. In the O-LAP modeling, the target protein cavity is filled with flexibly docked active ligands, the overlapping ligand atoms are clustered, and the shape/electrostatic potential of the resulting model is compared against the flexibly sampled molecular docking poses. The O-LAP modeling is shown to ensure high enrichment in both docking rescoring and rigid docking based on comprehensive benchmark-testing.

Identification of SARS-CoV-2 Mpro inhibitors through deep reinforcement learning for de novo drug design and computational chemistry approaches

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of coronavirus disease (COVID-19) since its emergence in December 2019. As of January 2024, there has been over 774 million reported cases and 7 million deaths worldwide. While vaccination efforts have been successful in reducing the severity of the disease and decreasing the transmission rate, the development of effective therapeutics against SARS-CoV-2 remains a critical need. The main protease (Mpro) of SARS-CoV-2 is an essential enzyme required for viral replication and has been identified as a promising target for drug development. In this study, we report the identification of novel Mpro inhibitors, using a combination of deep reinforcement learning for de novo drug design with 3D pharmacophore/shape-based alignment and privileged fragment match count scoring components followed by hit expansions and molecular docking approaches. Our experimentally validated results show that 3 novel series exhibit potent inhibitory activity against SARS-CoV-2 Mpro, with IC50 values ranging from 1.3 μM to 2.3 μM and a high degree of selectivity. These findings represent promising starting points for the development of new antiviral therapies against COVID-19.

Redocking the PDB

Molecular docking is a standard technique in structure-based drug design (SBDD). It aims to predict the 3D structure of a small molecule in the binding site of a receptor (often a protein). Despite being a common technique, it often necessitates multiple tools and involves manual steps. Here, we present the JAMDA preprocessing and docking workflow that is easy to use and allows fully automated docking. We evaluate the JAMDA docking workflow on binding sites extracted from the complete PDB and derive key factors determining JAMDA's docking performance. With that, we try to remove most of the bias due to manual intervention and provide a realistic estimate of the redocking performance of our JAMDA preprocessing and docking workflow for any PDB structure. On this large PDBScan22 data set, our JAMDA workflow finds a pose with an RMSD of at most 2 Å to the crystal ligand on the top rank for 30.1% of the structures. When applying objective structure quality filters to the PDBScan22 data set, the success rate increases to 61.8%. Given the prepared structures from the JAMDA preprocessing pipeline, both JAMDA and the widely used AutoDock Vina perform comparably on this filtered data set (the PDBScan22-HQ data set).

In Search of Novel SGLT2 Inhibitors by High-throughput Virtual Screening

BACKGROUND

Type 2 diabetes mellitus constitutes approximately 90% of all reported forms of diabetes mellitus. Insulin resistance characterizes this manifestation of diabetes. The prevalence of this condition is commonly observed in patients aged 45 and above; however, there is an emerging pattern of younger cohorts receiving diagnoses primarily attributed to lifestyle-related variables, including obesity, sedentary behavior, and poor dietary choices. The enzyme SGLT2 exerts a negative regulatory effect on insulin signaling pathways, resulting in the development of insulin resistance and subsequent elevation of blood glucose levels. The maintenance of glucose homeostasis relies on the proper functioning of insulin signaling pathways, while disruptions in insulin signaling can contribute to the development of type 2 diabetes.

OBJECTIVE

Our study aimed to identify novel SGLT2 inhibitors by high-throughput virtual Screening.

METHODS

We screened the May bridge Hit Discover database to identify potent hits followed by druglikeness, synthetic accessibility, PAINS alert, toxicity estimation, ADME assessment, and consensus molecular docking.

RESULTS

The screening process led to the identification of three molecules that demonstrated significant binding affinity, favorable drug-like properties, effective ADME, and minimal toxicity.

CONCLUSION

The identified molecules could manage T2DM effectively by inhibiting SGLT2, providing a promising avenue for future therapeutic strategies.

ChemFlow_py: a flexible toolkit for docking and rescoring

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 .

Silibinin: an inhibitor for a high-expressed BCL-2A1/BFL1 protein, linked with poor prognosis in breast cancer

Breast cancer (BC) accounts for 30% of all diagnosed cases of cancer in women and remains a leading cause of cancer-related deaths among women worldwide. The current study looks for a protein from the anti-apoptotic/pro-survival BCL-2 family whose overexpression reduces survivability in BC patients and a potential inhibitor for the protein. We found BCL-2A1/BFL1 protein with high expression linked to low survivability in BC. The protein shows prognosis in 8 out of 29 categories, whereas no other family member manifests this property. Out of 7379 compounds, three small molecules (CHEMBL9509, CHEMBL2104550 and CHEMBL3545011) form an H-bond with BCL-2A1/BFL1 protein's unique residue Cys55. Of the three small molecules, we found CHEMBL9509 (Silibinin) to be a potent inhibitor. The compound forms a stable H-bond with the residue Cys55 with the lowest binding energy compared to the other two compounds. It remains stable in the BH3 binding region for more than 100 ns, whereas the other two detach from the region. Additionally, the compound is found to be better than Venetoclax and Nematoclax. We firmly believe in the compound CHEMBL9509 potency to halt BC's progression by inhibiting the BCL-2A1/BFL1 protein, increasing patients' survivability.Communicated by Ramaswamy H. Sarma.

