Ligand-based Virtual Screening Research Articles

Ligand-based Virtual Screening, Quantum Mechanics Calculations, and Normal Mode Analysis of Phytochemical Compounds Targeting Toll‐Interacting Protein (Tollip) Against Bacterial Diseases

The Labeo rohita (Rohu) Toll interacting protein (Tollip) is ubiquitously expressed in the kidneys, gills, spleen, liver, and blood. Tollip in L. rohita has higher eukaryotic structural features and is produced in response to bacterial infections. Several bacterial diseases, such as Aeromonas hydrophila and Vibrio spp, have been reported in the internal organs of L. rohita. The consequences of these bacterial infections can be 100% mortality of fish. There are currently no medicines or vaccines available to prevent or treat infections caused by the involvement of this protein. During bacterial infections, it was discovered that Tollip plays an essential function as a negative regulator of the MyD88-dependent TLR signalling pathway. Therefore, the study aimed to evaluate the inhibitory potentiality of the Allium sativum compound against Tollip. A. sativum has been reported to show potential antibacterial activity against numerous microbial pathogens. Still, activity against the Tollip-promoted pathogens has not yet been reported. In silico virtual screen and molecular docking methods were used in this study to calculate the binding affinity of 48 drug compounds of A. sativum against the receptor Tollip. The docking and normal mode analysis methods predict 2 (PubChem CID: 122130381 and CID 12303662) inhibitory compounds that bind strongly with the Tollip with a binding affinity of -9.2 and -8.8 kcal/mol, respectively. The ADMET properties of the compounds also verified the drug resemblance features of the two compounds of A. sativum. Furthermore, to evaluate the efficacy of these two potential inhibitors, more in-vitro testing is required.

Identification of Small Molecule Inhibitors of Human Cytomegalovirus pUL89 Endonuclease Using Integrated Computational Approaches.

Replication of Human Cytomegalovirus (HCMV) requires the presence of a metal-dependent endonuclease at the C-terminus of pUL89, in order to properly pack and cleave the viral genome. Therefore, pUL89 is an attractive target to design anti-CMV intervention. Herein, we used integrated structure-based and ligand-based virtual screening approaches in combination with MD simulation for the identification of potential metal binding small molecule antagonist of pUL89. In this regard, the essential chemical features needed for the inhibition of pUL89 endonuclease domain were defined and used as a 3D query to search chemical compounds from ZINC and ChEMBL database. Thereafter, the molecular docking and ligand-based shape screening were used to narrow down the compounds based on previously identified pUL89 antagonists. The selected virtual hits were further subjected to MD simulation to determine the intrinsic and ligand-induced flexibility of pUL89. The predicted binding modes showed that the compounds reside well in the binding site of endonuclease domain by chelating with the metal ions and crucial residues. Taken in concert, the in silico investigation led to the identification of potential pUL89 antagonists. This study provided promising starting point for further in vitro and in vivo studies.

Ligand-based 3D pharmacophore modeling, virtual screening, and molecular dynamic simulation of potential smoothened inhibitors.

The Hedgehog (Hh) signaling pathway is a crucial regulator of various cellular processes. Dysregulated activation of the Smoothened (SMO) oncoprotein, a key component of the Hh pathway, has been implicated in several types of cancer. Although SMO inhibitors are important anti-cancer therapeutics, drug-resistant SMO mutants have emerged, limiting their efficacy. This study aimed to discover stable SMO inhibitors for both wild-type and mutant SMOs, using a 12-feature pharmacophore model validated for virtual screening. One lead compound, LCT10312, was identified with high affinity to SMO and showed a significant conformational change in the SMO structure upon binding. Molecular dynamic simulation revealed stable interaction of LCT10312 with SMO and large atom motions, indicating SMO structural fluctuation. The lead compound showed high predicted binding scores to several clinically relevant SMO mutants. A ligand-based pharmacophore model was developed from 25 structurally clustered SMO inhibitors using LigandScout v3.12 software and virtually screened for hit identification from a library of 511,878 chemicals. Molecular docking was employed to identify potential leads based on SMO affinities. Molecular dynamic simulation (MDS) with GROMACS v5.1.4 was performed to analyze the structural changes of SMO oncoprotein upon binding lead compound(s) and cyclopamine as the control for 100 ns. The binding affinity of lead compound(s) was predicted on clinical and laboratory SMO mutants.

Exploring potential SARS-CoV-2 Mpro non-covalent inhibitors through docking, pharmacophore profile matching, molecular dynamic simulation, and MM-GBSA.

