QSAR-based Virtual Screening Research Articles

First report on exploration of structural features of natural compounds (NPACT database) for anti-breast cancer activity (MCF-7): QSAR-based virtual screening, molecular docking, ADMET, MD simulation, and DFT studies.

The online version contains supplementary material available at 10.1007/s40203-024-00266-5.

Identification of tuberculosis inhibitors through QSAR-based virtual screening and molecular dynamics simulation of novel pyrimidine derivatives

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is the leading cause of death due to antimicrobial resistance, highlighting the urgency for innovative solutions and required the development of new drugs for TB treatment. In this study, we have conducted virtual screening and 2D quantitative structure activity relationship (2D-QSAR) models to analyze a set of fifty pyrimidine derivatives, aiming to uncover potential inhibitors for TB. The dataset is divided into a training set of thirty-eight molecules and a test set, using multiple linear regression (MLR). The key metrics such as R2 = 0.82, R2adj = 0.78, Ntest = 12, and R2test = 0.70, demonstrate the robustness of the built 2D-QSAR model. Leveraging the applicability domain of the model, using the Williams plot, databases of newer pyrimidine derivatives were created for drug-like property screening and activity prediction (pIC50) in TB treatment. Subsequently, molecular docking high-throughput virtual screening (HTVS), and dynamics simulations were employed to predict docking poses within Mtb kinases A and B (PDB: 6B2P). Detailed analysis revealed effective interactions between active amino acid sites in the Mtb pocket and the novel design drug molecules, sustaining the stability of their complexes.

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach.

Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.

Application of in-silico drug discovery techniques to discover a novel hit for target-specific inhibition of SARS-CoV-2 Mpro's revealed allosteric bindingwith MAO-B receptor: A theoretical study to find a cure for post-covid neurological disorder.

Several studies have revealed that SARS-CoV-2 damages brain function and produces significant neurological disability. The SARS-CoV-2 coronavirus, which causes COVID-19, may infect the heart, kidneys, and brain. Recent research suggests that monoamine oxidase B (MAO-B) may be involved in metabolomics variations in delirium-prone individuals and severe SARS-CoV-2 infection. In light of this situation, we have employed a variety of computational to develop suitable QSAR model using PyDescriptor and genetic algorithm-multilinear regression (GA-MLR) models (R2 = 0.800-793, Q2LOO = 0.734-0.727, and so on) on the data set of 106 molecules whose anti-SARS-CoV-2 activity was empirically determined. QSAR models generated follow OECD standards and are predictive. QSAR model descriptors were also observed in x-ray-resolved structures. After developing a QSAR model, we did a QSAR-based virtual screening on an in-house database of 200 compounds and found a potential hit molecule. The new hit's docking score (-8.208 kcal/mol) and PIC50 (7.85 M) demonstrated a significant affinity for SARS-CoV-2's main protease. Based on post-covid neurodegenerative episodes in Alzheimer's and Parkinson's-like disorders and MAO-B's role in neurodegeneration, the initially disclosed hit for the SARS-CoV-2 main protease was repurposed against the MAO-B receptor using receptor-based molecular docking, which yielded a docking score of -12.0 kcal/mol. This shows that the compound that inhibits SARS-CoV-2's primary protease may bind allosterically to the MAO-B receptor. We then did molecular dynamic simulations and MMGBSA tests to confirm molecular docking analyses and quantify binding free energy. The drug-receptor complex was stable during the 150-ns MD simulation. The first computational effort to show in-silico inhibition of SARS-CoV-2 Mpro and allosteric interaction of novel inhibitors with MAO-B in post-covid neurodegenerative symptoms and other disorders. The current study seeks a novel compound that inhibits SAR's COV-2 Mpro and perhaps binds MAO-B allosterically. Thus, this study will enable scientists design a new SARS-CoV-2 Mpro that inhibits the MAO-B receptor to treat post-covid neurological illness.

