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

Novel hits for autosomal dominated polycystic kidney disease (ADPKD) targeting derived by in silico screening on ZINC-15 natural product database

Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic kidney disorder that leads to growth cysts in the kidney, ultimately resulting in loss of function. Currently, no effective drug therapy can be safely used in the clinic. So, looking for effective therapeutic drugs is urgent for treating ADPKD. Our natural product library was prepared based on the ZINC-15 database. Lipinski's rule of five, drug-likeness, and toxicity screening of the designed library were evaluated. Swiss model online server was used for modeling of GANAB target. Finally, docking-based screening against ADPKD targets was done by MOE 2019 software. The top 14 favorable druglike and non-toxic hits were selected for docking studies. Our results showed that compound-10 (ZINC 6073947) as a sesquiterpene coumarin had more negative binding interaction into the active site of PPARG, OXSR1, GANAB, AVPR2, and PC2 with docking scores of -8.22, -7.52, -6.98, -6.61 and -6.05 kcal/mol, respectively, in comparison to Curcumin, as a natural product that is now in phase 4 clinical trial in ADPKD disease, with an affinity of -8.03, -6.42, -6.82, -5.84 and -5.10 kcal/mol, respectively. Furthermore, seven sesquiterpene coumarins similar to compound 10 were generated and docked. Farnesiferol B (16), compared to compound-10, showed binding affinity of -8.16, -6.4, -7.46, -6.92, and -6.11 kcal/mol against the above targets, respectively. Molecular dynamics, which was done on the compound-10 and 16 (Farnesiferol B) in complex with PPARG, GANAB, and AVPR2, showed more negative binding free-energy than Pioglitazone, Miglitol, and Tolvaptan as FDA-approved drugs for each target, respectively.Communicated by Ramaswamy H. Sarma.

Drug Repositioning Using Computer-aided Drug Design (CADD)

Drug repositioning is a method of using authorized drugs for other unusually complex diseases. Compared to new drug development, this method is fast, low in cost, and effective. Through the use of outstanding bioinformatics tools, such as computer-aided drug design (CADD), computer strategies play a vital role in the re-transformation of drugs. The use of CADD's special strategy for target-based drug reuse is the most promising method, and its realization rate is high. In this review article, we have particularly focused on understanding the various technologies of CADD and the use of computer-aided drug design for target-based drug reuse, taking COVID-19 and cancer as examples. Finally, it is concluded that CADD technology is accelerating the development of repurposed drugs due to its many advantages, and there are many facts to prove that the new ligand-targeting strategy is a beneficial method and that it will gain momentum with the development of technology.

Decision tree-based identification of important molecular fragments for protein-ligand binding

Fragment-based drug design is an emerging technology in pharmaceutical research and development. One of the key aspects of this technology is the identification and quantitative characterization of molecular fragments. This study presents a strategy for identifying important molecular fragments based on molecular fingerprints and decision tree algorithms and verifies its feasibility in predicting protein-ligand binding affinity. Specifically, the three-dimensional (3D) structures of protein-ligand complexes are encoded using extended-connectivity fingerprints (ECFP), and three decision tree models, namely Random Forest, XGBoost, and LightGBM, are used to quantitatively characterize the feature importance, thereby extracting important molecular fragments with high reliability. Few-shot learning reveals that the extracted molecular fragments contribute significantly and consistently to the binding affinity even with a small sample size. Despite the absence of location and distance information for molecular fragments in ECFP, 3D visualization, in combination with the reverse ECFP process, shows that the majority of the extracted fragments are located at the binding interface of the protein and the ligand. This alignment with the distance constraints critical for binding affinity further supports the reliability of the strategy for identifying important molecular fragments.

Sequential virtual screening collaborated with machine-learning strategies for the discovery of precise medicine against non-small cell lung cancer

Dysregulation of MAPK pathway receptors are crucial in causing uncontrolled cell proliferation in many cancer types including non-small cell lung cancer. Due to the complications in targeting the upstream components, MEK is an appealing target to diminish this pathway activity. Hence, we have aimed to discover potent MEK inhibitors by integrating virtual screening and machine learning-based strategies. Preliminary screening was conducted on 11,808 compounds using the cavity-based pharmacophore model AADDRRR. Further, seven ML models were accessed to predict the MEK active compounds using six molecular representations. The LGB model with morgan2 fingerprints surpasses other models ensuing 0.92 accuracy and 0.83 MCC value versus test set and 0.85 accuracy and 0.70 MCC value with external set. Further, the binding ability of screened hits were examined using glide XP docking and prime-MM/GBSA calculations. Note that we have utilized three ML-based scoring functions to predict the various biological properties of the compounds. The two hit compounds such as DB06920 and DB08010 resulted excellent binding mechanism with acceptable toxicity properties against MEK. Further, 200 ns of MD simulation combined with MM-GBSA/PBSA calculations confirms that DB06920 may have stable binding conformations with MEK thus step forwarded to the experimental studies in the near future.Communicated by Ramaswamy H. Sarma.

