pROC-Chemotype Plots Enhance the Interpretability of Benchmarking Results in Structure-Based Virtual Screening

Molecular generation strategy and optimization based on A2C reinforcement learning in de novo drug design

MOTIVATION

In the field of pharmacochemistry, it is a time-consuming and expensive process for the new drug development. The existing drug design methods face a significant challenge in terms of generation efficiency and quality.

RESULTS

In this paper, we proposed a novel molecular generation strategy and optimization based on A2C reinforcement learning. In molecular generation strategy, we adopted transformer-DNN to retain the scaffolds advantages, while accounting for the generated molecules' similarity and internal diversity by dynamic parameter adjustment, further improving the overall quality of molecule generation. In molecular optimization, we introduced heterogeneous parallel supercomputing for large-scale molecular docking based on message passing interface communication technology to rapidly obtain bioactive information, thereby enhancing the efficiency of drug design. Experiments show that our model can generate high-quality molecules with multi-objective properties at a high generation efficiency, with effectiveness and novelty close to 100%. Moreover, we used our method to assist shandong university school of pharmacy to find several candidate drugs molecules of anti-PEDV.

AVAILABILITY AND IMPLEMENTATION

The datasets involved in this method and the source code are freely available to academic users at https://github.com/wq-sunshine/MomdTDSRL.git.

In Silico Targeting of Fascin Protein for Cancer Therapy: Benchmarking, Virtual Screening and Molecular Dynamics Approaches

Fascin is an actin-bundling protein overexpressed in various invasive metastatic carcinomas through promoting cell migration and invasion. Therefore, blocking Fascin binding sites is considered a vital target for antimetastatic drugs. This inspired us to find new Fascin binding site blockers. First, we built an active compound set by collecting reported small molecules binding to Fascin's binding site 2. Consequently, a high-quality decoys set was generated employing DEKOIS 2.0 protocol to be applied in conducting the benchmarking analysis against the selected Fascin structures. Four docking programs, MOE, AutoDock Vina, VinaXB, and PLANTS were evaluated in the benchmarking study. All tools indicated better-than-random performance reflected by their pROC-AUC values against the Fascin crystal structure (PDB: ID 6I18). Interestingly, PLANTS exhibited the best screening performance and recognized potent actives at early enrichment. Accordingly, PLANTS was utilized in the prospective virtual screening effort for repurposing FDA-approved drugs (DrugBank database) and natural products (NANPDB). Further assessment via molecular dynamics simulations for 100 ns endorsed Remdesivir (DrugBank) and NANPDB3 (NANPDB) as potential binders to Fascin binding site 2. In conclusion, this study delivers a model for implementing a customized DEKOIS 2.0 benchmark set to enhance the VS success rate against new potential targets for cancer therapies.

EViS: An Enhanced Virtual Screening Approach Based on Pocket-Ligand Similarity

Virtual screening (VS) is a popular technology in drug discovery to identify a new scaffold of actives for a specific drug target, which can be classified into ligand-based and structure-based approaches. As the number of protein-ligand complex structures available in public databases increases, it would be possible to develop a template searching-based VS approach that utilizes such information. In this work, we proposed an enhanced VS approach, which is termed EViS, to integrate ligand docking, protein pocket template searching, and ligand template shape similarity calculation. A novel and simple PL-score to characterize local pocket-ligand template similarity was used to evaluate the screening compounds. Benchmark tests were performed on three datasets including DUDE, LIT-PCBA, and DEKOIS. EViS achieved the average enrichment factors (EFs) of 27.8 and 23.4 at a 1% cutoff for experimental and predicted structures on the widely used DUDE dataset, respectively. Detailed data analysis shows that EViS benefits from obtaining favorable ligand poses from docking and using such ligand geometric information to perform three-dimensional (3D) ligand similarity calculations, and the PL-score is efficient to screen compounds based on template searching in the protein-ligand structure database.

Virtual screening using docking and molecular dynamics of cannabinoid analogs against CB1 and CB2 receptors

BACKGROUND

Cannabis sativa has been attributed to different pharmacological properties. A number of secondary metabolites such as tetrahydrocannabinol (THC), cannabinol (CBD), and different analogs, with highly promising biological activity on CB₁ and CB₂ receptors, have been identified.