GENERA: A Combined Genetic/Deep-Learning Algorithm for Multiobjective Target-Oriented De Novo Design

This study introduces a new de novo design algorithm called GENERA that combines the capabilities of a deep-learning algorithm for automated drug-like analogue design, called DeLA-Drug, with a genetic algorithm for generating molecules with desired target-oriented properties. Specifically, GENERA was applied to the angiotensin-converting enzyme 2 (ACE2) target, which is implicated in many pathological conditions, including COVID-19. The ability of GENERA to de novo design promising candidates for a specific target was assessed using two docking programs, PLANTS and GLIDE. A fitness function based on the Pareto dominance resulting from computed PLANTS and GLIDE scores was applied to demonstrate the algorithm's ability to perform multiobjective optimizations effectively. GENERA can quickly generate focused libraries that produce better scores compared to a starting set of known ACE-2 binders. This study is the first to utilize a DL-based algorithm designed for analogue generation as a mutational operator within a GA framework, representing an innovative approach to target-oriented de novo design.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

MetaDOCK: A Combinatorial Molecular Docking Approach

Molecular docking plays a major role in academic and industrial drug screening and discovery processes. Despite the availability of numerous docking software packages, there is a lot of scope for improvement for the docking algorithms in terms of becoming more reliable to replicate the experimental binding results. Here, we propose a combinatorial or consensus docking approach where complementary powers of the existing methods are captured. We created a meta-docking protocol by combining the results of AutoDock4.2, LeDock, and rDOCK programs as these are freely available, easy to use, and suitable for large-scale analysis and produced better performance on benchmarking studies. Rigorous benchmarking analyses were undertaken to evaluate the scoring, posing, and screening capability of our approach. Further, the performance measures were compared against one standard state-of-the-art commercial docking software, GOLD, and one freely available software, PLANTS. Performances of MetaDOCK for scoring, posing, and screening the protein-ligand complexes were found to be quite superior compared to the reference programs. Exhaustive molecular dynamics simulation and molecular mechanics Poisson-Boltzmann and surface area-based free energy estimation also suggest better energetic stability of the docking solutions produced by our meta-approach. We believe that the MetaDOCK approach is a useful packaging of the freely available software and provides a better alternative to the scientific community who are unable to afford costly commercial packages.

Towards Effective Consensus Scoring in Structure-Based Virtual Screening

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.

Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening

The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.

Actives-Based Receptor Selection Strongly Increases the Success Rate in Structure-Based Drug Design and Leads to Identification of 22 Potent Cancer Inhibitors

Computer-aided drug design, an important component of the early stages of the drug discovery pipeline, routinely identifies large numbers of false positive hits that are subsequently confirmed to be experimentally inactive compounds. We have developed a methodology to improve true positive prediction rates in structure-based drug design and have successfully applied the protocol to twenty target systems and identified the top three performing conformers for each of the targets. Receptor performance was evaluated based on the area under the curve of the receiver operating characteristic curve for two independent sets of known actives. For a subset of five diverse cancer-related disease targets, we validated our approach through experimental testing of the top 50 compounds from a blind screening of a small molecule library containing hundreds of thousands of compounds. Our methods of receptor and compound selection resulted in the identification of 22 novel inhibitors in the low μM-nM range, with the most potent being an EGFR inhibitor with an IC₅₀ value of 7.96 nM. Additionally, for a subset of five independent target systems, we demonstrated the utility of Gaussian accelerated molecular dynamics to thoroughly explore a target system's potential energy surface and generate highly predictive receptor conformations.

A Hybrid Docking and Machine Learning Approach to Enhance the Performance of Virtual Screening Carried out on Protein-Protein Interfaces

The modulation of protein-protein interactions (PPIs) by small chemical compounds is challenging. PPIs play a critical role in most cellular processes and are involved in numerous disease pathways. As such, novel strategies that assist the design of PPI inhibitors are of major importance. We previously reported that the knowledge-based DLIGAND2 scoring tool was the best-rescoring function for improving receptor-based virtual screening (VS) performed with the Surflex docking engine applied to several PPI targets with experimentally known active and inactive compounds. Here, we extend our investigation by assessing the vs. potential of other types of scoring functions with an emphasis on docking-pose derived solvent accessible surface area (SASA) descriptors, with or without the use of machine learning (ML) classifiers. First, we explored rescoring strategies of Surflex-generated docking poses with five GOLD scoring functions (GoldScore, ChemScore, ASP, ChemPLP, ChemScore with Receptor Depth Scaling) and with consensus scoring. The top-ranked poses were post-processed to derive a set of protein and ligand SASA descriptors in the bound and unbound states, which were combined to derive descriptors of the docked protein-ligand complexes. Further, eight ML models (tree, bagged forest, random forest, Bayesian, support vector machine, logistic regression, neural network, and neural network with bagging) were trained using the derivatized SASA descriptors and validated on test sets. The results show that many SASA descriptors are better than Surflex and GOLD scoring functions in terms of overall performance and early recovery success on the used dataset. The ML models were superior to all scoring functions and rescoring approaches for most targets yielding up to a seven-fold increase in enrichment factors at 1% of the screened collections. In particular, the neural networks and random forest-based ML emerged as the best techniques for this PPI dataset, making them robust and attractive vs. tools for hit-finding efforts. The presented results suggest that exploring further docking-pose derived SASA descriptors could be valuable for structure-based virtual screening projects, and in the present case, to assist the rational design of small-molecule PPI inhibitors.

In Silico Investigations into the Selectivity of Psychoactive and New Psychoactive Substances in Monoamine Transporters

New psychoactive substances (NPS) are a group of compounds that mimic the effects of illicit substances. A range of NPS have been shown to interact with the three main classes of monoamine transporters (DAT, NET, and SERT) to differing extents, but it is unclear why these differences arise. To aid in understanding the differences in affinity between the classes of monoamine transporters, several in silico experiments were conducted. Docking experiments showed there was no direct correlation between a range of scoring functions and experimental activity, but Spearman ranking analysis showed a significant correlation (α = 0.1) for DAT, with the affinity ΔG (0.42), αHB (0.40), GoldScore (0.40), and PLP (0.41) scoring functions, and for DAT (0.38) and SERT (0.40) using a consensus scoring approach. Qualitative structure-activity relationship (QSAR) experiments resulted in the generation of robust and predictive three-descriptor models for SERT (r ² = 0.87, q ² = 0.8, and test set r ² = 0.74) and DAT (r ² = 0.68, q ² = 0.51, test set r ² = 0.63). Both QSAR models described similar characteristics for binding, i.e., rigid hydrophobic molecules with a biogenic amine moiety, and were not sufficient to facilitate a deeper understanding of differences in affinity between the monoamine transporters. This contextualizes the observed promiscuity for NPS between the isoforms and highlights the difficulty in the design and development of compounds that are isoform-selective.