The Discovery of Novel Agents against Staphylococcus aureus by Targeting Sortase A: A Combination of Virtual Screening and Experimental Validation

Staphylococcus aureus (S. aureus), commonly known as "superbugs", is a highly pathogenic bacterium that poses a serious threat to human health. There is an urgent need to replace traditional antibiotics with novel drugs to combat S. aureus. Sortase A (SrtA) is a crucial transpeptidase involved in the adhesion process of S. aureus. The reduction in virulence and prevention of S. aureus infections have made it a significant target for antimicrobial drugs. In this study, we combined virtual screening with experimental validation to identify potential drug candidates from a drug library. Three hits, referred to as Naldemedine, Telmisartan, and Azilsartan, were identified based on docking binding energy and the ratio of occupied functional sites of SrtA. The stability analysis manifests that Naldemedine and Telmisartan have a higher binding affinity to the hydrophobic pockets. Specifically, Telmisartan forms stable hydrogen bonds with SrtA, resulting in the highest binding energy. Our experiments prove that the efficiency of adhesion and invasion by S. aureus can be decreased without significantly affecting bacterial growth. Our work identifies Telmisartan as the most promising candidate for inhibiting SrtA, which can help combat S. aureus infection.

Multi-and many-objective optimization: present and future in de novo drug design

de novo Drug Design (dnDD) aims to create new molecules that satisfy multiple conflicting objectives. Since several desired properties can be considered in the optimization process, dnDD is naturally categorized as a many-objective optimization problem (ManyOOP), where more than three objectives must be simultaneously optimized. However, a large number of objectives typically pose several challenges that affect the choice and the design of optimization methodologies. Herein, we cover the application of multi- and many-objective optimization methods, particularly those based on Evolutionary Computation and Machine Learning techniques, to enlighten their potential application in dnDD. Additionally, we comprehensively analyze how molecular properties used in the optimization process are applied as either objectives or constraints to the problem. Finally, we discuss future research in many-objective optimization for dnDD, highlighting two important possible impacts: i) its integration with the development of multi-target approaches to accelerate the discovery of innovative and more efficacious drug therapies and ii) its role as a catalyst for new developments in more fundamental and general methodological frameworks in the field.

Active peptides with hypoglycemic effect obtained from hemp (Cannabis sativa L) protein through identification, molecular docking, and virtual screening

Hemp (Cannabis sativa L) seeds are rich in proteins of high nutritional value, which makes the study of beneficial properties of hemp seed proteins and peptides, such as hypotensive and hypoglycemic effects, increasingly attractive. The present results confirm the good processability and stability of the hemp protein hydrolysate obtained by enzymatic hydrolysis of non-dehulled hemp seed meal (NDHM). Six peptides with potential hypoglycemic activity were obtained by ethanol-graded precipitation, Nano LC-Q-Orbitrap-MS/MS mass spectrometry, and computerized virtual screening. Further, validation experiments for in vitro synthesis showed that TGLGR, SPVI, FY, and FR exhibited good α-glucosidase inhibitory activity, respectively. Animal experiments showed that the hemp protein peptides modulated blood glucose and blood lipids in hyperglycemic rats. These results indicate that hemp protein peptides can reduce blood glucose levels in hyperglycemic rats, suggesting that hemp proteins may be a promising natural source for the prevention and treatment of hyperglycemia.