In the replication of SARS-CoV-2, the main protease (Mpro/3CLpro) is significant. It is conserved in a number of novel coronavirus variations, and no known human proteases share its cleavage sites. Therefore, 3CLpro is an ideal target. In the report, we screened five potential inhibitors (1543, 2308, 3717, 5606, and 9000) of SARS-CoV-2 Mpro through a workflow. The calculation of MM-GBSA binding free energy showed that three of the five potential inhibitors (1543, 2308, 5606) had similar inhibitor effects to X77 against Mpro of SARS-CoV-2. In conclusion, the manuscript lays the groundwork for the design of Mpro inhibitors. In the virtual screening phase, we used structure-based virtual screening (Qvina2.1) and ligand-based virtual screening (AncPhore). In the molecular dynamic simulation part, we used the Amber14SB + GAFF force field to perform molecular dynamic simulation of the complex for 100ns (Gromacs2021.5) and performed MM-GBSA binding free energy calculation according to the simulation trajectory.

3D-Sensitive Encoding of Pharmacophore Features.

In the presence of structural data, one sometimes need to compare 3D ligands. We design an overlay-free method to rank order 3D molecules in the pharmacophore feature space. The proposed encoding includes only two fittable parameters, is sparse, and is not too high dimensional. At the cost of an additional parameter, to delineate the binding site from a protein-ligand complex, the method can compare binding sites. The method was benchmarked on the LIT-PCBA data set for ligand-based virtual screening experiments and the sc-PDB and a Vertex data set when comparing binding sites. In similarity searches, the proposed method outperforms an open-source software doing optimal superposition of ligand-based pharmacophores and RDKit's 3D pharmacophore fingerprints. When comparing binding sites, the method is competitive with state of the art approaches. On a single CPU core, up to 374,000 ligand/s or 132,000 binding site/s can be rank ordered. The "AutoCorrelation of Pharmacophore Features" open-source software is released at https://github.com/tsudalab/ACP4.

Artificial intelligence assisted identification of potential tau aggregation inhibitors: ligand- and structure-based virtual screening, in silico ADME, and molecular dynamics study.

Alzheimer's disease (AD) is a severe, growing, multifactorial disorder affecting millions of people worldwide characterized by cognitive decline and neurodegeneration. The accumulation of tau protein into paired helical filaments is one of the major pathological hallmarks of AD and has gained the interest of researchers as a potential drug target to treat AD. Lately, Artificial Intelligence (AI) has revolutionized the drug discovery process by speeding it up and reducing the overall cost. As a part of our continuous effort to identify potential tau aggregation inhibitors, and leveraging the power of AI, in this study, we used a fully automated AI-assisted ligand-based virtual screening tool, PyRMD to screen a library of 12 million compounds from the ZINC database to identify potential tau aggregation inhibitors. The preliminary hits from virtual screening were filtered for similar compounds and pan-assay interference compounds (the compounds containing reactive functional groups which can interfere with the assays) using RDKit. Further, the selected compounds were prioritized based on their molecular docking score with the binding pocket of tau where the binding pockets were identified using replica exchange molecular dynamics simulation. Thirty-three compounds showing good docking scores for all the tau clusters were selected and were further subjected to in silico pharmacokinetic prediction. Finally, top 10 compounds were selected for molecular dynamics simulation and MMPBSA binding free energy calculations resulting in the identification of UNK_175, UNK_1027, UNK_1172, UNK_1173, UNK_1237, UNK_1518, and UNK_2181 as potential tau aggregation inhibitors.

VSFlow: an open-source ligand-based virtual screening tool

Ligand-based virtual screening is a widespread method in modern drug design. It allows for a rapid screening of large compound databases in order to identify similar structures. Here we report an open-source command line tool which includes a substructure-, fingerprint- and shape-based virtual screening. Most of the implemented features fully rely on the RDKit cheminformatics framework. VSFlow accepts a wide range of input file formats and is highly customizable. Additionally, a quick visualization of the screening results as pdf and/or pymol file is supported.Graphical

Ligand-based pharmacophore modelling, virtual screening and docking studies to identify potential compounds against FtsZ of Mycobacterium tuberculosis

