Enhancing the accuracy of virtual screening: molecular dynamics with quantum-refined force fields

Summary A methodology aimed at improving the accuracy of current docking–scoring procedures is proposed, and validated through detailed tests of its performance in predicting the activity of HIV-1 protease inhibitors. This methodology is based on molecular dynamics simulations using a force field whose effective charges are refined by means of a novel procedure that relies on quantum-mechanical calculations and preserves the internal consistency of the parameterization scheme.     

Identification of natural compounds targeting SARS-CoV-2 Mpro by virtual screening and molecular dynamics simulations

The SARS-CoV-2 main protease (Mpro) has been chosen as a conserved molecular target to develop broad-spectrum antiviral drugs. Using molecular docking and molecular dynamics (MD) simulations, a total of 5600 natural compounds available for virtual screening were tested to identify potential inhibitors of SARS-CoV-2 Mpro. As a result, three natural compounds (pentagalloylglucose, malonylawobanin and gnetin E dihydride) were found to be potential inhibitors of SARS-CoV-2, which confirms the theoretical and practical significance of this approach for the design of SARS-CoV-2 inhibitors.     

Prediction of new chromene-based inhibitors of tubulin using structure-based virtual screening and molecular dynamics simulation methods

Multidrug resistance (MDR) is one of the serious problems in cancer research that causes failure in chemotherapy. Chromene-based compounds have been proven to be the novel anti-MDR agents for inhibiting proliferation of tumor cells through tubulin polymerization inhibition of by binding at the colchicine binding site. In this study, we screened a chromene-based database of small molecules using physicochemical, ADMET properties and molecular docking to identify potential hit compounds. In order to validate our hit compounds, molecular dynamics simulations and related analysis were carried out and the results suggest that our hit compounds (PubChem CIDs: 16814409, 17594471, 57367244 and 69899719) can prove to be potential inhibitors of tubulin. The in silico results show that the present hits, like colchicine, effectively suppressed the dynamic instability of microtubules and induced microtubule-depolymerization and cell cycle arrest.     

Evaluations of molecular docking programs for virtual screening

Structure-based virtual screening is carried out using molecular docking programs. A number of such docking programs are currently available, and the selection of docking program is difficult without knowing the characteristics or performance of each program. In this study, the screening performances of three molecular docking programs, DOCK, AutoDock, and GOLD, were evaluated with 116 target proteins. The screening performances were validated using two novel standards, along with a traditional enrichment rate measurement. For the evaluations, each docking run was repeated 1000 times with three initial conformations of a ligand. While each docking program has some merit over the other docking programs in some aspects, DOCK showed an unexpectedly better screening performance in the enrichment rates. Finally, we made several recommendations based on the evaluation results to enhance the screening performances of the docking programs.     

Inhibitors of SARS-3CLpro: virtual screening, biological evaluation, and molecular dynamics simulation studies

SARS-CoV from the coronaviridae family has been identified as the etiological agent of Severe Acute Respiratory Syndrome (SARS), a highly contagious upper respiratory disease that reached epidemic status in 2002. SARS-3CL(pro), a cysteine protease indispensible to the viral life cycle, has been identified as one of the key therapeutic targets against SARS. A combined ligand and structure-based virtual screening was carried out against the Asinex Platinum collection. Multiple low micromolar inhibitors of the enzyme were identified through this search, one of which also showed activity against SARS-CoV in a whole cell CPE assay. Furthermore, multinanosecond explicit solvent simulations were carried out using the docking poses of the identified hits to study the overall stability of the binding site interactions as well as identify important changes in the interaction profile that were not apparent from the docking study. Cumulative analysis of the evaluated compounds and the simulation studies led to the identification of certain protein-ligand interaction patterns which would be useful in further structure based design efforts.     