Utilization of AlphaFold models for drug discovery: Feasibility and challenges. Histone deacetylase 11 as a case study

Histone deacetylase 11 (HDAC11), an enzyme that cleaves acyl groups from acylated lysine residues, is the sole member of class IV of HDAC family with no reported crystal structure so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms which complicates the conventional template-based homology modeling. AlphaFold is a neural network machine learning approach for predicting the 3D structures of proteins with atomic accuracy even in absence of similar structures. However, the structures predicted by AlphaFold are missing small molecules as ligands and cofactors. In our study, we first optimized the HDAC11 AlphaFold model by adding the catalytic zinc ion followed by assessment of the usability of the model by docking of the selective inhibitor FT895. Minimization of the optimized model in presence of transplanted inhibitors, which have been described as HDAC11 inhibitors, was performed. Four complexes were generated and proved to be stable using three replicas of 50 ns MD simulations and were successfully utilized for docking of the selective inhibitors FT895, MIR002 and SIS17. For SIS17, The most reasonable pose was selected based on structural comparison between HDAC6, HDAC8 and the HDAC11 optimized AlphaFold model. The manually optimized HDAC11 model is thus able to explain the binding behavior of known HDAC11 inhibitors and can be used for further structure-based optimization.

Insight into the Hantaan virus RNA-dependent RNA polymerase inhibition using in-silico approaches

The Hantaan virus (HTN) is a member of the hantaviridae family. It is a segmented type, negative-strand virus (sNSVs). It causes hemorrhagic fever with renal syndrome, which includes fever, vascular hemorrhage, and renal failure. This illness is one of the most serious hemorrhagic diseases in the world, and it is a major public health concern due to its high mortality rate. The Hantaan virus RNA-dependent RNA polymerase complex (RdRp) is involved in viral RNA transcription and replication for the survival and transmission of this virus. Therefore, it is a primary target for antiviral drug development. Interference with the endonucleolytic "cap-snatching" reaction by the HTN virus RdRp endonuclease domain is a particularly appealing approach for drug discovery against this virus. This RdRp endonuclease domain of the HTN virus has a metal-dependent catalytic activity. We targeted this metal-dependent enzymatic activity to identify inhibitors that can bind and disrupt this endonuclease enzyme activity using in-silico approaches i.e., molecular docking, molecular dynamics simulation, predicted absorption, distribution, metabolism, excretion, toxicity (ADMET) and drug-likeness studies. The docking studies showed that peramivir, and ingavirin compounds can effectively bind with the manganese ions and engage with other active site residues of this protein. Molecular simulations also showed stable binding of these ligands with the active site of HTN RdRp. Simulation analysis showed that they were in constant contact with the active site manganese ions and amino acid residues of the HTN virus endonuclease domain. This study will help in better understanding the HTN and related viruses.

Comparison of Equilibrium and Nonequilibrium Approaches for Relative Binding Free Energy Predictions

Alchemical relative binding free energy calculations have recently found important applications in drug optimization. A series of congeneric compounds are generated from a preidentified lead compound, and their relative binding affinities to a protein are assessed in order to optimize candidate drugs. While methods based on equilibrium thermodynamics have been extensively studied, an approach based on nonequilibrium methods has recently been reported together with claims of its superiority. However, these claims pay insufficient attention to the basis and reliability of both methods. Here we report a comparative study of the two approaches across a large data set, comprising more than 500 ligand transformations spanning in excess of 300 ligands binding to a set of 14 diverse protein targets. Ensemble methods are essential to quantify the uncertainty in these calculations, not only for the reasons already established in the equilibrium approach but also to ensure that the nonequilibrium calculations reside within their domain of validity. If and only if ensemble methods are applied, we find that the nonequilibrium method can achieve accuracy and precision comparable to those of the equilibrium approach. Compared to the equilibrium method, the nonequilibrium approach can reduce computational costs but introduces higher computational complexity and longer wall clock times. There are, however, cases where the standard length of a nonequilibrium transition is not sufficient, necessitating a complete rerun of the entire set of transitions. This significantly increases the computational cost and proves to be highly inconvenient during large-scale applications. Our findings provide a key set of recommendations that should be adopted for the reliable implementation of nonequilibrium approaches to relative binding free energy calculations in ligand-protein systems.

Computer-aided drug design for virtual-screening and active-predicting of main protease (Mpro) inhibitors against SARS-CoV-2

Introduction: SARS-CoV-2 is a novel coronavirus with highly contagious and has posed a significant threat to global public health. The main protease (Mpro) is a promising target for antiviral drugs against SARS-CoV-2. Methods: In this study, we have used pharmacophore-based drug design technology to identify potential compounds from drug databases as Mpro inhibitors. Results: The procedure involves pharmacophore modeling, validation, and pharmacophore-based virtual screening, which identifies 257 compounds with promising inhibitory activity. Discussion: Molecular docking and non-bonding interactions between the targeted protein Mpro and compounds showed that ENA482732 was the best compound. These results provided a theoretical foundation for future studies of Mpro inhibitors against SARS-CoV-2.