METHODS

Thus, this study aimed was to evaluate the activity of THC, CBD, and their analogs using molecular docking and molecular dynamics simulations (MD) methods. Initially, the molecules (ligands) were selected by bioinformatics searches in databases. Subsequently, CB₁ and CB₂ receptors were retrieved from the protein data bank database. Afterward, each receptor and its ligands were optimized to perform molecular docking. Then, MD Simulation was performed with the most stable ligand-receptor complexes. Finally, the Molecular Mechanics-Generalized Born Surface Area (MM-PBSA) method was applied to analyze the binding free energy between ligands and cannabinoid receptors.

RESULTS

The results obtained showed that ligand LS-61176 presented the best affinity in the molecular docking analysis. Also, this analog could be a CB₁ negative allosteric modulator like CBD and probably an agonist in CB₂ like THC and CBD according to their dynamic behavior in silico. The possibility of having a THC and a CBD analog (LS-61176) as a promising molecule for experimental evaluation since it could have no central side-effects on CB₁ and have effects of CB₂ useful in pain, inflammation, and some immunological disorders. Docking results were validate using ROC curve for both cannabinoids receptor where AUC for CB1 receptor was 0.894±0.024, and for CB2 receptor AUC was 0.832±0032, indicating good affinity prediction.

Facilitating SARS CoV-2 RNA-Dependent RNA polymerase (RdRp) drug discovery by the aid of HCV NS5B palm subdomain binders: In silico approaches and benchmarking

Corona Virus 2019 Disease (COVID-19) is a rapidly emerging pandemic caused by a newly discovered beta coronavirus, called Sever Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2). SARS CoV-2 is an enveloped, single stranded RNA virus that depends on RNA-dependent RNA polymerase (RdRp) to replicate. Therefore, SARS CoV-2 RdRp is considered as a promising target to cease virus replication. SARS CoV-2 polymerase shows high structural similarity to Hepatitis C Virus-1b genotype (HCV-1b) polymerase.

Arising from the high similarity between SARS CoV-2 RdRp and HCV NS5B, we utilized the reported small-molecule binders to the palm subdomain of HCV NS5B (genotype 1b) to generate a high-quality DEKOIS 2.0 benchmark set and conducted a benchmarking analysis against HCV NS5B. The three highly cited and publicly available docking tools AutoDock Vina, FRED and PLANTS were benchmarked. Based on the benchmarking results and analysis via pROC-Chemotype plot, PLANTS showed the best screening performance and can recognize potent binders at the early enrichment. Accordingly, we used PLANTS in a prospective virtual screening to repurpose both the FDA-approved drugs (DrugBank) and the HCV-NS5B palm subdomain binders (BindingDB) for SARS CoV-2 RdRp palm subdomain. Further assessment by molecular dynamics simulations for 50 ns recommended diosmin (from DrugBank) and compound 3 (from BindingDB) to be the best potential binders to SARS CoV-2 RdRp palm subdomain. The best predicted compounds are recommended to be biologically investigated against COVID-19. In conclusion, this work provides in-silico analysis to propose possible SARS CoV-2 RdRp palm subdomain binders recommended as a remedy for COVID-19. Up-to-our knowledge, this study is the first to propose binders at the palm subdomain of SARS CoV2 RdRp. Furthermore, this study delivers an example of how to make use of a high quality custom-made DEKOIS 2.0 benchmark set as a procedure to elevate the virtual screening success rate against a vital target of the rapidly emerging pandemic.

Supporting SARS-CoV-2 Papain-Like Protease Drug Discovery: In silico Methods and Benchmarking