Collection and preparation of molecular databases for virtual screening

Drug discovery and development research is undergoing a paradigm shift from a linear and sequential nature of the various steps involved in the drug discovery process of the past to the more parallel approach of the present, due to a lack of sufficient correlation between activities estimated by in vitro and in vivo assays. This is attributed to the non-drug-likeness of the lead molecules, which has often been detected at advanced drug development stages. Thus a striking aspect of this paradigm shift has been early/parallel in silico prioritization of drug-like molecular databases (also database pre-processing), in addition to prioritizing compounds with high affinity and selectivity for a protein target. In view of this, a drug-like database useful for virtual screening has been created by prioritizing molecules from 36 catalog suppliers, using our recently derived binary QSAR based drug-likeness model as a filter. The performance of this model was assessed by a comparative evaluation with respect to commonly used filters implemented by the ZINC database. Since the model was derived considering all the limitations that have plagued the existing rules and models, it performs better than the existing filters and thus the molecules prioritized by this filter represent a better subset of drug-like compounds. The application of this model on exhaustive subsets of 4,972,123 molecules, many of which have passed the ZINC database filters for drug-likeness, led to a further prioritization of 2,920,551 drug-like molecules. This database may have a great potential for in silico virtual screening for discovering molecules, which may survive the later stages of the drug development research.     

Collection and preparation of molecular databases for virtual screening

Drug discovery and development research is undergoing a paradigm shift from a linear and sequential nature of the various steps involved in the drug discovery process of the past to the more parallel approach of the present, due to a lack of sufficient correlation between activities estimated by in vitro and in vivo assays. This is attributed to the non-drug-likeness of the lead molecules, which has often been detected at advanced drug development stages. Thus a striking aspect of this paradigm shift has been early/parallel in silico prioritization of drug-like molecular databases (also database pre-processing), in addition to prioritizing compounds with high affinity and selectivity for a protein target. In view of this, a drug-like database useful for virtual screening has been created by prioritizing molecules from 36 catalog suppliers, using our recently derived binary QSAR based drug-likeness model as a filter. The performance of this model was assessed by a comparative evaluation with respect to commonly used filters implemented by the ZINC database. Since the model was derived considering all the limitations that have plagued the existing rules and models, it performs better than the existing filters and thus the molecules prioritized by this filter represent a better subset of drug-like compounds. The application of this model on exhaustive subsets of 4,972,123 molecules, many of which have passed the ZINC database filters for drug-likeness, led to a further prioritization of 2,920,551 drug-like molecules. This database may have a great potential for in silico virtual screening for discovering molecules, which may survive the later stages of the drug development research.     

Accelerating high-throughput virtual screening through molecular pool-based active learning

Structure-based virtual screening is an important tool in early stage drug discovery that scores the interactions between a target protein and candidate ligands. As virtual libraries continue to grow (in excess of 10⁸ molecules), so too do the resources necessary to conduct exhaustive virtual screening campaigns on these libraries. However, Bayesian optimization techniques, previously employed in other scientific discovery problems, can aid in their exploration: a surrogate structure–property relationship model trained on the predicted affinities of a subset of the library can be applied to the remaining library members, allowing the least promising compounds to be excluded from evaluation. In this study, we explore the application of these techniques to computational docking datasets and assess the impact of surrogate model architecture, acquisition function, and acquisition batch size on optimization performance. We observe significant reductions in computational costs; for example, using a directed-message passing neural network we can identify 94.8% or 89.3% of the top-50 000 ligands in a 100M member library after testing only 2.4% of candidate ligands using an upper confidence bound or greedy acquisition strategy, respectively. Such model-guided searches mitigate the increasing computational costs of screening increasingly large virtual libraries and can accelerate high-throughput virtual screening campaigns with applications beyond docking.

Bayesian optimization can accelerate structure-based virtual screening campaigns by minimizing the total number of simulations performed while still identifying the vast majority of computational hits.     