Pharmacophore-Based Screening, Molecular Docking, and Dynamic Simulation of Fungal Metabolites as Inhibitors of Multi-Targets in Neurodegenerative Disorders

Neurodegenerative disorders, such as Alzheimer's disease (AD), negatively affect the economic and psychological system. For AD, there is still a lack of disease-altering treatments and promising cures due to its complex pathophysiology. In this study, we computationally screened the natural database of fungal metabolites against three known therapeutic target proteins of AD. Initially, a pharmacophore-based, drug-likeness category was employed for screening, and it filtered the 14 (A-N) best hits out of 17,544 fungal metabolites. The 14 best hits were docked individually against GSK-3β, the NMDA receptor, and BACE-1 to investigate the potential of finding a multitarget inhibitor. We found that compounds B, F, and L were immuno-toxic, whereas E, H, I, and J had a higher LD50 dose (5000 mg/kg). Among the examined metabolites, the Bisacremine-C (compound I) was found to be the most active molecule against GSK-3β (ΔG: -8.7 ± 0.2 Kcal/mol, Ki: 2.4 × 106 M-1), NMDA (ΔG: -9.5 ± 0.1 Kcal/mol, Ki: 9.2 × 106 M-1), and BACE-1 (ΔG: -9.1 ± 0.2 Kcal/mol, Ki: 4.7 × 106 M-1). It showed a 25-fold higher affinity with GSK-3β, 6.3-fold higher affinity with NMDA, and 9.04-fold higher affinity with BACE-1 than their native ligands, respectively. Molecular dynamic simulation parameters, such as RMSD, RMSF, Rg, and SASA, all confirmed that the overall structures of the targeted enzymes did not change significantly after binding with Bisacremine-C, and the ligand remained inside the binding cavity in a stable conformation for most of the simulation time. The most significant hydrophobic contacts for the GSK-3β-Bisacremine-C complex are with ILE62, VAL70, ALA83, and LEU188, whereas GLN185 is significant for H-bonds. In terms of hydrophobic contacts, TYR184 and PHE246 are the most important, while SER180 is vital for H-bonds in NMDA-Bisacremine-C. THR232 is the most crucial for H-bonds in BACE-1-Bisacremine-C and ILE110-produced hydrophobic contacts. This study laid a foundation for further experimental validation and clinical trials regarding the biopotency of Bisacremine-C.

DeepSA: a deep-learning driven predictor of compound synthesis accessibility

With the continuous development of artificial intelligence technology, more and more computational models for generating new molecules are being developed. However, we are often confronted with the question of whether these compounds are easy or difficult to synthesize, which refers to synthetic accessibility of compounds. In this study, a deep learning based computational model called DeepSA, was proposed to predict the synthesis accessibility of compounds, which provides a useful tool to choose molecules. DeepSA is a chemical language model that was developed by training on a dataset of 3,593,053 molecules using various natural language processing (NLP) algorithms, offering advantages over state-of-the-art methods and having a much higher area under the receiver operating characteristic curve (AUROC), i.e., 89.6%, in discriminating those molecules that are difficult to synthesize. This helps users select less expensive molecules for synthesis, reducing the time and cost required for drug discovery and development. Interestingly, a comparison of DeepSA with a Graph Attention-based method shows that using SMILES alone can also efficiently visualize and extract compound's informative features. DeepSA is available online on the below web server ( https://bailab.siais.shanghaitech.edu.cn/services/deepsa/ ) of our group, and the code is available at https://github.com/Shihang-Wang-58/DeepSA .

Applying molecular docking to pesticides

Pesticide creation is related to the development of sustainable agricultural and ecological safety, and molecular docking technology can effectively help in pesticide innovation. This paper introduces the basic theory behind molecular docking, pesticide databases, and docking software. It also summarizes the application of molecular docking in the pesticide field, including the virtual screening of lead compounds, detection of pesticides and their metabolites in the environment, reverse screening of pesticide targets, and the study of resistance mechanisms. Finally, problems with the use of molecular docking technology in pesticide creation are discussed, and prospects for the future use of molecular docking technology in new pesticide development are discussed. © 2023 Society of Chemical Industry.