The coronavirus disease 19 (COVID-19) is a rapidly growing pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Its papain-like protease (SARS-CoV-2 PLpro) is a crucial target to halt virus replication. SARS-CoV PLpro and SARS-CoV-2 PLpro share an 82.9% sequence identity and a 100% sequence identity for the binding site reported to accommodate small molecules in SARS-CoV. The flexible key binding site residues Tyr269 and Gln270 for small-molecule recognition in SARS-CoV PLpro exist also in SARS-CoV-2 PLpro. This inspired us to use the reported small-molecule binders to SARS-CoV PLpro to generate a high-quality DEKOIS 2.0 benchmark set. Accordingly, we used them in a cross-benchmarking study against SARS-CoV-2 PLpro. As there is no SARS-CoV-2 PLpro structure complexed with a small-molecule ligand publicly available at the time of manuscript submission, we built a homology model based on the ligand-bound SARS-CoV structure for benchmarking and docking purposes. Three publicly available docking tools FRED, AutoDock Vina, and PLANTS were benchmarked. All showed better-than-random performances, with FRED performing best against the built model. Detailed performance analysis via pROC-Chemotype plots showed a strong enrichment of the most potent bioactives in the early docking ranks. Cross-benchmarking against the X-ray structure complexed with a peptide-like inhibitor confirmed that FRED is the best-performing tool. Furthermore, we performed cross-benchmarking against the newly introduced X-ray structure complexed with a small-molecule ligand. Interestingly, its benchmarking profile and chemotype enrichment were comparable to the built model. Accordingly, we used FRED in a prospective virtual screen of the DrugBank database. In conclusion, this study provides an example of how to harness a custom-made DEKOIS 2.0 benchmark set as an approach to enhance the virtual screening success rate against a vital target of the rapidly emerging pandemic.

Small-Molecule Intervention At The Dimerization Interface Of Survivin By Novel Rigidized Scaffolds

Introduction

Survivin is a nodal protein involved in several cellular pathways. It is a member of the IAP family and an integral component of the chromosomal passenger complex, where it binds to borealin and INCENP through its dimerization interface. By targeting survivin with a small molecule at its dimerization interface, inhibition of the proliferation of cancer cells has been suggested. With Abbott 8, a small-molecule dimerization inhibitor has been recently reported. The structure–activity relationship of this series of inhibitors implied that the middle pyridin-2(1H)-one ring did not tolerate modifications of any kind.

Methods

Based on the synthetic strategy of Abbott 8 using multicomponent reactions, we synthesized a series of small molecules bearing a novel rigidized core scaffold. This rigidization strategy was accomplished by integrating the pyridin-2(1H)-one and its 6-phenyl substituent into a tricyclic structure, linking position 5 of pyridin-2(1H)-one to the phenyl substituent by rings of different sizes. The new scaffolds were designed based on in silico molecular dynamics of survivin.

Results

Binding of these rigidized scaffolds to the recombinant L54M mutant of survivin was evaluated, revealing affinities in the low micromolar range.

Conclusion

This easily accessible, new class of survivin-dimerization modulators is an interesting starting point for further lead optimization.

Synthesis, in vitro biological evaluation and in silico studies of certain arylnicotinic acids conjugated with aryl (thio)semicarbazides as a novel class of anti-leishmanial agents

Herein we introduce new compounds as conjugates of arylnicotinic acids with aryl (thio)semicarbazide derivatives. Based on a structure-guided approach, they were designed to possess anti-leishmanial activity through anti-folate mechanism, via targeting Leishmania major pteridine reductase 1 (Lm-PTR1). The in vitro anti-promastigote and anti-amastigote activity were promising for many thiosemicarbazide derivatives and superior to the reference miltefosine. The most active compounds 8i and 8j exhibited their anti-amastigote activity with IC₅₀ values of 4.2 and 3.3 μM, respectively, compared to reference miltefosine (IC₅₀ value of 7.3). Their anti-folate mechanism was confirmed via the ability of folic and folinic acids to reverse the anti-leishmanial activity of these compounds, comparably to Lm-PTR1 inhibitor trimethoprim. Interestingly, the in vitro cytotoxicity test of the most active compounds displayed higher selectivity indices than that of miltefosine emphasizing their safety on mammalian cells. Furthermore, the docking experiments on Lm-PTR1 as a putative target rationalized the in vitro anti-leishmanial activity. The in silico predictions exhibited promising pharmacokinetics and drug-likeness profiles of the most active compounds. Generally, this work introduces a fruitful matrix for new anti-leishmanial chemotype which would extend the chemical space for the anti-leishmanial activity.