Interpretable QSAR Modelling and QSAR-Based Virtual Screening of Novel 3H-Thiazolo[4,5-b]pyridin-2-one Derivatives as Potential Antioxidant Drug Candidates

Quantitative structure-activity relationship (QSAR) study has been carried out for 32 N3 substituted 3H-thiazolo[4,5-b]pyridin-2-one derivatives as potential antioxidant drug candidates. The genetic algorithm (GA) and multiple linear regression analysis (MLRA) were used as appropriate techniques for descriptors selection and correlation models generation. The four best regressions for the prediction of the ability to scavenge the DPPH radical were generated as three-parameter QSAR models with the highest statistical characteristics and predictive ability. Based on the validation parameters of the generated models, it may be stated that they all satisfy the statistical requirements for their goodness-of-fitting with no current overfitting. The predictive ability of the constructed models was assessed with both internal and external validation approach and estimated with the leave-one-out and leave-group-out cross-validation coefficients (Q2LOO and Q2LGO). The values of Q2LOO (0.7060  0.7480) and Q2LGO (0.6647  0.7711) are reasonable, showing that the models are significant and robust to predict the free radical scavenging activity of the compounds from both training and validation sets. Applicability domain defining technique was employed to the obtained models and it was indicated that most structures were adequately represented by the chemical space of the models.

In-silico studies to recognize repurposing therapeutics toward arginase-I inhibitors as a potential onco-immunomodulators.

Rudolf Virchow was the first person to point out the important link between immune function and cancer. He did this by noticing that leukocytes were often found in tumors. Overexpression of arginase 1 (ARG1) and inducible nitric oxide synthase (iNOS) in myeloid-derived suppressor cells (MDSCs) and tumour-associated macrophages (TAMs) depletes both intracellular and extracellular arginine. TCR signalling is slowed as a result, and the same types of cells produce reactive oxygen and nitrogen species (ROS and RNS), which aggravates the situation. Human arginase I is a double-stranded manganese metalloenzyme that helps L-arginine break down into L-ornithine and urea. Thus, a quantitative structure-activity relationship (QSAR) analysis was performed to unearth the unrecognised structural aspects crucial for arginase-I inhibition. In this work, a balanced QSAR model with good prediction performance and clear mechanistic interpretation was developed using a dataset of 149 molecules encompassing a broad range of structural scaffolds and compositions. The model was made to meet OECD standards, and all of its validation parameters have values that are higher than the minimum requirements (R2 tr = 0.89, Q2 LMO = 0.86, and R2 ex = 0.85). The present QSAR study linked structural factors to arginase-I inhibitory action, including the proximity of lipophilic atoms to the molecule's centre of mass (within 3A), the position of the donor to the ring nitrogen (exactly 3 bonds away), and the surface area ratio. As OAT-1746 and two others are the only arginase-I inhibitors in development at the time, we have performed a QSAR-based virtual screening with 1650 FDA compounds taken from the zinc database. In this screening, 112 potential hit compounds were found to have a PIC50 value of less than 10nm against the arginase-I receptor. The created QSAR model's application domain was evaluated in relation to the most active hit molecules identified using QSAR-based virtual screening, utilising a training set of 149 compounds and a prediction set of 112 hit molecules. As shown in the Williams plot, the top hit molecule, ZINC000252286875, has a low leverage value of HAT i/i h* = 0.140, placing it towards the boundary of the usable range. Furthermore, one of 112 hit molecules with a docking score of -10.891kcal/mol (PIC50 = 10.023M) was isolated from a study of arginase-I using molecular docking. Protonated ZINC000252286875-linked arginase-1 showed 2.9 RMSD, whereas non-protonated had 1.8. RMSD plots illustrate protein stability in protonated and non-protonated ZINC000252286875-bound states. Protonated-ZINC000252286875-bound proteins contain 25 Rg. The non-protonated protein-ligand combination exhibits a 25.2-Rg, indicating compactness. Protonated and non-protonated ZINC000252286875 stabilised protein targets in binding cavities posthumously. Significant root mean square fluctuations (RMSF) were seen in the arginase-1 protein at a small number of residues for a time function of 500ns in both the protonated and unprotonated states. Protonated and non-protonated ligands interacted with proteins throughout the simulation. ZINC000252286875 bound Lys64, Asp124, Ala171, Arg222, Asp232, and Gly250. Aspartic acid residue 232 exhibited 200% ionic contact. 500-ns simulations-maintained ions. Salt bridges for ZINC000252286875 aided docking. ZINC000252286875 created six ionic bonds with Lys68, Asp117, His126, Ala171, Lys224, and Asp232 residues. Asp117, His126, and Lys224 showed 200% ionic interactions. In protonated and deprotonated states, GbindvdW, GbindLipo, and GbindCoulomb energies played crucial role. Moreover, ZINC000252286875 meets all of the ADMET standards to serve as a drug. As a result, the current analyses were successful in locating a novel and potent hit molecule that inhibits arginase-I effectively at nanomolar concentrations. The results of this investigation can be used to develop brand-new arginase I inhibitors as an alternative immune-modulating cancer therapy.