DockingPie: a consensus docking plugin for PyMOL

MOTIVATION

The primary strategy for predicting the binding mode of small molecules to their receptors and for performing receptor-based virtual screening studies is protein-ligand docking, which is undoubtedly the most popular and successful approach in computer-aided drug discovery. The increased popularity of docking has resulted in the development of different docking algorithms and scoring functions. Nonetheless, it is unlikely that a single approach outperforms the others in terms of reproducibility and precision. In this ground, consensus docking techniques are taking hold.

RESULTS

We have developed DockingPie, an open source PyMOL plugin for individual, as well as consensus docking analyses. Smina, AutoDock Vina, ADFR and RxDock are the four docking engines that DockingPie currently supports in an easy and extremely intuitive way, thanks to its integrated docking environment and its GUI, fully integrated within PyMOL.

AVAILABILITY AND IMPLEMENTATION

https://github.com/paiardin/DockingPie.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Discovery of 2-Thiobenzimidazoles as Noncovalent Inhibitors of SARS-CoV-2 Main Protease

The discovery of antiviral agents against SARS-CoV-2 is an important step toward ending the COVID-19 pandemic and to tackle future outbreaks. In this context, the main protease (M^pro) represents an ideal target for developing coronavirus antivirals, being conserved among different strains and essential for survival. In this work, using in silico tools, we created and validated a docking protocol able to predict binders to the catalytic site of M^pro. The following structure-based virtual screening of a subset of the ZINC library (over 4.3 million unique structures), led to the identification of a hit compound having a 2-thiobenzimidazole scaffold. The inhibitory activity was confirmed using a FRET-based proteolytic assay against recombinant M^pro. Structure-activity relationships were obtained with the synthesis of a small library of analogs, guided by the analysis of the docking pose. Our efforts led to the identification of a micromolar M^pro inhibitor (IC₅₀ = 14.9 µM) with an original scaffold possessing ideal drug-like properties (predicted using the QikProp function) and representing a promising lead for the development of a novel class of coronavirus antivirals.

Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review

The rapid and accurate in silico prediction of protein-ligand binding free energies or binding affinities has the potential to transform drug discovery. In recent years, there has been a rapid growth of interest in deep learning methods for the prediction of protein-ligand binding affinities based on the structural information of protein-ligand complexes. These structure-based scoring functions often obtain better results than classical scoring functions when applied within their applicability domain. Here we review structure-based scoring functions for binding affinity prediction based on deep learning, focussing on different types of architectures, featurization strategies, data sets, methods for training and evaluation, and the role of explainable artificial intelligence in building useful models for real drug-discovery applications.

Consensus scoring evaluated using the GPCR-Bench dataset: Reconsidering the role of MM/GBSA

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 EF_1% and EF_5% 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 EF_1% and EF_5% levels. In comparison, only 14-22% and 6-11% of combinations of classical scoring functions produced improvements at EF_1% and EF_5% 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.

Virtual screening, optimization and molecular dynamics analyses highlighting a pyrrolo[1,2-a]quinazoline derivative as a potential inhibitor of DNA gyrase B of Mycobacterium tuberculosis

Tuberculosis is a disease that remains a significant threat to public health worldwide, and this is mainly due to the selection of strains increasingly resistant to Mycobacterium tuberculosis, its causative agent. One of the validated targets for the development of new antibiotics is DNA gyrase. This enzyme is a type II topoisomerase responsible for regulating DNA topology and, as it is essential in bacteria. Thus, to contribute to the search for new molecules with potential to act as competitive inhibitors at the active site of M. tuberculosis DNA gyrase B, the present work explored a dataset of 20,098 natural products that were filtered using the FAF-Drugs4 server to obtain a total of 5462 structures that were subsequently used in virtual screenings. The consensus score analysis between LeDock and Auto-Dock Vina software showed that ZINC000040309506 (pyrrolo[1,2-a]quinazoline derivative) exhibit the best binding energy with the enzyme. In addition, its subsequent optimization generated the derivative described as PQPNN, which show better binding energy in docking analysis, more stability in molecular dynamics simulations and improved pharmacokinetic and toxicological profiles, compared to the parent compound. Taken together, the pyrrolo[1,2-a]quinazoline derivative described for the first time in the present work shows promising potential to inhibit DNA gyrase B of M. tuberculosis.

Rutin increases alpha-tubulin acetylation via histone deacetylase 6 inhibition

Microtubules are dynamic cytoskeletal filaments composed of alpha- (α) and beta (β)-tubulin proteins. α-tubulin proteins are posttranslationally acetylated, and loss of acetylation is associated with axonal transport defects, a common alteration contributing to the pathomechanisms of several neurodegenerative diseases. Restoring α-tubulin acetylation by pharmacological inhibition of HDAC6, a primary α-tubulin deacetylase, can rescue impaired transport. Therefore, HDAC6 is considered a promising therapeutic target for neurodegenerative diseases, but currently, there is no clinically approved inhibitor for this purpose. In this study, using drug repurposing strategy, we aimed to identify compounds possessing HDAC6 inhibition activity and inducing α-tubulin acetylation. We systematically analyzed the FDA-approved library by utilizing virtual screening and consensus scoring approaches. Inhibition activities of promising compounds were tested using in vitro assays. Motor neuron-like NSC34 cells were treated with the candidate compounds, and α-tubulin acetylation levels were determined by Western blot. Our results demonstrated that rutin, a natural flavonoid, inhibits cellular HDAC6 activity without inducing any toxicity, and it significantly increases α-tubulin acetylation level in motor neuron-like cells.

Consensus combining outcomes of multiple ensemble dockings: examples using dDAT crystalized complexes

Docking using different programs provides more reliable information about the interaction of molecules than data obtained in a single program. An exponential consensus ranking (ECR) was developed to combine scoring functions across docking programs differing in efficiencies and scales of measurements. The ECR method was adapted to merge results of re- and cross-dockings (i.e., ensemble docking) made in multiple docking programs. Adapted ECR consisted of four consecutive steps: 1- determination of scoring functions for a ligand with a series of macromolecules in multiple docking programs; 2- ranking of the scoring functions per macromolecule in each program; 3- combining the ranking across the programs creating a ranking per macromolecule; 4- averaging the ranking per macromolecule creating a final ranking. This last step incorporated the heterogeneity of the macromolecule conformations in the consensual score. The final ranking based on the adapted ECR represents relative affinity of a series of ligands to a macromolecule on average. As an example, a ranking of the average affinity of antidepressants and other ligands to the Drosophila melanogaster dopamine transporter (dDAT) was presented. Adapted ECR generated a ranking similar to that based on the affinity constant of each ligand obtained from the literature.