Virtual Insights into Natural Compounds as Potential 5α-Reductase Type II Inhibitors: A Structure-Based Screening and Molecular Dynamics Simulation Study

Androgenic alopecia (AGA) is a dermatological disease with psychosocial consequences for those who experience hair loss. AGA is linked to an increase in androgen levels caused by an excess of dihydrotestosterone in blood capillaries produced from testosterone by 5α-reductase type II (5αR2), which is expressed in scalp hair follicles; 5αR2 activity and dihydrotestosterone levels are elevated in balding scalps. The diverse health benefits of flavonoids have been widely reported in epidemiological studies, and research interest continues to increase. In this study, a virtual screening approach was used to identify compounds that interact with active site residues of 5αR2 by screening a library containing 241 flavonoid compounds. Here, we report two potent flavonoid compounds, eriocitrin and silymarin, that interacted strongly with 5αR2, with binding energies of -12.1 and -11.7 kcal/mol, respectively, which were more significant than those of the control, finasteride (-11.2 kcal/mol). Molecular dynamic simulations (200 ns) were used to optimize the interactions between compounds and 5αR2 and revealed that the interaction of eriocitrin and silymarin with 5αR2 was stable. The study shows that eriocitrin and silymarin provide developmental bases for novel 5αR2 inhibitors for the management of AGA.

A comprehension on structure guided alignment dependent 3D-QSAR modelling, and molecular dynamics simulation on 2,4-thiazolidinediones as aldose reductase inhibitors for the management of diabetic complications

Aldose reductase is an oxo-reductase enzyme belonging to the aldo-keto reductase class. Compounds having thiazolidine-2,4-dione scaffold are reported as potential aldose reductase inhibitors for diabetic complications. The present work uses structure-guided alignment-dependent Gaussian field- and atom-based 3D-QSAR on a dataset of 84 molecules. 3D-QSAR studies on two sets of dataset alignment have been carried out to understand the favourable and unfavourable structural features influencing the affinity of these inhibitors towards the enzyme. Using common pharmacophore hypotheses, the five-point pharmacophores for aldose reductase favourable features were generated. The molecular dynamics simulations (up to 100 ns) were performed for the potent molecule from each alignment set (compounds 24 and 65) compared to reference standard tolrestat and epalrestat to study target-ligand complexes' binding energy and stability. Compound 65 was most stable with better interactions in the aldose reductase binding pocket than tolrestat. The MM-PBSA study suggests compound 65 possessed better binding energy than reference standard tolrestat, i.e. -87.437 ± 19.728 and -73.424 ± 12.502 kJ/mol, respectively. The generated 3D-QSAR models provide information about structure-activity relationships and ligand-target binding energy. Target-specific stability data from MD simulation would be helpful for rational compound design with better aldose reductase activity.Communicated by Ramaswamy H. Sarma.

DataPype: A Fully Automated Unified Software Platform for Computer-Aided Drug Design

With the advent of computer-aided drug design (CADD), traditional physical testing of thousands of molecules has now been replaced by target-focused drug discovery, where potentially bioactive molecules are predicted by computer software before their physical synthesis. However, despite being a significant breakthrough, CADD still faces various limitations and challenges. The increasing availability of data on small molecules has created a need to streamline the sourcing of data from different databases and automate the processing and cleaning of data into a form that can be used by multiple CADD software applications. Several standalone software packages are available to aid the drug designer, each with its own specific application, requiring specialized knowledge and expertise for optimal use. These applications require their own input and output files, making it a challenge for nonexpert users or multidisciplinary discovery teams. Here, we have developed a new software platform called DataPype, which wraps around these different software packages. It provides a unified automated workflow to search for hit compounds using specialist software. Additionally, multiple virtual screening packages can be used in the one workflow, and if different ways of looking at potential hit compounds all predict the same set of molecules, we have higher confidence that we should make or purchase and test the molecules. Importantly, DataPype can run on computer servers, speeding up the virtual screening for new compounds. Combining access to multiple CADD tools within one interface will enhance the early stage of drug discovery, increase usability, and enable the use of parallel computing.

CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks

We present a novel and interpretable approach for predicting small-molecule binding affinities using context explanation networks (CENs). Given the specific structure of a protein/ligand complex, our CENsible scoring function uses a deep convolutional neural network to predict the contributions of pre-calculated terms to the overall binding affinity. We show that CENsible can effectively distinguish active vs. inactive compounds for many systems. Its primary benefit over related machine-learning scoring functions, however, is that it retains interpretability, allowing researchers to identify the contribution of each pre-calculated term to the final affinity prediction, with implications for subsequent lead optimization.

Decoding molecular mechanism of species-selective targeting of fungal versus human HSP90 using multiple replica molecular dynamics simulations and binding free energy calculations

As a highly evolutionarily conserved molecular chaperone, heat shock protein (HSP90), plays an important role in virulence traits, representing a therapeutic target for the treatment of fungal infections. The close evolutionary relationship between fungi and their human hosts poses a key challenge for the development of selective antifungal agents. In this work, molecular docking, multiple replica microsecond-based molecular dynamics (MD) simulations, and binding free energy calculations were performed to decode molecular mechanism of species-selective targeting of fungal versus human HSP90 triggered by the compound A11. MD simulations reveal that binding of compound A11 to human HSP90 nucleotide-binding domain (NBD) leads to obvious conformational changes relative to fungal HSP90 NBD. Binding free energy calculations show that the binding of compound A11 to fungal HSP90 NBD is stronger than that to human HSP90 NBD. Per residue-based free energy decomposition analysis was used to evaluate the inhibitor - residue interaction profile. The results efficiently identify the hot spot residues that play vital roles in favorable binding of compound A11 to fungal HSP90 NBD. This study is expected to provide a useful guidance for the development of selective inhibitors toward fungal HSP90.Communicated by Ramaswamy H. Sarma.

Anti-Viral Activity of Bioactive Molecules of Silymarin against COVID-19 via In Silico Studies

The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection drove the global coronavirus disease 2019 (COVID-19) pandemic, causing a huge loss of human life and a negative impact on economic development. It is an urgent necessity to explore potential drugs against viruses, such as SARS-CoV-2. Silymarin, a mixture of herb-derived polyphenolic flavonoids extracted from the milk thistle, possesses potent antioxidative, anti-apoptotic, and anti-inflammatory properties. Accumulating research studies have demonstrated the killing activity of silymarin against viruses, such as dengue virus, chikungunya virus, and hepatitis C virus. However, the anti-COVID-19 mechanisms of silymarin remain unclear. In this study, multiple disciplinary approaches and methodologies were applied to evaluate the potential mechanisms of silymarin as an anti-viral agent against SARS-CoV-2 infection. In silico approaches such as molecular docking, network pharmacology, and bioinformatic methods were incorporated to assess the ligand-protein binding properties and analyze the protein-protein interaction network. The DAVID database was used to analyze gene functions, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment. TCMSP and GeneCards were used to identify drug target genes and COVID-19-related genes. Our results revealed that silymarin compounds, such as silybin A/B and silymonin, displayed triplicate functions against SARS-CoV-2 infection, including directly binding with human angiotensin-converting enzyme 2 (ACE2) to inhibit SARS-CoV-2 entry into the host cells, directly binding with viral proteins RdRp and helicase to inhibit viral replication and proliferation, and regulating host immune response to indirectly inhibit viral infection. Specifically, the targets of silymarin molecules in immune regulation were screened out, such as proinflammatory cytokines TNF and IL-6 and cell growth factors VEGFA and EGF. In addition, the molecular mechanism of drug-target protein interaction was investigated, including the binding pockets of drug molecules in human ACE2 and viral proteins, the formation of hydrogen bonds, hydrophobic interactions, and other drug-protein ligand interactions. Finally, the drug-likeness results of candidate molecules passed the criteria for drug screening. Overall, this study demonstrates the molecular mechanism of silymarin molecules against SARS-CoV-2 infection.

