Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

Developing New Treatments for COVID-19 through Dual-Action Antiviral/Anti-Inflammatory Small Molecules and Physiologically Based Pharmacokinetic Modeling

Broad-spectrum antiviral agents that are effective against many viruses are difficult to develop, as the key molecules, as well as the biochemical pathways by which they cause infection, differ largely from one virus to another. This was more strongly highlighted by the COVID-19 pandemic, which found health systems all over the world largely unprepared and proved that the existing armamentarium of antiviral agents is not sufficient to address viral threats with pandemic potential. The clinical protocols for the treatment of COVID-19 are currently based on the use of inhibitors of the inflammatory cascade (dexamethasone, baricitinib), or inhibitors of the cytopathic effect of the virus (monoclonal antibodies, molnupiravir or nirmatrelvir/ritonavir), using different agents. There is a critical need for an expanded armamentarium of orally bioavailable small-molecular medicinal agents, including those that possess dual antiviral and anti-inflammatory (AAI) activity that would be readily available for the early treatment of mild to moderate COVID-19 in high-risk patients. A multidisciplinary approach that involves the use of in silico screening tools to identify potential drug targets of an emerging pathogen, as well as in vitro and in vivo models for the determination of a candidate drug’s efficacy and safety, are necessary for the rapid and successful development of antiviral agents with potentially dual AAI activity. Characterization of candidate AAI molecules with physiologically based pharmacokinetics (PBPK) modeling would provide critical data for the accurate dosing of new therapeutic agents against COVID-19. This review analyzes the dual mechanisms of AAI agents with potential anti-SARS-CoV-2 activity and discusses the principles of PBPK modeling as a conceptual guide to develop new pharmacological modalities for the treatment of COVID-19.

Based on Network Pharmacology and Molecular Dynamics Simulations, Baicalein, an Active Ingredient of Yiqi Qingre Ziyin Method, Potentially Protects Patients With Atrophic Rhinitis From Cognitive Impairment

Cognition may be improved by the active ingredients of the Yiqi Qingre Ziyin method in patients with atrophic rhinitis (AR). This study aimed to identify potential targets of the Yiqi Qingre Ziyin method for the treatment of patients with cognitive impairment. Nasal mucosal tissue samples from patients with AR were subjected to proteomic assays, and differentially expressed proteins were obtained. To explore the mechanism of AR leading to mild cognitive impairment (MCI), a differential analysis of AR related differential proteins in the MCI related GSE140831 dataset was performed. Most AR-related differential proteins are also differentially expressed in peripheral blood tissues of MCI, have similar biological functions and are enriched in similar pathways. These co-expressed differential factors in AR and MCI are known as common differential proteins of AR and MCI (CDPAM). Based on the analysis and validation of the random forest, support vector machine and neural network models, CDPAM acted as a diagnostic marker for MCI risk. Cytochrome C (CYCS) was significantly upregulated in the peripheral blood of patients with MCI. The active ingredients in the Yiqi Qingre Ziqin method were obtained and targeted 137 proteins. Among these targeted proteins, CYCS belong to the CDPAM set. Molecular docking and molecular dynamics analysis revealed that baicalein, an active ingredient in the Yiqi Qingre Ziyin method, stably targeted the CYCS protein. Results of the enrichment analysis revealed that the up-regulation of CYCS expression may have a defensive effect on the cells to resist foreign stimuli. Therefore, baicalein, an active ingredient in the Yiqi Qingre Ziyin method, may prevent the development and progression of MCI by targeting the CYCS protein.

From Traditional Ethnopharmacology to Modern Natural Drug Discovery: A Methodology Discussion and Specific Examples