• A final ranking of the average relative affinity of different ligands to the dDAT.

• A consensus method combining multiple ensemble dockings.

• A complete protocol to make re-docking and cross-docking using Autodock Vina, Gold and DockThor.

Turbo Similarity Searching: Effect of Partial Ranking and Fusion Rules on ChEMBL Database

Turbo Similarity Searching (TSS) is the simplest and most recent chemical similarity searching (SS) approach, which improves the effectiveness of SS by performing a multi-target searching. TSS has four important elements, namely structural representation, similarity coefficient, number of nearest neighbours (NNs), and fusion rule, and any changes in these elements could affect the TSS results. A previous study suggested the advantage of using large numbers of reference compounds with small fractions of the database structures to obtain a better recall in group fusion. Therefore, this study aims to investigate the effect of partial ranking on TSS utilising different fusion rules and different numbers of NNs on the ChEMBL database and to evaluate whether these observations hold in TSS. Furthermore, the objective is to observe the effect of the indirect relationship feature of TSS on the partial ranking investigation. The results showed that the effect of using partial ranking on TSS was significant. This study also found that the performance of TSS improved as the database proportions used in the fusion process decreased and by using a small number of NNs. In addition, fusion rules based on reciprocal rank positions (RKP), maximum similarity score (sMAX), and sMNZ were superior to all the other fusion rules.

Discovery of inhibitors targeting protein tyrosine phosphatase 1B using a combined virtual screening approach

Protein tyrosine phosphatase 1B (PTP1B) acts as a therapeutic target for type 2 diabetes. However, the major challenges of PTP1B drug discovery are the poor selectivity and the weak oral bioavailability. In this study, we performed a combined virtual screening approach including multicomplex pharmacophore, molecular docking-based screening, van der Waals energy normalization, pose scaling factor, ADMET evaluation, and molecular dynamics simulation to select PTP1B inhibitors from three databases (PubChem, ChEMBL, and ZINC). We identified three potential PTP1B inhibitors, compounds 1, 4, and 5, with favorable binding energy and good oral bioavailability. The energetic and geometrical analyses show that the three compounds are stably bound to PTP1B, via occupying both the catalytic site (site A) and the proximal noncatalytic site (site B or C). Such occupancy may improve the selectivity. This work not only provided a feasible virtual screening protocol, but also suggested three potential PTP1B inhibitors for the treatment of type 2 diabetes.

Strengths and Weaknesses of Docking Simulations in the SARS-CoV-2 Era: the Main Protease (Mpro) Case Study

The scientific community is working against the clock to arrive at therapeutic interventions to treat patients with COVID-19. Among the strategies for drug discovery, virtual screening approaches have the capacity to search potential hits within millions of chemical structures in days, with the appropriate computing infrastructure. In this article, we first analyzed the published research targeting the inhibition of the main protease (Mpro), one of the most studied targets of SARS-CoV-2, by docking-based methods. An alarming finding was the lack of an adequate validation of the docking protocols (i.e., pose prediction and virtual screening accuracy) before applying them in virtual screening campaigns. The performance of the docking protocols was tested at some level in 57.7% of the 168 investigations analyzed. However, we found only three examples of a complete retrospective analysis of the scoring functions to quantify the virtual screening accuracy of the methods. Moreover, only two publications reported some experimental evaluation of the proposed hits until preparing this manuscript. All of these findings led us to carry out a retrospective performance validation of three different docking protocols, through the analysis of their pose prediction and screening accuracy. Surprisingly, we found that even though all tested docking protocols have a good pose prediction, their screening accuracy is quite limited as they fail to correctly rank a test set of compounds. These results highlight the importance of conducting an adequate validation of the docking protocols before carrying out virtual screening campaigns, and to experimentally confirm the predictions made by the models before drawing bold conclusions. Finally, successful structure-based drug discovery investigations published during the redaction of this manuscript allow us to propose the inclusion of target flexibility and consensus scoring as alternatives to improve the accuracy of the methods.

Systematic comparison of ligand-based and structure-based virtual screening methods on poly (ADP-ribose) polymerase-1 inhibitors

The poly (ADP-ribose) polymerase-1 (PARP1) has been regarded as a vital target in recent years and PARP1 inhibitors can be used for ovarian and breast cancer therapies. However, it has been realized that most of PARP1 inhibitors have disadvantages of low solubility and permeability. Therefore, by discovering more molecules with novel frameworks, it would have greater opportunities to apply it into broader clinical fields and have a more profound significance. In the present study, multiple virtual screening (VS) methods had been employed to evaluate the screening efficiency of ligand-based, structure-based and data fusion methods on PARP1 target. The VS methods include 2D similarity screening, structure-activity relationship (SAR) models, docking and complex-based pharmacophore screening. Moreover, the sum rank, sum score and reciprocal rank were also adopted for data fusion methods. The evaluation results show that the similarity searching based on Torsion fingerprint, six SAR models, Glide docking and pharmacophore screening using Phase have excellent screening performance. The best data fusion method is the reciprocal rank, but the sum score also performs well in framework enrichment. In general, the ligand-based VS methods show better performance on PARP1 inhibitor screening. These findings confirmed that adding ligand-based methods to the early screening stage will greatly improve the screening efficiency, and be able to enrich more highly active PARP1 inhibitors with diverse structures.