Synthesis, anti-inflammatory screening, molecular docking, and COX-1,2/-5-LOX inhibition profile of some novel quinoline derivatives

New quinoline compounds comprising pyrazole scaffold through different amide linkages were synthesized. The synthesized compounds were evaluated for their anti-inflammatory activity. Eight compounds (5c, 11b,c, 12c, 14a,b, 20a and 21a) were found to exhibit promising anti-inflammatory profiles in acute and sub-acute inflammatory models. They were screened for their ulcerogenic activity and none of them showed significant ulcerogenic activity comparable to the reference drug celecoxib and are well tolerated by experimental animals with high safety margin (ALD50 > 0.3 g/kg). Compounds 5c, 11b,c, 12c, 14a,b, 20a and 21a showed significant in vitro LOX inhibitory activity higher than that of zileuton. In vitro COX-1/COX-2 inhibition study revealed that compounds 12c, 14a,b and 20a showed higher selectivity towards COX-2 than COX-1. Among the tested compounds, 12c, 14a and 14b showed the highest inhibitory activity against COX-2 with an IC50 values of 0.1, 0.11 and 0.11 μM respectively. The docking experiments attempted to postulate the binding mode for the most active compounds in the binding site of COX-2 enzymes and confirmed the high selectivity binding towards COX-2 enzyme over COX-1.

Decoys Selection in Benchmarking Datasets: Overview and Perspectives

Virtual Screening (VS) is designed to prospectively help identifying potential hits, i.e., compounds capable of interacting with a given target and potentially modulate its activity, out of large compound collections. Among the variety of methodologies, it is crucial to select the protocol that is the most adapted to the query/target system under study and that yields the most reliable output. To this aim, the performance of VS methods is commonly evaluated and compared by computing their ability to retrieve active compounds in benchmarking datasets. The benchmarking datasets contain a subset of known active compounds together with a subset of decoys, i.e., assumed non-active molecules. The composition of both the active and the decoy compounds subsets is critical to limit the biases in the evaluation of the VS methods. In this review, we focus on the selection of decoy compounds that has considerably changed over the years, from randomly selected compounds to highly customized or experimentally validated negative compounds. We first outline the evolution of decoys selection in benchmarking databases as well as current benchmarking databases that tend to minimize the introduction of biases, and secondly, we propose recommendations for the selection and the design of benchmarking datasets.

Synthesis, modeling and biological evaluation of hybrids from pyrazolo[1,5c]pyrimidine as antileishmanial agents

Aim: A new series of pyrazolo[1,5-c]pyrimidines were synthesized by different hybridization strategies. Methodology: All structures were confirmed by IR, 1H, 13C, 1H-13C heteronuclear multiple-quantum correlation (HMQC) spectra and microanalysis. They were evaluated for their in vitro antileishmanial activity against miltefosine and amphotericin B deoxycholate as reference drugs. Results: The most active compounds 2a and 9a demonstrated superior potencies to miltefosine by ten- and six-fold, respectively, for the promastigote form, and by 5.5-fold for the amastigote form. Their binding scenario to Leishmania major pteridine reductase was rationalized by docking experiments. In addition, all compounds were safe for the experimental animals orally up to 150 mg/kg and parenterally up to 75 mg/kg. Conclusion: This study provides novel chemotype class for antileishmanial activity.

[Formula: see text]

Synthesis, evaluation and modeling of some triazolothienopyrimidinones as anti-inflammatory and antimicrobial agents

AIM

New triazolotetrahydrobenzothienopyrimidinone derivatives were synthesized.

EXPERIMENTAL

Their structures were confirmed, and their anti-inflammatory, antimicrobial activities and ulcerogenic potentials were evaluated.

RESULTS

Compounds 7a, 10a and 11a showed minimal ulcerogenic effect and high selectivity toward human recombinant COX-2 over COX-1 enzyme with IC₅₀ values of 1.39, 1.22 and 0.56 μM, respectively. Their docking outcome correlated with their biological activity and confirmed the high selectivity binding toward COX-2. Compound 12b displayed antimicrobial activity comparable to that of ampicillin against Escherichia coli while compounds 6 and 11c were similar to ampicillin against Staphylococcus aureus. In addition, compounds 7a, 9a, 10b and 11c showed dual anti-inflammatory/antimicrobial activities.

CONCLUSION

This work represents a promising matrix for developing new potential anti-inflammatory, antimicrobial and dual antimicrobial/anti-inflammatory candidates. [Formula: see text].