Exploring bixin from Bixa orellana L. seeds: quantification and in silico insights into its anti-cancer potential

Bixin, the key pigment of Bixa orellana L., is an apo-carotenoid found in the seed arils. The present study aimed to quantitatively determine the bixin content of seeds and explore its anti-cancer activity through in silico studies. The bixin content from the seeds of the local genotype, TNMTP8, quantified by RP-HPLC was 4.58 mg per gram. The prediction of pharmacological activity suggested that bixin may serve as a BRAF, MMP9, TNF expression inhibitors, and TP53 expression enhancer. According to molecular docking analysis, bixin interacted with eight different skin cancer targets and had the lowest binding energy compared to the standard drug, 5-fluorouracil. The binding score between bixin and the targets ranged from -4.7 to -8.7 kcal/mol. The targets BRAF and SIRT3 interacted well with bixin, with binding energies as low as -8.3 and -8.7 kcal/mol, respectively. Hence, the dynamic behavior of these two docked complexes throughout a 500 ns trajectory run was investigated further. The Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF) values, and total contacts as a function of time recorded during scrutiny suggest that both complexes were stable. This was validated by post-molecular dynamics analysis using Molecular Mechanics Generalized Born Surface Area (MM-GBSA). Principal component analysis (PCA) was used to analyze the significant differences in motion exhibited by BRAF-Bixin and SIRT3-Bixin. The results showed that bixin is a promising source for potential treatment interventions in skin cancer therapies.Communicated by Ramaswamy H. Sarma.

On the Path to Optimal Alchemistry

Alchemical free energy calculations have become a standard and widely used tool, in particular for calculating and comparing binding affinities of drugs. Although methods to compute such free energies have improved significantly over the last decades, the choice of path between the end states of interest is usually still the same as two decades ago. We will show that there is a fundamentally arbitrary, implicit choice of parametrization of this path. To address this, the notion of the length of a path or a metric is required. A metric recently introduced in the context of the accelerated weight histogram method also proves to be very useful here. We demonstrate that this metric can not only improve the efficiency of sampling along a given path, but that it can also be used to improve the actual choice of path. For a set of relevant use cases, the combination of these improvements can increase the efficiency of alchemical free energy calculations by up to a factor 16.

Navigating the Chemical Space and Chemical Multiverse of a Unified Latin American Natural Product Database: LANaPDB

The number of databases of natural products (NPs) has increased substantially. Latin America is extraordinarily rich in biodiversity, enabling the identification of novel NPs, which has encouraged both the development of databases and the implementation of those that are being created or are under development. In a collective effort from several Latin American countries, herein we introduce the first version of the Latin American Natural Products Database (LANaPDB), a public compound collection that gathers the chemical information of NPs contained in diverse databases from this geographical region. The current version of LANaPDB unifies the information from six countries and contains 12,959 chemical structures. The structural classification showed that the most abundant compounds are the terpenoids (63.2%), phenylpropanoids (18%) and alkaloids (11.8%). From the analysis of the distribution of properties of pharmaceutical interest, it was observed that many LANaPDB compounds satisfy some drug-like rules of thumb for physicochemical properties. The concept of the chemical multiverse was employed to generate multiple chemical spaces from two different fingerprints and two dimensionality reduction techniques. Comparing LANaPDB with FDA-approved drugs and the major open-access repository of NPs, COCONUT, it was concluded that the chemical space covered by LANaPDB completely overlaps with COCONUT and, in some regions, with FDA-approved drugs. LANaPDB will be updated, adding more compounds from each database, plus the addition of databases from other Latin American countries.

Insight into the Binding of First- and Second-Generation PET Tracers to 4R and 3R/4R Tau Protofibrils

Primary supranuclear palsy (PSP) is a rare neurodegenerative disease that perturbs body movement, eye movement, and walking balance. Similar to Alzheimer's disease (AD), the abnormal aggregation of tau fibrils in the central neuronal and glial cells is a major hallmark of PSP disease. In this study, we use multiple approaches, including docking, molecular dynamics, and metadynamics simulations, to investigate the binding mechanism of 10 first- and second-generations of PET tracers for PSP tau and compare their binding in cortical basal degeneration (CBD) and AD tauopathies. Structure-activity relationships, binding preferences, the nature of ligand binding in terms of basic intermolecular interactions, the role of polar/charged residues, induced-fit mechanisms, grove closures, and folding patterns for the binding of these tracers in PSP, CBD, and AD tau fibrils are evaluated and discussed in detail in order to build a holistic picture of what is essential for the binding and also to rank the potency of the different tracers. For example, we found that the same tracer shows different binding preferences for the surface sites of tau fibrils that are intrinsically distinct in the folding patterns. Results from the metadynamics simulations predict that PMPBB3 and PBB3 exhibit the strongest binding free energies onto the Q276[I277]I278, Q351[S352]K353, and N368[K369]K370 sites of PSP than the other explored tracers, indicating a solid preference for vdW and cation-π interactions. Our results also reproduced known preferences of tracers, namely, that MK6240 binds better to AD tau than CBD tau and PSP tau and that CBD2115, PI2620, and PMPBB3 are 4R tau binders. These findings fill in the well-sought-after knowledge gap in terms of these tracers' potential binding mechanisms and will be important for the design of highly selective novel PET tracers for tauopathies.