Hit identification against peptidyl-prolyl isomerase of Theileria annulata by combined virtual high-throughput screening and molecular dynamics simulation approach

Theileria annulata secretes peptidyl prolyl isomerase enzyme (TaPIN1) to manipulate the host cell oncogenic signaling pathway by disrupting the tumor suppressor F-box and WD repeat domain-containing 7 (FBW7) protein level leading to an increased level of c-Jun proto-oncogene. Buparvaquone is a hydroxynaphthoquinone anti-theilerial drug and has been used to treat theileriosis. However, TaPIN1 contains the A53 P mutation that causes drug resistance. In this study, potential TaPIN1 inhibitors were investigated using a library of naphthoquinone derivatives. Comparative models of mutant (m) and wild type (wt) TaPIN1 were predicted and energy minimization was followed by structure validation. A naphthoquinone (hydroxynaphthalene-1,2-dione, hydroxynaphthalene-1,4-dione) and hydroxynaphthalene-2,3-dione library was screened by Schrödinger Glide HTVS, SP and XP docking methodologies and the docked compounds were ranked by the Glide XP scoring function. The two highest ranked docked compounds Compound 1 (4-hydroxy-3-[3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxynaphthalene-1,2-dione) and Compound 2 (6-acetyl-1,4,5,7,8-pentahydroxynaphthalene-2,3-dione) were used for further molecular dynamics (MD) simulation studies. The MD results showed that ligand Compound 1 was located in the active site of both mTaPIN1 and wtTaPIN1 and could be proposed as a potential inhibitor by acting as a substrate antagonist. However, ligand Compound 2 was displaced away from the binding pocket of wtTaPIN1 but was located near the active site binding pocket of mTaPIN1 suggesting that could be selectively evaluated as a potential inhibitor against the mTaPIN1. Compound 1 and Compound 2 ligands are potential inhibitors but Compound 2 is suggested as a better inhibitor for mTaPIN1. These ligands could also further evaluated as potential inhibitors against human peptidyl prolyl isomerase which causes cancer in humans by using the same mechanism as TaPIN1.     

Assessing the discriminatory power of scoring functions for virtual screening

The efficiency of scoring functions for hit identification is usually quantified in terms of enrichment factors and enrichment curves. Close inspection of simulated and real score distributions from virtual screening, however, suggests that 'analysis of variance' (ANOVA) is a more reliable method for assessing their performance. Using ANOVA to quantify the discriminatory power of scoring functions with respect to ligands, decoys, and a reproducible reference database has the potential to facilitate the advancement of scoring functions significantly.     

Structure-based virtual screening and molecular dynamics of phytochemicals derived from Saudi medicinal plants to identify potential COVID-19 therapeutics

Coronavirus disease 2019 (COVID-19) has affected almost every country in the world by causing a global pandemic with a high mortality rate. Lack of an effective vaccine and/or antiviral drugs against SARS-CoV-2, the causative agent, has severely hampered the response to this novel coronavirus. Natural products have long been used in traditional medicines to treat various diseases, and purified phytochemicals from medicinal plants provide a valuable scaffold for the discovery of new drug leads. In the present study, we performed a computational screening of an in-house database composed of ~1000 phytochemicals derived from traditional Saudi medicinal plants with recognised antiviral activity. Structure-based virtual screening was carried out against three druggable SARS-CoV-2 targets, viral RNA-dependent RNA polymerase (RdRp), 3-chymotrypsin-like cysteine protease (3CL^pro) and papain like protease (PL^pro) to identify putative inhibitors that could facilitate the development of potential anti-COVID-19 drug candidates. Computational analyses identified three compounds inhibiting each target, with binding affinity scores ranging from −9.9 to −6.5 kcal/mol. Among these, luteolin 7-rutinoside, chrysophanol 8-(6-galloylglucoside) and kaempferol 7-(6″-galloylglucoside) bound efficiently to RdRp, while chrysophanol 8-(6-galloylglucoside), 3,4,5-tri-O-galloylquinic acid and mulberrofuran G interacted strongly with 3CL^pro, and withanolide A, isocodonocarpine and calonysterone bound tightly to PL^pro. These potential drug candidates will be subjected to further in vitro and in vivo studies and may assist the development of effective anti-COVID-19 drugs.     