In silico study to recognize novel angiotensin-converting-enzyme-I inhibitors by 2D-QSAR and constraint-based molecular simulations

Cardiovascular diseases (CVD) such as heart failure, stroke, and hypertension affect 64.3 million people worldwide and are responsible for 30% of all deaths. Primary inhibition of the angiotensin-converting enzyme (ACE) is significant in the management of CVD. In the present study, the genetic algorithm-multiple linear regressions (GA-MLR) method is used to generate highly predictive and statistically significant (R 2 = 0.70–0.75, Q 2 LOO=0.67–0.73, Q 2 LMO=0.66–0.72, CCCex=0.70–0.78) quantitative structure-activity relationships (QSAR) models conferring to OECD requirements using a dataset of 255 structurally diverse and experimentally validated ACE inhibitors. The models contain simply illustratable Padel, Estate, and PyDescriptors that correlate structural scaffold requisite for ACE inhibition. Also, constraint-based molecular docking reveals an interaction profile between ligands and enzymes which is then correlated with the essential structural features associated with the QSAR models. The QSAR-based virtual screening was utilized to find novel lead molecules from a designed database of 102 thiadiazole derivatives. The Applicability domain (AD), Molecular Docking, Molecular dynamics, and ADMET analysis suggest two compound D24 and D40 are inflexibly linked to the protein binding site and follows drug-likeness properties. Communicated by Ramaswamy H. Sarma

QSAR-based virtual screening of traditional Chinese medicine for the identification of mitotic kinesin Eg5 inhibitors

Cell division is a crucial process for the growth and development of all living organisms. Unfortunately, uncontrolled cell division and growth is a hallmark of cancer, leading to the formation of tumors. The Human Eg5 protein, also known as the mitotic kinesin Eg5, plays a vital role in the regulation of cell division and its dysfunction has been linked to cancer development. This study aimed to identify new inhibitors of the Human Eg5 protein. Over 2000 Traditional Chinese Medicine (TCM) compounds were screened through a combination of virtual and structure-based screening methods. The top five compounds (Compounds 1–5) showed improved binding affinity to Human Eg5 compared to the standard drug Monastrol, as demonstrated by docking and MMGBSA scores, as well as interactions with key amino acids GLY 116 and GLY 118. The potential absorption and bioactivity of these compounds were also predicted through ADMET properties and a QSAR model, respectively, and showed improved results compared to the standard. Further quantum mechanics docking confirmed the better binding affinity of the lead compound, Compound 1. Our findings highlight Compound 1–5 as promising hits for inhibiting Human Eg5 and the need for experimental validation of their potential in treating cancer.

Discovery of Novel Inhibitors of Bacterial DNA Gyrase Using a QSAR-Based Approach.