Background and introductionTuberculosis (TB) is caused by Mycobacterium tuberculosis (M.tb) which is the most common cause of death from bacterial illness. Millions of victims of TB infections have been recorded including 20,800 deaths amongst HIV positive individuals. Hence, there is a rising need for new and active compounds against M. tb protein targets especially as there is a persistent resistance to the current drug treatment regime. AimThis study identifies new potential compounds against the M. tb target protein ftsZ via pharmacophore modelling, QSAR analysis and docking studies. MethodInhibitors with known PIC50 were used as a training set and the pharmacophore features (1 aromatic center, 2 hydrophobic, 2 hydrogen bond acceptors and 1 hydrogen bond donor) were validated against four test set compounds. The identified hits were subjected to rigorous ADMET properties and docked using PyRx. DS visualizer was used in binding interactions study. Stability was measured based on the total number of interactions and preference given to the number of hydrogen bond interactions. ResultsBased on the number of interactions, hydrogen bonds, extensive virtual screening and ADMET filtration, 40 compounds have been identified as potential inhibitors of ftsZ with only 3 considered to be the best leads. Significance of researchThe identified compounds have potential of being drug candidate against Mycobacterium tuberculosis and may possess a novel mechanistic route in inhibiting the resistant strains.

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors.

Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.

Structural and molecular insights into tacrine-benzofuran hybrid induced inhibition of amyloid-β peptide aggregation and BACE1 activity

Amyloid-β (Aβ) aggregation and β-amyloid precursor protein cleaving enzyme 1 (BACE1) are the potential therapeutic drug targets for Alzheimer’s disease (AD). A recent study highlighted that tacrine-benzofuran hybrid C1 displayed anti-aggregation activity against Aβ42 peptide and inhibit BACE1 activity. However, the inhibition mechanism of C1 against Aβ42 aggregation and BACE1 activity remains unclear. Thus, molecular dynamics (MD) simulations of Aβ42 monomer and BACE1 with and without C1 were performed to inspect the inhibitory mechanism of C1 against Aβ42 aggregation and BACE1 activity. In addition, a ligand-based virtual screening followed by MD simulations was employed to explore potent new small-molecule dual inhibitors of Aβ42 aggregation and BACE1 activity. MD simulations highlighted that C1 promotes the non aggregating helical conformation in Aβ42 and destabilizes D23–K28 salt bridge that plays a vital role in the self-aggregation of Aβ42. C1 displays a favourable binding free energy (–50.7 ± 7.3 kcal/mol) with Aβ42 monomer and preferentially binds to the central hydrophobic core (CHC) residues. MD simulations highlighted that C1 strongly interacted with the BACE1 active site (Asp32 and Asp228) and active pockets. The scrutiny of interatomic distances among key residues of BACE1 highlighted the close flap (non-active) position in BACE1 on the incorporation of C1. The MD simulations explain the observed high inhibitory activity of C1 against Aβ aggregation and BACE1 in the in vitro studies. The ligand-based virtual screening followed by MD simulations identified CHEMBL2019027 (C2) as a promising dual inhibitor of Aβ42 aggregation and BACE1 activity. Communicated by Ramaswamy H. Sarma

Allosteric targeting resolves limitations of earlier LFA-1 directed modalities

Integrins are a family of cell surface receptors well-recognized for their therapeutic potential in a wide range of diseases. However, the development of integrin targeting medications has been impacted by unexpected downstream effects, reflecting originally unforeseen interference with the bidirectional signalling and cross-communication of integrins. We here selected one of the most severely affected target integrins, the integrin lymphocyte function-associated antigen-1 (LFA-1, αLβ2, CD11a/CD18), as a prototypic integrin to systematically assess and overcome these known shortcomings. We employed a two-tiered ligand-based virtual screening approach to identify a novel class of allosteric small molecule inhibitors targeting this integrin’s αI domain. The newly discovered chemical scaffold was derivatized, yielding potent bis-and tris-aryl-bicyclic-succinimides which inhibit LFA-1 in vitro at low nanomolar concentrations. The characterisation of these compounds in comparison to earlier LFA-1 targeting modalities established that the allosteric LFA-1 inhibitors (i) are devoid of partial agonism, (ii) selectively bind LFA-1 versus other integrins, (iii) do not trigger internalization of LFA-1 itself or other integrins and (iv) display oral availability. This profile differentiates the new generation of allosteric LFA-1 inhibitors from previous ligand mimetic-based LFA-1 inhibitors and anti-LFA-1 antibodies, and is projected to support novel immune regulatory regimens selectively targeting the integrin LFA-1. The rigorous computational and experimental assessment schedule described here is designed to be adaptable to the preclinical discovery and development of novel allosterically acting compounds targeting integrins other than LFA-1, providing an exemplary approach for the early characterisation of next generation integrin inhibitors.

Target-driven machine learning-enabled virtual screening (TAME-VS) platform for early-stage hit identification.