Ethnopharmacology, through the description of the beneficial effects of plants, has provided an early framework for the therapeutic use of natural compounds. Natural products, either in their native form or after crude extraction of their active ingredients, have long been used by different populations and explored as invaluable sources for drug design. The transition from traditional ethnopharmacology to drug discovery has followed a straightforward path, assisted by the evolution of isolation and characterization methods, the increase in computational power, and the development of specific chemoinformatic methods. The deriving extensive exploitation of the natural product chemical space has led to the discovery of novel compounds with pharmaceutical properties, although this was not followed by an analogous increase in novel drugs. In this work, we discuss the evolution of ideas and methods, from traditional ethnopharmacology to in silico drug discovery, applied to natural products. We point out that, in the past, the starting point was the plant itself, identified by sustained ethnopharmacological research, with the active compound deriving after extensive analysis and testing. In contrast, in recent years, the active substance has been pinpointed by computational methods (in silico docking and molecular dynamics, network pharmacology), followed by the identification of the plant(s) containing the active ingredient, identified by existing or putative ethnopharmacological information. We further stress the potential pitfalls of recent in silico methods and discuss the absolute need for in vitro and in vivo validation as an absolute requirement. Finally, we present our contribution to natural products’ drug discovery by discussing specific examples, applying the whole continuum of this rapidly evolving field. In detail, we report the isolation of novel antiviral compounds, based on natural products active against influenza and SARS-CoV-2 and novel substances active on a specific GPCR, OXER1.

Octanol-Water Partition Coefficients of Fluorinated Drug Molecules with Continuum Solvation Models

In this work, we have examined the efficiency of continuum solvation models, used with density functional theory methods, in calculating octanol-water partition coefficients (logP) of 56 fluorine containing drug molecules. The solvation model based on density model computed logP values that are in good agreement with the benchmark values. The conductor-like polarizable continuum models computed results have issues in predicting correct trend, often with reversal of sign from benchmark. The choice of basis set does not show significant effect, and the selection of atomic radii affects geometry convergence during calculations.

The performance of ensemble-based free energy protocols in computing binding affinities to ROS1 kinase

Optimization of binding affinities for compounds to their target protein is a primary objective in drug discovery. Herein we report on a collaborative study that evaluates a set of compounds binding to ROS1 kinase. We use ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and TIES (thermodynamic integration with enhanced sampling) protocols to rank the binding free energies. The predicted binding free energies from ESMACS simulations show good correlations with experimental data for subsets of the compounds. Consistent binding free energy differences are generated for TIES and ESMACS. Although an unexplained overestimation exists, we obtain excellent statistical rankings across the set of compounds from the TIES protocol, with a Pearson correlation coefficient of 0.90 between calculated and experimental activities.

Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling

Computer modeling is a method that is widely used in scientific investigations to predict the biological activity, toxicity, pharmacokinetics, and synthesis strategy of compounds based on the structure of the molecule. This work is a systematic review of articles performed in accordance with the recommendations of PRISMA and contains information on computer modeling of the interaction of classical flavonoids with different biological targets. The review of used computational approaches is presented. Furthermore, the affinities of flavonoids to different targets that are associated with the infection, cardiovascular, and oncological diseases are discussed. Additionally, the methodology of bias risks in molecular docking research based on principles of evidentiary medicine was suggested and discussed. Based on this data, the most active groups of flavonoids and lead compounds for different targets were determined. It was concluded that flavonoids are a promising object for drug development and further research of pharmacology by in vitro, ex vivo, and in vivo models is required.

Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next?

Conventional in silico modeling is often viewed as 'one-target' or 'single-task' computer-aided modeling since it mainly relies on forecasting an endpoint of interest from similar input data. Multitasking or multitarget in silico modeling, in contrast, embraces a set of computational techniques that efficiently integrate multiple types of input data for setting up unique in silico models able to predict the outcome(s) relating to various experimental and/or theoretical conditions. The latter, specifically, based upon the Box-Jenkins moving average approach, has been applied in the last decade to several research fields including drug and materials design, environmental sciences, and nanotechnology. The present review discusses the current status of multitasking computer-aided modeling efforts, meanwhile describing both the existing challenges and future opportunities of its underlying techniques. Some important applications are also discussed to exemplify the ability of multitasking modeling in deriving holistic and reliable in silico classification-based models as well as in designing new chemical entities, either through fragment-based design or virtual screening. Focus will also be given to some software recently developed to automate and accelerate such types of modeling. Overall, this review may serve as a guideline for researchers to grasp the scope of multitasking computer-aided modeling as a promising in silico tool.