Type II topoisomerases like DNA gyrase initiate ATP-dependent negative supercoils in bacterial DNA. It is critical in all of the bacteria but is missing from eukaryotes, making it a striking target for antibacterials. Ciprofloxacin is a clinically approved drug, but its clinical effectiveness is affected by the emergence of resistance in both Gram-positive and Gram-negative bacteria. Thus, it is vital to identify novel compounds that can efficiently inhibit DNA gyrase, and quantitative structure–activity relationship (QSAR) modeling is a quick and economical means to do so. A QSAR-based virtual screening approach was applied to identify new gyrase inhibitors using an in-house-generated combinatorial library of 29828 compounds from seven ciprofloxacin scaffold structures. QSAR was built using a data set of 271 compounds, which were identified as positive and negative inhibitors from existing data reported in in vitro studies. The best QSAR model was developed using the 5-fold cross-validation Neural Network in Orange, and it was based on five PaDEL descriptors with an accuracy and sensitivity of 83%. As a result of screening of an in-house-built combinatorial library with the best-developed QSAR model, 675 compounds were identified as potential inhibitors of DNA gyrase. These inhibitors were further docked with DNA gyrase using AutoDock to compare the binding mode and score of the selected/screened compounds, and 615 compounds exhibited a docking score comparable to or lower than that of ciprofloxacin. Out of these, the top five analogues 902b, 9699f, 4419f, 5538f, and 898b reported in our study have binding scores of −13.81, −12.95, −12.52, −12.43, and −12.41 kcal/mol, respectively. The MD simulations of these five analogues for 100 ns supported the interaction stability of analogues with Escherichia coli DNA gyrase. Ninety-one per cent of the analogues screened by the QSAR model displayed better binding energy than ciprofloxacin, demonstrating the efficacy of the generated model. The NN-QSAR model proposed in this manuscript can be downloaded from https://github.com/ritu225/NN-QSAR_model.git.

Repurposing food molecules as a potential BACE1 inhibitor for Alzheimer's disease.

Alzheimer’s disease (AD) is a severe neurodegenerative disorder of the brain that manifests as dementia, disorientation, difficulty in speech, and progressive cognitive and behavioral impairment. The emerging therapeutic approach to AD management is the inhibition of β-site APP cleaving enzyme-1 (BACE1), known to be one of the two aspartyl proteases that cleave β-amyloid precursor protein (APP). Studies confirmed the association of high BACE1 activity with the proficiency in the formation of β-amyloid-containing neurotic plaques, the characteristics of AD. Only a few FDA-approved BACE1 inhibitors are available in the market, but their adverse off-target effects limit their usage. In this paper, we have used both ligand-based and target-based approaches for drug design. The QSAR study entails creating a multivariate GA-MLR (Genetic Algorithm-Multilinear Regression) model using 552 molecules with acceptable statistical performance (R2 = 0.82, Q2loo = 0.81). According to the QSAR study, the activity has a strong link with various atoms such as aromatic carbons and ring Sulfur, acceptor atoms, sp2-hybridized oxygen, etc. Following that, a database of 26,467 food compounds was primarily used for QSAR-based virtual screening accompanied by the application of the Lipinski rule of five; the elimination of duplicates, salts, and metal derivatives resulted in a truncated dataset of 8,453 molecules. The molecular descriptor was calculated and a well-validated 6-parametric version of the QSAR model was used to predict the bioactivity of the 8,453 food compounds. Following this, the food compounds whose predicted activity (pKi) was observed above 7.0 M were further docked into the BACE1 receptor which gave rise to the Identification of 4-(3,4-Dihydroxyphenyl)-2-hydroxy-1H-phenalen-1-one (PubChem I.D: 4468; Food I.D: FDB017657) as a hit molecule (Binding Affinity = −8.9 kcal/mol, pKi = 7.97 nM, Ki = 10.715 M). Furthermore, molecular dynamics simulation for 150 ns and molecular mechanics generalized born and surface area (MMGBSA) study aided in identifying structural motifs involved in interactions with the BACE1 enzyme. Molecular docking and QSAR yielded complementary and congruent results. The validated analyses can be used to improve a drug/lead candidate’s inhibitory efficacy against the BACE1. Thus, our approach is expected to widen the field of study of repurposing nutraceuticals into neuroprotective as well as anti-cancer and anti-viral therapeutic interventions.

QSAR based virtual screening derived identification of a novel hit as a SARS CoV-229E 3CLpro Inhibitor: GA-MLR QSAR modeling supported by molecular Docking, molecular dynamics simulation and MMGBSA calculation approaches.