High-throughput screening (HTS) methods enable the empirical evaluation of a large scale of compounds and can be augmented by virtual screening (VS) techniques to save time and money by using potential active compounds for experimental testing. Structure-based and ligand-based virtual screening approaches have been extensively studied and applied in drug discovery practice with proven outcomes in advancing candidate molecules. However, the experimental data required for VS are expensive, and hit identification in an effective and efficient manner is particularly challenging during early-stage drug discovery for novel protein targets. Herein, we present our TArget-driven Machine learning-Enabled VS (TAME-VS) platform, which leverages existing chemical databases of bioactive molecules to modularly facilitate hit finding. Our methodology enables bespoke hit identification campaigns through a user-defined protein target. The input target ID is used to perform a homology-based target expansion, followed by compound retrieval from a large compilation of molecules with experimentally validated activity. Compounds are subsequently vectorized and adopted for machine learning (ML) model training. These machine learning models are deployed to perform model-based inferential virtual screening, and compounds are nominated based on predicted activity. Our platform was retrospectively validated across ten diverse protein targets and demonstrated clear predictive power. The implemented methodology provides a flexible and efficient approach that is accessible to a wide range of users. The TAME-VS platform is publicly available at https://github.com/bymgood/Target-driven-ML-enabled-VS to facilitate early-stage hit identification.

Virtual Screening of Benzimidazole Derivatives as Potential Triose Phosphate Isomerase Inhibitors with Biological Activity against Leishmania mexicana.

Leishmania mexicana (L. mexicana) is a causal agent of cutaneous leishmaniasis (CL), a "Neglected disease", for which the search for new drugs is a priority. Benzimidazole is a scaffold used to develop antiparasitic drugs; therefore, it is interesting molecule against L. mexicana. In this work, a ligand-based virtual screening (LBVS) of the ZINC15 database was performed. Subsequently, molecular docking was used to predict the compounds with potential binding at the dimer interface of triosephosphate isomerase (TIM) of L. mexicana (LmTIM). Compounds were selected on binding patterns, cost, and commercial availability for in vitro assays against L. mexicana blood promastigotes. The compounds were analyzed by molecular dynamics simulation on LmTIM and its homologous human TIM. Finally, the physicochemical and pharmacokinetic properties were determined in silico. A total of 175 molecules with docking scores between -10.8 and -9.0 Kcal/mol were obtained. Compound E2 showed the best leishmanicidal activity (IC50 = 4.04 µM) with a value similar to the reference drug pentamidine (IC50 = 2.23 µM). Molecular dynamics analysis predicted low affinity for human TIM. Furthermore, the pharmacokinetic and toxicological properties of the compounds were suitable for developing new leishmanicidal agents.

An improved 3D quantitative structure-activity relationships (QSAR) of molecules with CNN-based partial least squares model

Ligand-based virtual screening plays an important role for cases in which protein structures are not available. Among ligand-based methods, accurate and fast prediction of protein-ligand binding affinity is crucial for reducing computational cost and exploring the chemical search space efficiently. Here we proposed a CNN-based method, termed as L3D-PLS for building the quantitative structure-activity relationships without target structures. In L3D-PLS, a CNN module was designed for extracting the key interaction features from the grids around aligned ligands, and a partial least square (PLS) model fits the binding affinity with the extracted features of the pre-trained CNN module. In 30 publicly available pre-aligned molecular datasets, L3D-PLS outperformed the traditional CoMFA method. This results highlight that L3D-PLS can be useful for lead optimization based on small datasets which is often true in drug discovery compaign.

Computer-Aided drug design of new 2-amino-thiophene derivatives as anti-leishmanial agents

The leishmaniasis is a neglected disease caused by a group of protozoan parasites from the genus Leishmania whose treatment is limited, obsolete, toxic, and ineffective in certain cases. These characteristics motivate researchers worldwide to plan new therapeutic alternatives for the treatment of leishmaniasis, where the use of cheminformatics tools applied to computer-assisted drug design has allowed research to make great advances in the search for new drugs candidates. In this study, a series of 2-amino-thiophene (2-AT) derivatives was screened virtually using QSAR tools, ADMET filters and prediction models, allowing direct the synthesis of compounds, which were evaluated in vitro against promastigotes and axenic amastigotes of Leishmania amazonensis. The combination of different descriptors and machine learning methods led to obtaining robust and predictive QSAR models, which was obtained from a dataset composed of 1862 compounds extracted from the ChEMBL database, with correct classification rates ranging from 0.53 (for amastigotes) to 0.91 (for promastigotes), allowing to select eleven 2-AT derivatives, which do not violate Lipinski's rules, exhibit good druglikeness, and with probability ≤70% of potential activity against the two evolutionary forms of the parasite. All compounds were properly synthesized and 8 of them were shown to be active at least against one of the evolutionary forms of the parasite with IC50 values lower than 10 μM, being more active than the reference drug meglumine antimoniate, and showing low or no citotoxicity against macrophage J774.A1 for the most part. Compounds 8CN and DCN-83, respectively, are the most active against promastigote and amastigote forms, with IC50 values of 1.20 and 0.71 μM, and selectivity indexes (SI) of 36.58 and 119.33. Structure Activity Relationship (SAR) study was carried out and allowed to identify some favorable and/or essential substitution patterns for the leishmanial activity of 2-AT derivatives. Taken together, these findings demonstrate that the use of ligand-based virtual screening proved to be quite effective and saved time, effort, and money in the selection of potential anti-leishmanial agents, and confirm, once again that 2-AT derivatives are promising hit compounds for the development of new anti-leishmanial agents.

Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development.

The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).

2-[(2-Amino-6-methylpyrimidin-4-yl)sulfanyl]-N-arylacetamides: Discovery of a new class of anti-tubercular agents and prospects for their further structural modification

The synthesis of 2-[(2-amino-6-methylpyrimidin-4-yl)sulfanyl]-N-arylacetamides 6a-j was encouraged by their antibacterial activity and drug-likeness predictions. Of the compounds, two bearing 4‑isopropylphenyl 6c and 2,5‑dichlorophenyl 6i moieties were found to be threefold more potent than the first-line tuberculosis drug ethambutol. A molecular docking study revealed that compound 6c may selectively bind to cyclopropane mycolic acid synthase 1, an enzyme essential for the construction of the tuberculosis bacteria cell wall. Keeping this in mind, a recently developed ligand-based virtual screening strategy combining the molecular similarity search and docking approaches was adopted to identify more potent analogs of the parent compound. As a result, a series of new ligands 18p-w with phenyl-substituted azinyl amide groups were in silico discovered. Due to their high binding affinities to the enzyme and improved toxicity profiles, the ligands are undoubtedly worth future synthetic efforts.

Quantum machine learning framework for virtual screening in drug discovery: a prospective quantum advantage

Machine Learning for ligand based virtual screening (LB-VS) is an important in-silico tool for discovering new drugs in a faster and cost-effective manner, especially for emerging diseases such as COVID-19. In this paper, we propose a general-purpose framework combining a classical Support Vector Classifier algorithm with quantum kernel estimation for LB-VS on real-world databases, and we argue in favor of its prospective quantum advantage. Indeed, we heuristically prove that our quantum integrated workflow can, at least in some relevant instances, provide a tangible advantage compared to state-of-art classical algorithms operating on the same datasets, showing strong dependence on target and features selection method. Finally, we test our algorithm on IBM Quantum processors using ADRB2 and COVID-19 datasets, showing that hardware simulations provide results in line with the predicted performances and can surpass classical equivalents.

Ligand-based virtual screening, molecular dynamics, and biological evaluation of repurposed drugs as inhibitors of Trypanosoma cruzi proteasome

Chagas disease is a well-known Neglected Tropical Disease, mostly endemic in continental Latin America, but that has spread to North America and Europe. Unfortunately, current treatments against such disease are ineffective and produce known and undesirable side effects. To find novel effective drug candidates to treat Chagas disease, we uniquely explore the Trypanosoma cruzi proteasome as a recent biological target and, also, apply drug repurposing through different computational methodologies. For this, we initially applied protein homology modeling to build a robust model of proteasome β4/β5 subunits, since there is no crystallographic structure of this target. Then, we used it on a drug repurposing via a virtual screening campaign starting with more than 8,000 drugs and including the methodologies: ligand-based similarity, toxicity predictions, and molecular docking. Three drugs were selected concerning their favorable interactions at the protein binding site and subsequently submitted to molecular dynamics simulations, which allowed us to elucidate their behavior and compare such theoretical results with experimental ones, obtained in biological assays also described in this paper.Communicated by Ramaswamy H. Sarma

Ligand- and structure-based identification of GPER-binding small molecules

ABSTRACT G protein estrogen receptor (GPER) has been implicated in oestrogen-signalling routes in several biological systems and has been associated with different pathophysiological processes. So, there has been an increasing interest in identifying GPER-binding small molecules to modulate their biological activity. To this aim, we report the ligand-based virtual screening of GPER-binding molecules based on chemical similarity to (-)-epicatechin (flavanol reported as a ligand for the GPER receptor). Further structure-based screening allowed us to identify molecules with higher binding affinity to GPER based on molecular docking, molecular dynamic simulation and adaptative biasing force calculations. Here, we predicted 4 small molecules with a high ability to bind GPER exhibit favourable energy interaction.