Molecular Descriptors as a Facile Tool toward Designing Surface-Functionalized Nanoparticles for Drug Delivery

Modulating the surface chemistry of nanoparticles, often by grafting hydrophilic polymer brushes (e.g., polyethylene glycol) to prepare nanoformulations that can resist opsonization in a hematic environment and negotiate with the mucus barrier, is a popular strategy toward developing biocompatible and effective nano-drug delivery systems. However, there is a need for tools that can screen multiple surface ligands and cluster them based on both structural similarity and physicochemical attributes. Molecular descriptors offer numerical readouts based on molecular properties and provide a fertile ground for developing quick screening platforms. Thus, a study was conducted with 14 monomers/repeating blocks of polymeric chains, namely, oxazoline, acrylamide, vinylpyrrolidone, glycerol, acryloyl morpholine, dimethyl acrylamide, hydroxypropyl methacrylamide, hydroxyethyl methacrylamide, sialic acid, carboxybetaine acrylamide, carboxybetaine methacrylate, sulfobetaine methacrylate, methacryloyloxyethyl phosphorylcholine, and vinyl-pyridinio propanesulfonate, capable of imparting hydrophilicity to a surface when assembled as polymeric brushes. Employing free, Web-based, and user-friendly platforms, such as SwissADME and ChemMine tools, a series of molecular descriptors and Tanimoto coefficient of molecular pairs were determined, followed by hierarchical clustering analyses. Molecular pairs of oxazoline/dimethyl acrylamide, hydroxypropyl methacrylamide/hydroxyethyl methacrylamide, acrylamide/glycerol, carboxybetaine acrylamide/vinyl-pyridinio propanesulfonate, and sulfobetaine methacrylate/methacryloyloxyethyl phosphorylcholine were clustered together. Similarly, the molecular pair of hydroxypropyl methacrylamide/hydroxyethyl methacrylamide demonstrated a high Tanimoto coefficient of >0.9, whereas the pairs oxazoline/vinylpyrrolidone, acrylamide/dimethyl acrylamide, acryloyl morpholine/dimethyl acrylamide, acryloyl morpholine/hydroxypropyl methacrylamide, acryloyl morpholine/hydroxyethyl methacrylamide, carboxybetaine methacrylate/sulfobetaine methacrylate, and glycerol/hydroxypropyl methacrylamide had a Tanimoto coefficient of >0.8. The analyzed data not only demonstrated the ability of such in silico tools as a facile technique in clustering molecules of interest based on their structure and physicochemical characteristics but also provided vital information on their behavior within biological systems, including the ability to engage an array of possible molecular targets when the monomers are self-assembled on nanoparticulate surfaces.

Hypothesis-Driven, Structure-Based Design in Photopharmacology: The Case of eDHFR Inhibitors

Photopharmacology uses light to regulate the biological activity of drugs. This precise control is obtained through the incorporation of molecular photoswitches into bioactive molecules. A major challenge for photopharmacology is the rational design of photoswitchable drugs that show light-induced activation. Computer-aided drug design is an attractive approach toward more effective, targeted design. Herein, we critically evaluated different structure-based approaches for photopharmacology with Escherichia coli dihydrofolate reductase (eDHFR) as a case study. Through the iterative examination of our hypotheses, we progressively tuned the design of azobenzene-based, photoswitchable eDHFR inhibitors in five design–make–switch–test–analyze cycles. Targeting a hydrophobic subpocket of the enzyme and a specific salt bridge only with the thermally metastable cis-isomer emerged as the most promising design strategy. We identified three inhibitors that could be activated upon irradiation and reached potencies in the low-nanomolar range. Above all, this systematic study provided valuable insights for future endeavors toward rational photopharmacology.