Identification of potential inhibitors of Schistosoma mansoni purine nucleoside phosphorylase from neolignan compounds using molecular modelling approaches

Schistosomiasis is a parasitic disease that is part of the neglected tropical diseases (NTDs), which cause significant levels of morbidity and mortality in millions of people throughout the world. The enzyme purine nucleoside phosphorylase from Schistosoma mansoni (SmPNP) represents a potential target for discovering new agents, and neolignans stand out as an important class of compounds. In this work, molecular modeling studies and biological assays of a set of neolignans were conducted against the PNP enzymes of the parasite and the human homologue (HssPNP). The results of the molecular docking described that the neolignans showed good complementarity by the active site of SmPNP. Molecular dynamics (MD) studies revealed that both complexes (Sm/HssPNP - neolignan compounds) were stable by analyzing the root mean square deviation (RMSD) values, and the binding free energy values suggest that the selected structures can interact and inhibit the catalytic activity of the SmPNP. Finally, the biological assay indicated that the selected neolignans presented a better molecular profile of inhibition compared to the human enzyme, as these ligands did not have the capacity to inhibit enzymatic activity, indicating that these compounds are promising candidates and that they can be used in future research in chemotherapy for schistosomiasis.Communicated by Ramaswamy H. Sarma.

Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion

Combinatorial fusion analysis (CFA) is an approach for combining multiple scoring systems using the rank-score characteristic function and cognitive diversity measure. One example is to combine diverse machine learning models to achieve better prediction quality. In this work, we apply CFA to the synthesis of metal halide perovskites containing organic ammonium cations via inverse temperature crystallization. Using a data set generated by high-throughput experimentation, four individual models (support vector machines, random forests, weighted logistic classifier, and gradient boosted trees) were developed. We characterize each of these scoring systems and explore 66 possible combinations of the models. When measured by the precision on predicting crystal formation, the majority of the combination models improves the individual model results. The best combination models outperform the best individual models by 3.9 percentage points in precision. In addition to improving prediction quality, we demonstrate how the fusion models can be used to identify mislabeled input data and address issues of data quality. In particular, we identify example cases where all single models and all fusion models do not give the correct prediction. Experimental replication of these syntheses reveals that these compositions are sensitive to modest temperature variations across the different locations of the heating element that can hinder or enhance the crystallization process. In summary, we demonstrate that model fusion using CFA can not only identify a previously unconsidered influence on reaction outcome but also be used as a form of quality control for high-throughput experimentation.

A unique ligand-steered strategy for CC chemokine receptor 2 homology modeling to facilitate structure-based virtual screening

CC chemokine receptor 2 (CCR2) antagonists that disrupt CCR2/MCP-1 interaction are expected to treat a variety of inflammatory and autoimmune diseases. The lack of CCR2 crystal structure limits the application of structure-based drug design (SBDD) to this target. Although a few three-dimensional theoretical models have been reported, their accuracy remains to be improved in terms of templates and modeling approaches. In this study, we developed a unique ligand-steered strategy for CCR2 homology modeling. It starts with an initial model based on the X-ray structure of the closest homolog so far, that is, CXCR4. Then, it uses Elastic Network Normal Mode Analysis (EN-NMA) and flexible docking (FD) by AutoDock Vina software to generate ligand-induced fit models. It selects optimal model(s) as well as scoring function(s) via extensive evaluation of model performance based on a unique benchmarking set constructed by our in-house tool, that is, MUBD-DecoyMaker. The model of 81_04 presents the optimal enrichment when combined with the scoring function of PMF04, and the proposed binding mode between CCR2 and Teijin lead by this model complies with the reported mutagenesis data. To highlight the advantage of our strategy, we compared it with the only reported ligand-steered strategy for CCR2 homology modeling, that is, Discovery Studio/Ligand Minimization. Lastly, we performed prospective virtual screening based on 81_04 and CCR2 antagonist bioassay. The identification of two hit compounds, that is, E859-1281 and MolPort-007-767-945, validated the efficacy of our model and the ligand-steered strategy.

Discovery of beta-lactamase CMY-10 inhibitors for combination therapy against multi-drug resistant Enterobacteriaceae

β-lactam antibiotics are the most widely used antimicrobial agents since the discovery of benzylpenicillin in the 1920s. Unfortunately, these life-saving antibiotics are vulnerable to inactivation by continuously evolving β-lactamase enzymes that are primary resistance determinants in multi-drug resistant pathogens. The current study exploits the strategy of combination therapeutics and aims at identifying novel β-lactamase inhibitors that can inactivate the β-lactamase enzyme of the pathogen while allowing the β-lactam antibiotic to act against its penicillin-binding protein target. Inhibitor discovery applied the Site-Identification by Ligand Competitive Saturation (SILCS) technology to map the functional group requirements of the β-lactamase CMY-10 and generate pharmacophore models of active site. SILCS-MC, Ligand-grid Free Energy (LGFE) analysis and Machine-learning based random-forest (RF) scoring methods were then used to screen and filter a library of 700,000 compounds. From the computational screens 74 compounds were subjected to experimental validation in which β-lactamase activity assay, in vitro susceptibility testing, and Scanning Electron Microscope (SEM) analysis were conducted to explore their antibacterial potential. Eleven compounds were identified as enhancers while 7 compounds were recognized as inhibitors of CMY-10. Of these, compound 11 showed promising activity in β-lactamase activity assay, in vitro susceptibility testing against ATCC strains (E. coli, E. cloacae, E. agglomerans, E. alvei) and MDR clinical isolates (E. cloacae, E. alvei and E. agglomerans), with synergistic assay indicating its potential as a β-lactam enhancer and β-lactamase inhibitor. Structural similarity search against the active compound 11 yielded 28 more compounds. The majority of these compounds also exhibited β-lactamase inhibition potential and antibacterial activity. The non-β-lactam-based β-lactamase inhibitors identified in the current study have the potential to be used in combination therapy with lactam-based antibiotics against MDR clinical isolates that have been found resistant against last-line antibiotics.