Congruous coronavirus drug targets and analogous lead molecules must be identified as quickly as possible to produce antiviral therapeutics against human coronavirus (HCoV SARS 3CLpro) infections. In the present communication, we bear recognized a HIT candidate for HCoV SARS 3CLpro inhibition. Four Parametric GA-MLR primarily based QSAR model (R2:0.84, R2adj:0.82, Q2loo: 0.78) was once promoted using a dataset over 37 structurally diverse molecules along QSAR based virtual screening (QSAR-VS), molecular docking (MD) then molecular dynamic simulation (MDS) analysis and MMGBSA calculations. The QSAR-based virtual screening was utilized to find novel lead molecules from an in-house database of 100 molecules. The QSAR-vS successfully offered a hit molecule with an improved PEC50 value from 5.88 to 6.08. The benzene ring, phenyl ring, amide oxygen and nitrogen, and other important pharmacophoric sites are revealed via MD and MDS studies. Ile164, Pro188, Leu190, Thr25, His41, Asn46, Thr47, Ser49, Asn189, Gln191, Thr47, and Asn141 are among the key amino acid residues in the S1 and S2 pocket. A stable complex of a lead molecule with the HCoV SARS 3CLpro was discovered using MDS. MM-GBSA calculations resulted from MD simulation results well supported with the binding energies calculated from the docking results. The results of this study can be exploited to develop a novel antiviral target, such as an HCoV SARS 3CLpro Inhibitor.

Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis.

Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure−Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA–MLR (Genetic Algorithm–Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole–indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.

Identifying Novel ATX Inhibitors via Combinatory Virtual Screening Using Crystallography-Derived Pharmacophore Modelling, Docking Study, and QSAR Analysis.

Autotaxin (ATX) is considered as an interesting drug target for the therapy of several diseases. The goal of the research was to detect new ATX inhibitors which have novel scaffolds by using virtual screening. First, based on two diverse receptor-ligand complexes, 14 pharmacophore models were developed, and the 14 models were verified through a big test database. Those pharmacophore models were utilized to accomplish virtual screening. Next, for the purpose of predicting the probable binding poses of compounds and then carrying out further virtual screening, docking-based virtual screening was performed. Moreover, an excellent 3D QSAR model was established, and 3D QSAR-based virtual screening was applied for predicting the activity values of compounds which got through the above two-round screenings. A correlation coefficient r2, which equals 0.988, was supplied by the 3D QSAR model for the training set, and the correlation coefficient r2 equaling 0.808 for the test set means that the developed 3D QSAR model is an excellent model. After the filtering was done by the combinatory virtual screening, which is based on the pharmacophore modelling, docking study, and 3D QSAR modelling, we chose nine potent inhibitors with novel scaffolds finally. Furthermore, two potent compounds have been particularly discussed.

Identification of a Novel Bcl-2 Inhibitor by Ligand-Based Screening and Investigation of Its Anti-cancer Effect on Human Breast Cancer Cells.

Bcl-2 family protein is an important factor in regulating apoptosis and is associated with cancer. The anti-apoptotic proteins of Bcl-2 family, such as Bcl-2, are overexpression in numerous tumors, and contribute to cancer formation, development, and therapy resistance. Therefore, Bcl-2 is a promising target for drug development, and several Bcl-2 inhibitors are currently undergoing clinical trials. In this study, we carried out a QSAR-based virtual screening approach to develop potential Bcl-2 inhibitors from the SPECS database. Surface plasmon resonance (SPR) binding assay was performed to examine the interaction between Bcl-2 protein and the screened inhibitors. After that, we measured the anti-tumor activities of the 8 candidate compounds, and found that compound M1 has significant cytotoxic effect on breast cancer cells. We further proved that compound M1 downregulated Bcl-2 expression and activated apoptosis by inducing mitochondrial dysfunction. In conclusion, we identified a novel Bcl-2 inhibitor by QSAR screening, which exerted significant cytotoxic activity in breast cancer cells through inducing mitochondria-mediated apoptosis.

Discovery of new potent hits against intracellular Trypanosoma cruzi by QSAR-based virtual screening