Virtual screening of phytochemicals from Indian medicinal plants against the endonuclease domain of SFTS virus L polymerase

Severe fever with thrombocytopenia syndrome virus (SFTSV) causes a highly infectious disease with reported mortality in the range 2.8% to 47%. The replication and transcription of the SFTSV genome is performed by L polymerase, which has both an RNA dependent RNA polymerase domain and an N-terminal endonuclease (endoN) domain. Due to its crucial role in the cap-snatching mechanism required for initiation of viral RNA transcription, the endoN domain is an ideal antiviral drug target. In this virtual screening study for the identification of potential inhibitors of the endoN domain of SFTSV L polymerase, we have used molecular docking and molecular dynamics (MD) simulation to explore the natural product space of 14 011 phytochemicals from Indian medicinal plants. After generating a heterogeneous ensemble of endoN domain structures reflecting conformational diversity of the corresponding active site using MD simulations, ensemble docking of the phytochemicals was performed against the endoN domain structures. Apart from the ligand binding energy from docking, our virtual screening workflow imposes additional filters such as drug-likeness, non-covalent interactions with key active site residues, toxicity and chemical similarity with other hits, to identify top 5 potential phytochemical inhibitors of endoN domain of SFTSV L polymerase. Further, the stability of the protein–ligand docked complexes for the top 5 potential inhibitors was analyzed using MD simulations. The potential phytochemical inhibitors, predicted in this study using contemporary computational methods, are expected to serve as lead molecules in future experimental studies towards development of antiviral drugs against SFTSV.

Virtual screening of a large phytochemical library from Indian medicinal plants to identify potential endonuclease inhibitors against emerging virus SFTSV.

Isolation and In Silico Anti-SARS-CoV-2 Papain-like Protease Potentialities of Two Rare 2-Phenoxychromone Derivatives from Artemisia spp

Two rare 2-phenoxychromone derivatives, 6-demethoxy-4`-O-capillarsine (1) and tenuflorin C (2), were isolated from the areal parts of Artemisia commutata and A. glauca, respectively, for the first time. Being rare in nature, the inhibition potentialities of 1 and 2 against SARS-CoV-2 was investigated using multistage in silico techniques. At first, molecular similarity and fingerprint studies were conducted for 1 and 2 against co-crystallized ligands of eight different COVID-19 enzymes. The carried-out studies indicated the similarity of 1 and 2 with TTT, the co-crystallized ligand of COVID-19 Papain-Like Protease (PLP), (PDB ID: 3E9S). Therefore, molecular docking studies of 1 and 2 against the PLP were carried out and revealed correct binding inside the active site exhibiting binding energies of −18.86 and −18.37 Kcal/mol, respectively. Further, in silico ADMET in addition to toxicity evaluation of 1 and 2 against seven models indicated the general safety and the likeness of 1 and 2 to be drugs. Lastly, to authenticate the binding and to investigate the thermodynamic characters, molecular dynamics (MD) simulation studies were conducted on 1 and PLP.

Phytochemical Profiling, In Vitro Biological Activities, and In Silico Molecular Docking Studies of Dracaena reflexa

Dracaena reflexa, a traditionally significant medicinal plant, has not been extensively explored before for its phytochemical and biological potential. The present study was conducted to evaluate the bioactive phytochemicals and in vitro biological activities of D. reflexa, and perform in silico molecular docking validation of D. reflexa. The bioactive phytochemicals were assessed by preliminary phytochemical testing, total bioactive contents, and GC-MS analysis. For biological evaluation, the antioxidant (DPPH, ABTS, CUPRAC, and ABTS), antibacterial, thrombolytic, and enzyme inhibition (tyrosinase and cholinesterase enzymes) potential were determined. The highest level of total phenolic contents (92.72 ± 0.79 mg GAE/g extract) was found in the n-butanol fraction while the maximum total flavonoid content (110 ± 0.83 mg QE/g extract) was observed in methanolic extract. The results showed that n-butanol fraction exhibited very significant tyrosinase inhibition activity (73.46 ± 0.80) and acetylcholinesterase inhibition activity (64.06 ± 2.65%) as compared to other fractions and comparable to the standard compounds (kojic acid and galantamine). The methanolic extract was considered to have moderate butyrylcholinesterase inhibition activity (50.97 ± 063) as compared to the standard compound galantamine (53.671 ± 0.97%). The GC-MS analysis of the n-hexane fraction resulted in the tentative identification of 120 bioactive phytochemicals. Furthermore, the major compounds as identified by GC-MS were analyzed using in silico molecular docking studies to determine the binding affinity between the ligands and the enzymes (tyrosinase, acetylcholinesterase, and butyrylcholinesterase enzymes). The results of this study suggest that Dracaena reflexa has unquestionable pharmaceutical importance and it should be further explored for the isolation of secondary metabolites that can be employed for the treatment of different diseases.