Identification of LASSBio-1945 as an inhibitor of SARS-CoV-2 main protease (MPRO) through in silico screening supported by molecular docking and a fragment-based pharmacophore model

In December 2019, an infectious disease was detected in Wuhan, China, caused by a new pathogenic coronavirus, named SARS-CoV-2. It spread very rapidly, and on March 11th of 2020, the outbreak was declared a pandemic by the World Health Organization. Currently, effective treatment options remain limited. SARS-CoV-2 enzyme main protease (MPRO) plays a pivotal role in the viral life cycle, making it a putative drug target. In order to identify suitable hits to develop inhibitors with adequate antiviral properties, we explored the LASSBio Chemical Library employing multiple strategies of virtual screening. A fragment-based pharmacophore model enabled the identification of key interactions involved in the molecular recognition at the catalytic site of MPRO, namely, with amino acid residues His41, His163 and Glu166. Docking-based virtual screening was performed, leading to the identification of LASSBio-1945 (9), a new hit of MPRO, presenting an IC50 = 15.97 μM. This compound, an 1,3-benzodioxolyl sulfonamide, represents an interesting starting point for subsequent hit-to-lead optimization steps and, to the best of our knowledge, a new distinct chemotype for MPRO inhibition.

Identification of novel αβ-tubulin modulators with antiproliferative activity directed to cancer therapy using ligand and structure-based virtual screening

Among several strategies related to cancer therapy targeting the modulation of αβ-tubulin has shown encouraging findings, more specifically when this is achieved by inhibitors located at the colchicine binding site. In this work, we aim to fish new αβ-tubulin modulators through a diverse and rational VS study, and thus, exhibiting the development of two VS pipelines. This allowed us to identify two compounds 5 and 9 that showed IC₅₀ values of 19.69 and 21.97 μM, respectively, towards possible modulation of αβ-tubulin, such as assessed by in vitro assays in C6 glioma and HEPG2 cell lines. We also evaluated possible mechanisms of action of obtained hits towards the colchicine binding site of αβ-tubulin by using docking approaches. In addition, assessment of the stability of the active (5 and 9) and inactive compounds (3 and 13) within the colchicine binding site was carried out by molecular dynamics (MD) simulations, highlighting the solvent effect and revealing the compound 5 as the most stable in the complex. At last, deep analysis of these results provided some valuable insights on the importance of using mixed ligand- and structure-based strategies in VS campaigns, in order to achieve higher chemical diversity and biological effect as well.

Flexi-pharma: a molecule-ranking strategy for virtual screening using pharmacophores from ligand-free conformational ensembles

Computer-aided strategies are useful for reducing the costs and increasing the success-rate in drug discovery. Among these strategies, methods based on pharmacophores (an ensemble of electronic and steric features representing the target active site) are efficient to implement over large compound libraries. However, traditional pharmacophore-based methods require knowledge of active compounds or ligand-receptor structures, and only few ones account for target flexibility. Here, we developed a pharmacophore-based virtual screening protocol, Flexi-pharma, that overcomes these limitations. The protocol uses molecular dynamics (MD) simulations to explore receptor flexibility, and performs a pharmacophore-based virtual screening over a set of MD conformations without requiring prior knowledge about known ligands or ligand-receptor structures for building the pharmacophores. The results from the different receptor conformations are combined using a "voting" approach, where a vote is given to each molecule that matches at least one pharmacophore from each MD conformation. Contrarily to other approaches that reduce the pharmacophore ensemble to some representative models and score according to the matching models or molecule conformers, the Flexi-pharma approach takes directly into account the receptor flexibility by scoring in regards to the receptor conformations. We tested the method over twenty systems, finding an enrichment of the dataset for 19 of them. Flexi-pharma is computationally efficient allowing for the screening of thousands of compounds in minutes on a single CPU core. Moreover, the ranking of molecules by vote is a general strategy that can be applied with any pharmacophore-filtering program.

Targeting shikimate pathway: In silico analysis of phosphoenolpyruvate derivatives as inhibitors of EPSP synthase and DAHP synthase

The shikimate pathway consists of seven enzymatic steps involved in the conversion of erythrose-4-phosphate and phosphoenolpyruvate to chorismate and also responsible to the production of aromatic amino acids, such as phenylalanine, tyrosine, and tryptophan which are essential to the bacterial metabolism. The 3-deoxy-D-arabino-heptulosonate-7-phosphate synthase (DAHPS) and 5-enolpyruvylshikimate 3-phosphate synthase (EPSPS) catalyze important steps in the shikimate pathway using as substrate the phosphoenolpyruvate (PEP). Due to the importance of PEP in shikimate pathway, its structure has been investigated to develop new bioinspired competitive inhibitors against DAHPS and EPSPS. In the present study, we perform a literature survey of 28 PEP derivatives, then we analyzed the selectivity and affinity of these compounds against the EPSPS and DAHPS structures using consensual molecular docking, pharmacophore prediction, molecular dynamics (MD) simulations, and binding free energy calculations. Here, we propose consistent binding modes of the selected ligands and indicate that their structures show interesting pharmacophoric properties related to multi-targets inhibitors for both enzymes. Our computational results are supported by previous experimental findings related to the interactions of PEP derivatives with DAHPS and EPSPS structures.

Fast Rescoring Protocols to Improve the Performance of Structure-Based Virtual Screening Performed on Protein-Protein Interfaces

Protein-protein interactions (PPIs) are attractive targets for drug design because of their essential role in numerous cellular processes and disease pathways. However, in general, PPIs display exposed binding pockets at the interface, and as such, have been largely unexploited for therapeutic interventions with low-molecular weight compounds. Here, we used docking and various rescoring strategies in an attempt to recover PPI inhibitors from a set of active and inactive molecules for 11 targets collected in ChEMBL and PubChem. Our focus is on the screening power of the various developed protocols and on using fast approaches so as to be able to apply such a strategy to the screening of ultralarge libraries in the future. First, we docked compounds into each target using the fast "pscreen" mode of the structure-based virtual screening (VS) package Surflex. Subsequently, the docking poses were postprocessed to derive a set of 3D topological descriptors: (i) shape similarity and (ii) interaction fingerprint similarity with a co-crystallized inhibitor, (iii) solvent-accessible surface area, and (iv) extent of deviation from the geometric center of a reference inhibitor. The derivatized descriptors, together with descriptor-scaled scoring functions, were utilized to investigate possible impacts on VS performance metrics. Moreover, four standalone scoring functions, RF-Score-VS (machine-learning), DLIGAND2 (knowledge-based), Vinardo (empirical), and X-SCORE (empirical), were employed to rescore the PPI compounds. Collectively, the results indicate that the topological scoring algorithms could be valuable both at a global level, with up to 79% increase in areas under the receiver operating characteristic curve for some targets, and in early stages, with up to a 4-fold increase in enrichment factors at 1% of the screened collections. Outstandingly, DLIGAND2 emerged as the best scoring function on this data set, outperforming all rescoring techniques in terms of VS metrics. The described methodology could help in the rational design of small-molecule PPI inhibitors and has direct applications in many therapeutic areas, including cancer, CNS, and infectious diseases such as COVID-19.