Chagas disease is a neglected tropical disease (NTD) caused by the protozoan parasite Trypanosoma cruzi and is primarily transmitted to humans by the feces of infected Triatominae insects during their blood meal. The disease affects 6–8 million people, mostly in Latin America countries, and kills more people in the region each year than any other parasite-born disease, including malaria. Moreover, patient numbers are currently increasing in non-endemic, developed countries, such as Australia, Japan, Canada, and the United States. The treatment is limited to one drug, benznidazole, which is only effective in the acute phase of the disease and is very toxic. Thus, there is an urgent need to develop new, safer, and effective drugs against the chronic phase of Chagas disease. Using a QSAR-based virtual screening followed by in vitro experimental evaluation, we report herein the identification of novel potent and selective hits against T. cruzi intracellular stage. We developed and validated binary QSAR models for prediction of anti-trypanosomal activity and cytotoxicity against mammalian cells using the best practices for QSAR modeling. These models were then used for virtual screening of a commercial database, leading to the identification of 39 virtual hits. Further in vitro assays showed that seven compounds were potent against intracellular T. cruzi at submicromolar concentrations (EC50 < 1 μM) and were very selective (SI > 30). Furthermore, other six compounds were also inside the hit criteria for Chagas disease, which presented activity at low micromolar concentrations (EC50 < 10 μM) against intracellular T. cruzi and were also selective (SI > 15). Moreover, we performed a multi-parameter analysis for the comparison of tested compounds regarding their balance between potency, selectivity, and predicted ADMET properties. In the next studies, the most promising compounds will be submitted to additional in vitro and in vivo assays in acute model of Chagas disease, and can be further optimized for the development of new promising drug candidates against this important yet neglected disease.

QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery.

Virtual screening (VS) has emerged in drug discovery as a powerful computational approach to screen large libraries of small molecules for new hits with desired properties that can then be tested experimentally. Similar to other computational approaches, VS intention is not to replace in vitro or in vivo assays, but to speed up the discovery process, to reduce the number of candidates to be tested experimentally, and to rationalize their choice. Moreover, VS has become very popular in pharmaceutical companies and academic organizations due to its time-, cost-, resources-, and labor-saving. Among the VS approaches, quantitative structure–activity relationship (QSAR) analysis is the most powerful method due to its high and fast throughput and good hit rate. As the first preliminary step of a QSAR model development, relevant chemogenomics data are collected from databases and the literature. Then, chemical descriptors are calculated on different levels of representation of molecular structure, ranging from 1D to nD, and then correlated with the biological property using machine learning techniques. Once developed and validated, QSAR models are applied to predict the biological property of novel compounds. Although the experimental testing of computational hits is not an inherent part of QSAR methodology, it is highly desired and should be performed as an ultimate validation of developed models. In this mini-review, we summarize and critically analyze the recent trends of QSAR-based VS in drug discovery and demonstrate successful applications in identifying perspective compounds with desired properties. Moreover, we provide some recommendations about the best practices for QSAR-based VS along with the future perspectives of this approach.

Proposing novel TNFα direct inhibitor Scaffolds using fragment-docking based e-pharmacophore modeling and binary QSAR-based virtual screening protocols pipeline

Tumor necrosis factor alpha (TNFα) is a homotrimer protein that plays a pivotal role for critical immune functions, including infection, inflammation and antitumor responses. It also plays a primary role in autoimmune diseases like rheumatoid arthritis (RA). So far, only biological therapeutics like infliximab, etanercept, and adalimumab are available as treatment of inflammatory diseases. They directly bind to TNFα and interrupt its binding to its receptor protein tumor necrosis factor receptor (TNFR). However, they may also cause serious side effects such as activating an autoimmune anti-antibody response or the weakening of the body's immune defenses. Thus, small molecule-based therapies can be considered as alternative methods. In this study, a novel method is applied to develop energetically optimized, structure-based pharmacophore models for rapid in silico drug screening. Fragment-based docking results were used in the construction of an universal e-pharmacophore model development. The developed model is then used for screening of small-molecule library Specs-screening compounds (Specs-SC) which includes more than 200.000 drug-like molecules. In another approach, binary QSAR-based models were used to screen Specs-SC, as well as Specs-natural products (NP) which has around 750 compounds, and a library of drugs registered or approved for use in humans NIH's NCGC pharmaceutical collection (NPC) which has around 7500 molecules. The MetaCore/MetaDrug platform was used for binary QSAR models for therapeutic activity prediction as well as pharmacokinetic and toxicity profile predictions of screening molecules. This platform is constructed based on a manually curated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism, and toxicity information. Molecular docking and molecular dynamics (MD) simulations were performed for the selected hit molecules. As target protein, both homodimer and homotrimer forms of TNFα were considered. The screening results showed that indinavir and medroxalol from NPC chemical library and a set of compounds (AT-057/43115940, AP-970/42897107, AK-968/41925665, AI-204/31679053, AN-648/41666950, AN-698/42006940) from Specs-SC database were identified as safe and active direct inhibitors of TNFα.