ER/AR Multi-Conformational Docking Server: A Tool for Discovering and Studying Estrogen and Androgen Receptor Modulators

The prediction of the estrogen receptor (ER) and androgen receptor (AR) activity of a compound is quite important to avoid the environmental exposures of endocrine-disrupting chemicals. The Estrogen and Androgen Receptor Database (EARDB, http://eardb.schanglab.org.cn/) provides a unique collection of reported ERα, ERβ, or AR protein structures and known small molecule modulators. With the user-uploaded query molecules, molecular docking based on multi-conformations of a single target will be performed. Moreover, the 2D similarity search against known modulators is also provided. Molecules predicted with a low binding energy or high similarity to known ERα, ERβ, or AR modulators may be potential endocrine-disrupting chemicals or new modulators. The server provides a tool to predict the endocrine activity for compounds of interests, benefiting for the ER and AR drug design and endocrine-disrupting chemical identification.

Targeting Cytotoxin-Associated Antigen A, a Virulent Factor of Helicobacter pylori-Associated Gastric Cancer: Structure-Based In Silico Screening of Natural Compounds

Gastric cancer is the fifth most frequent cancer and the third major cause of mortality worldwide. Helicobacter pylori, a bacterial infection linked with GC, injects the cytotoxin-associated antigen A (CagA; an oncoprotein) into host cells. When the phosphorylated CagA protein enters the cell, it attaches to other cellular components, interfering with normal cellular signaling pathways. CagA plays an important role in the progression of GC by interacting with phosphatidylserine of the host cell membrane. Therefore, disrupting the CagA-phosphatidylserine connection using small molecules appears to be a promising therapeutic approach. In this report, we screened the natural compounds from ZINC database against the CagA protein using the bioinformatics tools. Hits were initially chosen based on their physicochemical, absorption, distribution, metabolism, excretion, and toxicity (ADMET) characteristics, as well as other drug-like characteristics. To locate safe and effective hits, the PAINS filter, binding affinities estimation, and interaction analysis were used. Three compounds with high binding affinity and specificity for the CagA binding pocket were discovered. The final hits, ZINC153731, ZINC69482055, and ZINC164387, were found to bind strongly with CagA protein, with binding energies of -11.53, -10.67, and -9.21 kcal/mol, respectively, which were higher than that of the control compound (-7.25 kcal/mol). Further, based on binding affinity and interaction pattern, two leads (ZINC153731, ZINC69482055) were chosen for molecular dynamics (MD) simulation analysis. MD results showed that they displayed stability in their vicinity at 100 ns. This study suggested that these compounds could be used as possible inhibitors of CagA protein in the fight against GC. However, additional benchwork tests are required to validate them as CagA protein inhibitors.

Interaction of a conical carbon scaffold with the thio-substituted model of fluorouracil towards approaching the drug delivery purposes

A representative FeN4-doped conical carbon (C) scaffold was investigated for participating in interactions with the thio-substituted fluorouracil (SFU) anticancer drug by performing density functional theory (DFT) calculations. In this regard, all possible relaxation configurations of SFU at the doped tip of C scaffold were examined, in which three models were obtained including one horizontal relaxation configuration (FC1) and two vertical relaxation configurations (FC2 and FC3). The results indicate the highest stability and strength for FC1 model. Examining formations and strengths of interactions showed two medium strength interactions in each of FC1, FC2, and FC3 models. Moreover, the evaluated electronic molecular orbitals features indicated availability of sensor function for the proposed C scaffold towards the interacting SFU substance. As a consequence, the models were determined to work in dual functions of sensor and carrier towards drug delivery purpose of SFU anticancer drug.