Consideration of molecular weight during compound selection in virtual target-based database screening

Virtual database screening allows for millions of chemical compounds to be computationally selected based on structural complimentary to known inhibitors or to a target binding site on a biological macromolecule. Compound selection in virtual database screening when targeting a biological macromolecule is typically based on the interaction energy between the chemical compound and the target macromolecule. In the present study it is shown that this approach is biased toward the selection of high molecular weight compounds due to the contribution of the compound size to the energy score. To account for molecular weight during energy based screening, we propose normalization strategies based on the total number of heavy atoms in the chemical compounds being screened. This approach is computationally efficient and produces molecular weight distributions of selected compounds that can be selected to be (1) lower than that of the original database used in the virtual screening, which may be desirable for selection of leadlike compounds or (2) similar to that of the original database, which may be desirable for the selection of drug-like compounds. By eliminating the bias in target-based database screening toward higher molecular weight compounds it is anticipated that the proposed procedure will enhance the success rate of computer-aided drug design.     

Pharmacophore model-aided virtual screening combined with comparative molecular docking and molecular dynamics for identification of marine natural products as SARS-CoV-2 papain-like protease inhibitors

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

Quadrupole correction for halogen bonding description in virtual screening and molecular docking

Halogen bonding is electrostatic attraction between halogen atoms in an organic molecule and Lewis bases. It is important to consider halogen bonding during molecular docking and virtual screening, in particular, at early stages of drug development. A new scoring function AutoDock-XB, which takes into account halogen bonding by means of the quadrupole correction, has been constructed. The function has been tested for a series of phosphodiesterase-5 inhibitors.     

Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures

Fingerprint-based similarity searching is widely used for virtual screening when only a single bioactive reference structure is available. This paper reviews three distinct ways of carrying out such searches when multiple bioactive reference structures are available: merging the individual fingerprints into a single combined fingerprint; applying data fusion to the similarity rankings resulting from individual similarity searches; and approximations to substructural analysis. Extended searches on the MDL Drug Data Report database suggest that fusing similarity scores is the most effective general approach, with the best individual results coming from the binary kernel discrimination technique.     

Rapid screening of a receptor with molecular memory

[reaction: see text] Atropisomeric receptor 1 can change conformation and maintain the new conformation when heated and cooled in the presence of a guest molecule. This molecular memory can be used as a rapid method of screening potential guests. Heating atropisomeric diacid 1 with various hydrogen-bonding guests leads to a shift in the syn/anti ratio that could be easily monitored as it is stable at room temperature even in the absence of the guest molecules.     

In search of novel inhibitors of anti-cancer drug target fibroblast growth factor receptors: Insights from virtual screening,molecular docking,and molecular dynamics

Fibroblast growth factor receptors (FGFR) are an essential player in oncogenesis and tumor progression. LY2874455 was identified as a pan-FGFR inhibitor and has gone through phase I clinical trial. In the current study, virtual screening was conducted against the PubChem database using a pharmacophore model generated from the crystal structure of FGFR4 inhibited by LY2874455. PubChem 137300327 was identified as the most suitable compound from this screening. Later, molecular docking and molecular dynamics studies conducted with FGFRs corroborated the initial finding. Analysis of ADMET properties disclosed that LY2874455 and PubChem 137300327 share alike properties. Our study suggests that PubChem 137300327 is a potential pan-FGFR inhibitor and can be exploited to treat different cancers following validation in proper wet-lab experiments and study in animal cancer models. This compound also follows Lipinski’s rules and can be used as a lead compound to synthesize more effective anticancer compounds.