Structure-based drug repositioning over the human TMPRSS2 protease domain: search for chemical probes able to repress SARS-CoV-2 Spike protein cleavages

In December 2019, a new coronavirus was identified in the Hubei province of central china and named SARS-CoV-2. This new virus induces COVID-19, a severe respiratory disease with high death rate. A putative target to interfere with the virus is the host transmembrane serine protease family member II (TMPRSS2). This enzyme is critical for the entry of coronaviruses into human cells by cleaving and activating the spike protein (S) of SARS-CoV-2. Repositioning approved, investigational and experimental drugs on the serine protease domain of TMPRSS2 could thus be valuable. There is no experimental structure for TMPRSS2 but it is possible to develop quality structural models for the serine protease domain using comparative modeling strategies as such domains are highly structurally conserved. Beside the TMPRSS2 catalytic site, we predicted on our structural models a main exosite that could be important for the binding of protein partners and/or substrates. To block the catalytic site or the exosite of TMPRSS2 we used structure-based virtual screening computations and two different collections of approved, investigational and experimental drugs. We propose a list of 156 molecules that could bind to the catalytic site and 100 compounds that may interact with the exosite. These small molecules should now be tested in vitro to gain novel insights over the roles of TMPRSS2 or as starting point for the development of second generation analogs.

Identification of Human Leukotriene A4 Hydrolase Inhibitors Using Structure-Based Pharmacophore Modeling and Molecular Docking

Leukotriene B4 (LTB4) is a potent, proinflammatory lipid mediator implicated in the pathologies of an array of inflammatory diseases and cancer. The biosynthesis of LTB4 is regulated by the leukotriene A4 hydrolase (LTA₄H). Compounds capable of limiting the formation of LTB4, through selective inhibition of LTA₄H, are expected to provide potent anti-inflammatory and anti-cancer agents. The aim of the current study is to obtain potential LTA₄H inhibitors using computer-aided drug design. A hybrid 3D structure-based pharmacophore model was generated based on the crystal structure of LTA₄H in complex with bestatin. The generated pharmacophore was used in a virtual screen of the Maybridge database. The retrieved hits were extensively filtered, then docked into the active site of the enzyme. Finally, they were consensually scored to yield five hits as potential LTA₄H inhibitors. Consequently, the selected hits were purchased and their biological activity assessed in vitro against the epoxide hydrolase activity of LTA₄H. The results were very promising, with the most active compound showing 73.6% inhibition of the basal epoxide hydrolase activity of LTA₄H. The results from this exploratory study provide valuable information for the design and development of more potent and selective inhibitors.

Consensus Docking in Drug Discovery

Background:

Molecular docking is probably the most popular and profitable approach in computer-aided drug design, being the staple technique for predicting the binding mode of bioactive compounds and for performing receptor-based virtual screening studies. The growing attention received by docking, as well as the need for improving its reliability in pose prediction and virtual screening performance, has led to the development of a wide plethora of new docking algorithms and scoring functions. Nevertheless, it is unlikely to identify a single procedure outperforming the other ones in terms of reliability and accuracy or demonstrating to be generally suitable for all kinds of protein targets.

Methods:

In this context, consensus docking approaches are taking hold in computer-aided drug design. These computational protocols consist in docking ligands using multiple docking methods and then comparing the binding poses predicted for the same ligand by the different methods. This analysis is usually carried out calculating the root-mean-square deviation among the different docking results obtained for each ligand, in order to identify the number of docking methods producing the same binding pose.

Results:

The consensus docking approaches demonstrated to improve the quality of docking and virtual screening results compared to the single docking methods. From a qualitative point of view, the improvement in pose prediction accuracy was obtained by prioritizing ligand binding poses produced by a high number of docking methods, whereas with regards to virtual screening studies, high hit rates were obtained by prioritizing the compounds showing a high level of pose consensus.

Conclusion:

In this review, we provide an overview of the results obtained from the performance assessment of various consensus docking protocols and we illustrate successful case studies where consensus docking has been applied in virtual screening studies.

Improving structure-based virtual screening performance via learning from scoring function components

Scoring functions (SFs) based on complex machine learning (ML) algorithms have gradually emerged as a promising alternative to overcome the weaknesses of classical SFs. However, extensive efforts have been devoted to the development of SFs based on new protein-ligand interaction representations and advanced alternative ML algorithms instead of the energy components obtained by the decomposition of existing SFs. Here, we propose a new method named energy auxiliary terms learning (EATL), in which the scoring components are extracted and used as the input for the development of three levels of ML SFs including EATL SFs, docking-EATL SFs and comprehensive SFs with ascending VS performance. The EATL approach not only outperforms classical SFs for the absolute performance (ROC) and initial enrichment (BEDROC) but also yields comparable performance compared with other advanced ML-based methods on the diverse subset of Directory of Useful Decoys: Enhanced (DUD-E). The test on the relatively unbiased actives as decoys (AD) dataset also proved the effectiveness of EATL. Furthermore, the idea of learning from SF components to yield improved screening power can also be extended to other docking programs and SFs available.

Identifying new liver X receptor alpha modulators and distinguishing between agonists and antagonists by crystal ligand pocket screening

Background: Modulators of LXRα are of high pharmacological interest as LXRα regulates fatty acid metabolism, inflammatory processes and cancer. We aim to identify new LXRα modulators and to recognize a distinguishable feature of agonists. Results&methodology: The ligand self-dock and largest-cavity-size searching purposely located two appropriate ligand-binding sites to reach the two aims. One is identifying the new modulators from Maybridge library. 20 new compounds are confirmed by the in vitro reporter gene assay. The other is denoting an agonist by at least one best docking pose having one hydrogen bond to LXRα Helix12 His421. Conclusion: Based on the quality x-ray binding pocket, we can identify new LXRα modulators and distinguish between agonists and antagonists by molecular docking.