3D-QSAR-based pharmacophore determination and design of novel DPP-4 inhibitors

Background/Aim: Therapy of diabetes mellitus type 2 includes drugs that act as inhibitors of dipeptidyl peptidase 4 (DPP-4) enzyme. Several DPP-4 inhibitors are marketed today and although they have favourable safety profile and tolerability, they show moderate activity in controlling glycaemia. The 3D quantitative structure-activity relationship (3D-QSAR) methodology was employed in order to find pharmacophore responsible for good DPP-4 inhibitory activity and designed new compounds with enhanced activity. Methods: For 3D-QSAR model development, 48 compounds structurally related to sitagliptin were collected from ChEMBL database. Structures of all compounds were optimised in order to find the best 3D conformations prior to QSAR modelling. To establish correlation between structure and biological activity Partial Least Squares (PLS) regression method integrated in Pentacle software was used. Results: Parameters of internal and external validation (R2 = 0.80, Q2 = 0.64 and R2 pred = 0.610) confirmed reliability of developed QSAR model. Analysis of obtained structural descriptors enabled identification of key structural characteristics that influenced DPP-4 inhibitory activity. Based on that information, new compounds were designed, of which 35 compounds had a better predicted activity, compared to sitagliptin. Conclusion: This QSAR model can be used for DPP-4 inhibitory activity prediction of structurally related compounds and resulting pharmacophore contains information useful for optimisation and design of new DPP-4 inhibitors. Finally, authors propose designed compounds for further synthesis, in vitro and in vivo testing, as new potential DPP-4 inhibitors.

Identification of tripeptides against tyrosine kinase domain of EGFR for lung cancer cell inhibition by in silico and in vitro studies

Epidermal growth factor receptor tyrosine kinase domain (EGFR-TK) has been one of the prominent targets for therapeutics of several human cancers, in particular non-small cell lung cancer. Although several small chemical compounds targeting EGFR-TK have been approved by FDA for treatment of such a cancer, the discovery of a new class of EGFR-TK inhibitors, for example, small peptides, is still desired. In this study, using molecular docking-based virtual screening, we selected five small peptides with high docking scores from eight thousand peptides as candidate compounds against EGFR-TK. Among five, the tripeptide WFF had the most potency to suppress the survival of non-small cell lung cancer cells but had the least toxicity to human liver cancer cells. Our in vitro kinase assays showed that WFF exhibited much lower inhibitory activity against purified EGFR-TK than the drug erlotinib (i.e., IC₅₀  values of ≈ 0.62 μM vs ≈ 7.57 nM, respectively). The relative free binding energies estimated from molecular dynamic simulations were consistent with the in vitro experiments in which the WFF bound had a lower affinity than erlotinib bound to EGFR-TK (i.e., ΔG_bind values of -20.3 kJ/mol vs ≈ -126.8 kJ/mol, respectively). In addition, the simulation analyses demonstrated the difference in EGFR binding preference between the drug and tripeptide in which erlotinib was stably bound in the ATP-binding pocket for 4-anilinoquinazoline class of inhibitors, while WFF moved out of that pocket to interact with polar amino acid residues on the αC-helix, activation loop, and substrate-binding region. Our findings suggest preferable interactions of the potential tripeptide on enzyme inhibition that are useful for further development of a new class of inhibitors targeting EGFR-TK.

GC-MS analysis of phytoconstituents from Ruellia prostrata and Senna tora and identification of potential anti-viral activity against SARS-CoV-2

SARS-CoV-2 is an etiologic agent responsible for the coronavirus disease 2019 (COVID-19) pandemic. The virus has rapidly extended globally and taken millions of lives due to the unavailability of therapeutics candidates against the virus. Till now, no specific drug candidates have been developed that can prevent or treat infections caused by the pathogen. The main protease (M^pro) of the SARS-CoV-2 plays a pivotal role in mediating viral replication and mechanistically inhibition of the protein can hinder the replication and infection process of the virus. Therefore, the study aimed to identify the natural bioactive compounds against the virus that can block the activity of the M^pro and subsequently block viral infections. Initially, a total of 96 phytochemicals from Ruellia prostrata Poir. and Senna tora (L.) Roxb. plants were identified through the gas chromatography-mass spectrometry (GC-MS) analytical method. Subsequently, the compounds were screened through molecular docking, absorption, distribution, metabolism, excretion (ADME), toxicity (T), and molecular dynamics (MD) simulation approach. The molecular docking method initially identified four molecules having a PubChem CID: 70825, CID: 25247358, CID: 54685836 and, CID: 1983 with a binding affinity ranging between −6.067 to −6.53 kcal mol⁻¹ to the active site of the target protein. All the selected compounds exhibit good pharmacokinetics and toxicity properties. Finally, the four compounds were further evaluated based on the MD simulation methods that confirmed the binding stability of the compounds to the targeted protein. The computational approaches identified the best four compounds CID: 70825, CID: 25247358, CID: 54685836 and, CID: 1983 that can be developed as a treatment option of SARS-CoV-2 disease-related complications. Although, experimental validation is suggested for further evaluation of the work.