In silico approaches to identify novel myeloid cell leukemia-1 (Mcl-1) inhibitors for treatment of cancer

Myeloid cell leukemia-1 (Mcl-1) has been a validated and attractive target for cancer therapy. Over-expression of Mcl-1 in many cancers allows cancer cells to evade apoptosis and contributes to the resistance to current chemotherapeutics. Here, we identified new Mcl-1 inhibitors using a multi-step virtual screening approach. First, based on two different ligand-receptor complexes, 20 pharmacophore models were established by simultaneously using ‘Receptor-Ligand Pharmacophore Generation’ method and manual build feature method, and then carefully validated by a test database. Then, pharmacophore-based virtual screening (PB-VS) could be performed by using the 20 pharmacophore models. In addition, docking study was used to predict the possible binding poses of compounds, and the docking parameters were optimized before performing docking-based virtual screening (DB-VS). Moreover, a 3D QSAR model was established by applying the 55 aligned Mcl-1 inhibitors. The 55 inhibitors sharing the same scaffold were docked into the Mcl-1 active site before alignment, then the inhibitors with possible binding conformations were aligned. For the training set, the 3D QSAR model gave a correlation coefficient r2 of 0.996; for the test set, the correlation coefficient r2 was 0.812. Therefore, the developed 3D QSAR model was a good model, which could be applied for carrying out 3D QSAR-based virtual screening (QSARD-VS). After the above three virtual screening methods orderly filtering, 23 potential inhibitors with novel scaffolds were identified. Furthermore, we have discussed in detail the mapping results of two potent compounds onto pharmacophore models, 3D QSAR model, and the interactions between the compounds and active site residues.

Combined QSAR-based virtual screening and fluorescence binding assay to identify natural product mediators of Interferon Regulatory Factor 7 (IRF-7) in pulmonary infection

Interferon regulatory factor-7 (IRF-7) is involved in pulmonary infection and pneumonia. Here, a synthetic strategy that combined quantitative structure–activity relationship (QSAR)-based virtual screening and in vitro binding assay was described to identify new and potent mediator ligands of IRF-7 from natural products. In the procedure, a QSAR scoring function was developed and validated using Gaussian process (GP) regression and a structure-based set of protein–ligand affinity data. By integrating hotspot pocket prediction, pharmacokinetics profile analysis and molecular docking calculations, the scoring function was successfully applied to virtual screening against a large library of structurally diverse, drug-like natural products. With the method we were able to identify a number of potential hits, from which several compounds were found to have moderate or high affinity to IRF-7 using fluorescence binding assays, with dissociation constants Kd at micromolar level. We have also examined the structural basis and noncovalent interactions of computationally modelled IRF-7 complex with its potent ligands. It is revealed that hydrophobic forces and van der Waals contacts play a central role in stabilization of the complex architecture, while few hydrogen bonds confer additional specificity for the protein–ligand recognition.

Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening.

Schistosomiasis is a debilitating neglected tropical disease, caused by flatworms of Schistosoma genus. The treatment relies on a single drug, praziquantel (PZQ), making the discovery of new compounds extremely urgent. In this work, we integrated QSAR-based virtual screening (VS) of Schistosoma mansoni thioredoxin glutathione reductase (SmTGR) inhibitors and high content screening (HCS) aiming to discover new antischistosomal agents. Initially, binary QSAR models for inhibition of SmTGR were developed and validated using the Organization for Economic Co-operation and Development (OECD) guidance. Using these models, we prioritized 29 compounds for further testing in two HCS platforms based on image analysis of assay plates. Among them, 2-[2-(3-methyl-4-nitro-5-isoxazolyl)vinyl]pyridine and 2-(benzylsulfonyl)-1,3-benzothiazole, two compounds representing new chemical scaffolds have activity against schistosomula and adult worms at low micromolar concentrations and therefore represent promising antischistosomal hits for further hit-to-lead optimization.