Rational drug repurposing for cancer by inclusion of the unbiased molecular dynamics simulation in the structure-based virtual screening approach: Challenges and breakthroughs

Managing cancer is now one of the biggest concerns of health organizations. Many strategies have been developed in drug discovery pipelines to help rectify this problem and two of the best ones are drug repurposing and computational methods. The combination of these approaches can have immense impact on the course of drug discovery. In silico drug repurposing can significantly reduce the time, the cost and the effort of drug development. Computational methods such as structure-based drug design (SBDD) and virtual screening can predict the potentials of small molecule binders, such as drugs, for having favorable effect on a particular molecular target. However, the demand for accuracy and efficiency of SBDD requires more sophisticated and complicated approaches such as unbiased molecular dynamics (UMD) simulation that has been recently introduced. As a complementary strategy, the knowledge acquired from UMD simulations can increase the chance of finding the right candidates and the pipeline of its administration is introduced and discussed in this review. An elaboration of this pipeline is also made by detailing an example, the binding and unbinding pathways of dasatinib-c-Src kinase complex, which shows that how influential this method can be in rational drug repurposing in cancer treatment.

Identification of new potential cyclooxygenase-2 inhibitors: insight from high throughput virtual screening of 18 million compounds combined with molecular dynamic simulation and quantum mechanics

The cyclooxygenase isoenzymes (COX-1 and COX-2) have a critical role in inflammation, fever, and pain. In contrary to COX-1, COX-2 is specifically expressed in inflamed tissues. Because of the subtle difference between both enzyme active sites, targeting COX-2 represents an efficient strategy for the development of novel inhibitors against inflammation with fewer side effects. In order to identify potential inhibitors of COX-2, more than 18,000,000 small molecules were retrieved from the ZINC database and virtually screened against it with a gradual increase in the precision through combined multistep docking. The results were sorted according to the rank-by-rank, induced-fit docking, and MM-GBSA evaluation. Subsequently from the final hit list, two top hits along with an approved selective inhibitor (celecoxib) were further investigated by the molecular dynamics (MD) simulations. The results were indicated that ZINC16934653 and ZINC40484701 demonstrate the highest affinity for the COX-2 binding pocket. Both ligands were bound to the important active-site residues, which are necessary for the correct orientation of inhibitors inside the binding cavity. Their binding free energies were comparable to celecoxib. 100 ns MD simulation is revealed that ZINC40484701 is more preferred in comparison with ZINC16934653 and celecoxib. In addition, non-covalent interactions between the compounds and key residues located in 6 Å distance from the COX-2 binding site show similar patterns of bonding by the reduced density gradient and the independent gradient model. Therefore, ZINC40484701 can be a potential candidate for further in vitro and in vivo analysis after lead-optimization efforts.Communicated by Ramaswamy H. Sarma.

Identification of natural products as selective PTP1B inhibitors via virtual screening

Protein tyrosine phosphatase 1B (PTP1B) is emerging as a promising yet challenging target for drug discovery. To identify natural products as new prototypes for PTP1B inhibitors, we employed a hierarchical protocol combining ligand-based and structure-based approaches for virtual screening against natural product libraries. Twenty-six compounds were prioritized for enzymatic evaluation against PTP1B, and ten of them were recognized as potent PTP1B inhibitors with IC₅₀ values at the micromolar level. Notably, nine compounds demonstrated evident selectivity to PTP1B over four other PTPs, including the most homologous T-cell protein tyrosine phosphatase (TCPTP). The results implicated that the structural uniqueness of the natural products might be a potential solution to the selectivity issue associated with the target PTP1B.

Consensus Analyses in Molecular Docking Studies Applied to Medicinal Chemistry

The increasing number of computational studies in medicinal chemistry involving molecular docking has put the technique forward as promising in Computer-Aided Drug Design. Considering the main method in the virtual screening based on the structure, consensus analysis of docking has been applied in several studies to overcome limitations of algorithms of different programs and mainly to increase the reliability of the results and reduce the number of false positives. However, some consensus scoring strategies are difficult to apply and, in some cases, are not reliable due to the small number of datasets tested. Thus, for such a methodology to be successful, it is necessary to understand why, when and how to use consensus docking. Therefore, the present study aims to present different approaches to docking consensus, applications, and several scoring strategies that have been successful and can be applied in future studies.

Combined strategies in structure-based virtual screening

The identification and optimization of lead compounds are inalienable components in drug design and discovery pipelines. As a powerful computational approach for the identification of hits with novel structural scaffolds, structure-based virtual screening (SBVS) has exhibited a remarkably increasing influence in the early stages of drug discovery. During the past decade, a variety of techniques and algorithms have been proposed and tested with different purposes in the scope of SBVS. Although SBVS has been a common and proven technology, it still shows some challenges and problems that are needed to be addressed, where the negative influence regardless of protein flexibility and the inaccurate prediction of binding affinity are the two major challenges. Here, focusing on these difficulties, we summarize a series of combined strategies or workflows developed by our group and others. Furthermore, several representative successful applications from recent publications are also discussed to demonstrate the effectiveness of the combined SBVS strategies in drug discovery campaigns.

Evaluation of consensus scoring methods for AutoDock Vina, smina and idock

We investigated the application of consensus scoring using the freely available and open source structure-based virtual screening docking programs AutoDock Vina, smina and idock. These individual programs and several simple consensus scoring methods were tested for their ability to identify hits against 20 DUD-E benchmark targets using the AUC and EF1 metrics. We found that all of the consensus scoring methods, however normalized, fared worse, on average, than simply using the output from a single program, smina. Additionally, the effect of a significant increase in the run time of all three programs was tested to find if a longer run time yielded improved results. Our results indicated that a longer run time than the default had little impact on the performance of these three programs or on consensus scoring methods based on their output. Thus, we have found that using the smina program alone at default settings is the best approach for researchers that do not have access to a suite of commercial docking software packages.