Protease (M^pro) of SARS-CoV-2 has been identified as being able to hinder the replication process of the virus. Using GC-MS analytical methods, phytochemicals were identified from different medicinal plants that resulted in inhibitory activity of the molecules against M^pro.

Ligand-based G Protein Coupled Receptor pharmacophore modeling: Assessing the role of ligand function in model development

Integral membrane proteins in the G Protein-Coupled Receptor (GPCR) class are attractive drug development targets. However, computational methods applicable to ligand discovery for many GPCR targets are restricted by limited numbers of known ligands. Pharmacophore models can be developed using variously sized training sets and applied in database mining to prioritize candidate ligands for subsequent validation. This in silico study assessed the impact of key pharmacophore modeling decisions that arise when known ligand numbers for a target of interest are low. GPCR included in this study are the adrenergic alpha-1A, 1D and 2A, adrenergic beta 2 and 3, kappa, delta and mu opioid, serotonin 1A and 2A, and the muscarinic 1 and 2 receptors, all of which have rich ligand data sets suitable to assess the performance of protocols intended for application to GPCR with limited ligand data availability. Impact of ligand function, potency and structural diversity in training set selection was assessed to define when pharmacophore modeling targeting GPCR with limited known ligands becomes viable. Pharmacophore elements and pharmacophore model selection criteria were also assessed. Pharmacophore model assessment was based on percent pharmacophore model generation failure, as well as Güner-Henry enrichment and goodness-of-hit scores. Three of seven pharmacophore element schemes evaluated in MOE 2018.0101, Unified, PCHD, and CHD, showed substantially lower failure rates and higher enrichment scores than the others. Enrichment and GH scores were used to compare construction protocol for pharmacophore models of varying purposes- such as function specific versus nonspecific ligand identification. Notably, pharmacophore models constructed from ligands of mixed functions (agonists and antagonists) were capable of enriching hitlists with active compounds, and therefore can be used when available sets of known ligands are limited in number.

An in silico pipeline for the discovery of multitarget ligands: A case study for epi-polypharmacology based on DNMT1/HDAC2 inhibition

The search for novel therapeutic compounds remains an overwhelming task owing to the time-consuming and expensive nature of the drug development process and low success rates. Traditional methodologies that rely on the one drug-one target paradigm have proven insufficient for the treatment of multifactorial diseases, leading to a shift to multitarget approaches. In this emerging paradigm, molecules with off-target and promiscuous interactions may result in preferred therapies. In this study, we developed a general pipeline combining machine learning algorithms and a deep generator network to train a dual inhibitor classifier capable of identifying putative pharmacophoric traits. As a case study, we focused on dual inhibitors targeting DNA methyltransferase 1 (DNMT) and histone deacetylase 2 (HDAC2), two enzymes that play a central role in epigenetic regulation. We used this approach to identify dual inhibitors from a novel large natural product database in the public domain. We used docking and atomistic simulations as complementary approaches to establish the ligand-interaction profiles between the best hits and DNMT1/HDAC2. By using the combined ligand- and structure-based approaches, we discovered two promising novel scaffolds that can be used to simultaneously target both DNMT1 and HDAC2. We conclude that the flexibility and adaptability of the proposed pipeline has predictive capabilities of similar or derivative methods and is readily applicable to the discovery of small molecules targeting many other therapeutically relevant proteins.

Investigations on Anticancer and Antimalarial Activities of Indole-Sulfonamide Derivatives and In Silico Studies

A library of 44 indole-sulfonamide derivatives (1-44) were investigated for their cytotoxic activities against four cancer cell lines (i.e., HuCCA-1, HepG2, A549, and MOLT-3) and antimalarial effect. Most of the studied indoles exhibit anticancer activity against the MOLT-3 cell line, whereas only hydroxyl-containing bisindoles displayed anticancer activities against the other tested cancer cells as well as antimalarial effect. The most promising anticancer compounds were noted to be CF₃, Cl, and NO₂ derivatives of hydroxyl-bearing bisindoles (30, 31, and 36), while the most promising antimalarial compound was an OCH₃ derivative of non-hydroxyl-containing bisindole 11. Five quantitative structure-activity relationship (QSAR) models were successfully constructed, providing acceptable predictive performance (training set: R = 0.6186-0.9488, RMSE = 0.0938-0.2432; validation set: R = 0.4242-0.9252, RMSE = 0.1100-0.2785). QSAR modeling revealed that mass, charge, polarizability, van der Waals volume, and electronegativity are key properties governing activities of the compounds. QSAR models were further applied to guide the rational design of an additional set of 22 compounds (P1-P22) in which their activities were predicted. The prediction revealed a set of promising virtually constructed compounds (P1, P3, P9, P10, and P16) for further synthesis and development as anticancer and antimalarial agents. Molecular docking was also performed to reveal possible modes of bindings and interactions between the studied compounds and target proteins. Taken together, insightful structure-activity relationship information obtained herein would be beneficial for future screening, design, and structural optimization of the related compounds.

Cytotoxic and Antifungal Amides Derived from Ferulic Acid: Molecular Docking and Mechanism of Action

Amides derived from ferulic acid have a wide spectrum of pharmacological activities, including antitumor and antifungal activity. In the present study, a series of ten amides were obtained by coupling reactions using the reagents (benzotriazol-1-yloxy) tripyrrolidinophosphonium hexafluorophosphate (PyBOP) and N,N′-dicyclohexylcarbodiimide (DCC). All the compounds were identified on the basis of their IR, ¹H- and ¹³C-NMR, HRMS data, and with yields ranging from 43.17% to 91.37%. The compounds were subjected to cytotoxic tests by the alamar blue technique and antifungal screening by the broth microdilution method to determine the minimum inhibitory concentration (MIC). The amides 10 and 11 displayed the best result in both biological evaluations, and compound 10 was the most potent and selective in HL-60 cancer cells, with no cytotoxicity on healthy cells. This amide had antifungal activity in all strains and had the lowest MIC against Candida albicans and Candida tropicalis. The possible mechanism of antifungal action occurs via the fungal cell wall. Molecular modeling suggested that compounds 10 and 11 interact with the enzymes GWT1 and GSC1, which are essential for the development of C. albicans. The findings of the present study demonstrated that compounds 10 and 11 may be used as a platform in drug development in the future.

Photopharmacology of Ion Channels through the Light of the Computational Microscope

The optical control and investigation of neuronal activity can be achieved and carried out with photoswitchable ligands. Such compounds are designed in a modular fashion, combining a known ligand of the target protein and a photochromic group, as well as an additional electrophilic group for tethered ligands. Such a design strategy can be optimized by including structural data. In addition to experimental structures, computational methods (such as homology modeling, molecular docking, molecular dynamics and enhanced sampling techniques) can provide structural insights to guide photoswitch design and to understand the observed light-regulated effects. This review discusses the application of such structure-based computational methods to photoswitchable ligands targeting voltage- and ligand-gated ion channels. Structural mapping may help identify residues near the ligand binding pocket amenable for mutagenesis and covalent attachment. Modeling of the target protein in a complex with the photoswitchable ligand can shed light on the different activities of the two photoswitch isomers and the effect of site-directed mutations on photoswitch binding, as well as ion channel subtype selectivity. The examples presented here show how the integration of computational modeling with experimental data can greatly facilitate photoswitchable ligand design and optimization. Recent advances in structural biology, both experimental and computational, are expected to further strengthen this rational photopharmacology approach.