TransFoxMol: predicting molecular property with focused attention

Predicting the biological properties of molecules is crucial in computer-aided drug development, yet it's often impeded by data scarcity and imbalance in many practical applications. Existing approaches are based on self-supervised learning or 3D data and using an increasing number of parameters to improve performance. These approaches may not take full advantage of established chemical knowledge and could inadvertently introduce noise into the respective model. In this study, we introduce a more elegant transformer-based framework with focused attention for molecular representation (TransFoxMol) to improve the understanding of artificial intelligence (AI) of molecular structure property relationships. TransFoxMol incorporates a multi-scale 2D molecular environment into a graph neural network + Transformer module and uses prior chemical maps to obtain a more focused attention landscape compared to that obtained using existing approaches. Experimental results show that TransFoxMol achieves state-of-the-art performance on MoleculeNet benchmarks and surpasses the performance of baselines that use self-supervised learning or geometry-enhanced strategies on small-scale datasets. Subsequent analyses indicate that TransFoxMol's predictions are highly interpretable and the clever use of chemical knowledge enables AI to perceive molecules in a simple but rational way, enhancing performance.

Transcriptomics-Guided In Silico Drug Repurposing: Identifying New Candidates with Dual-Stage Antiplasmodial Activity

In tropical and subtropical areas, malaria stands as a profound public health challenge, causing an estimated 247 million cases worldwide annually. Given the absence of a viable vaccine, the timely and effective treatment of malaria remains a critical priority. However, the growing resistance of parasites to currently utilized drugs underscores the critical need for the identification of new antimalarial therapies. Here, we aimed to identify potential new drug candidates against Plasmodium falciparum, the main causative agent of malaria, by analyzing the transcriptomes of different life stages of the parasite and identifying highly expressed genes. We searched for genes that were expressed in all stages of the parasite's life cycle, including the asexual blood stage, gametocyte stage, liver stage, and sexual stages in the insect vector, using transcriptomics data from publicly available databases. From this analysis, we found 674 overlapping genes, including 409 essential ones. By searching through drug target databases, we discovered 70 potential drug targets and 75 associated bioactive compounds. We sought to expand this analysis to similar compounds to known drugs. So, we found a list of 1557 similar compounds, which we predicted as actives and inactives using previously developed machine learning models against five life stages of Plasmodium spp. From this analysis, two compounds were selected, and the reactions were experimentally evaluated. The compounds HSP-990 and silvestrol aglycone showed potent inhibitory activity at nanomolar concentrations against the P. falciparum 3D7 strain asexual blood stage. Moreover, silvestrol aglycone exhibited low cytotoxicity in mammalian cells, transmission-blocking potential, and inhibitory activity comparable to those of established antimalarials. These findings warrant further investigation of silvestrol aglycone as a potential dual-acting antimalarial and transmission-blocking candidate for malaria control.

Vismodegib Identified as a Novel COX-2 Inhibitor via Deep-Learning-Based Drug Repositioning and Molecular Docking Analysis

Artificial intelligence algorithms have been increasingly applied in drug development due to their efficiency and effectiveness. Deep-learning-based drug repurposing can contribute to the identification of novel therapeutic applications for drugs with other indications. The current study used a trained deep-learning model to screen an FDA-approved drug library for novel COX-2 inhibitors. Reference COX-2 data sets, composed of active and decoy compounds, were obtained from the DUD-E database. To extract molecular features, compounds were subjected to RDKit, a cheminformatic toolkit. GraphConvMol, a graph convolutional network model from DeepChem, was applied to obtain a predictive model from the DUD-E data sets. Then, the COX-2 inhibitory potential of the FDA-approved drugs was predicted using the trained deep-learning model. Vismodegib, an anticancer agent that inhibits the hedgehog signaling pathway by binding to smoothened, was predicted to inhibit COX-2. Noticeably, some compounds that exhibit high potential from the prediction were known to be COX-2 inhibitors, indicating the prediction model's liability. To confirm the COX-2 inhibition activity of vismodegib, molecular docking was carried out with the reference compounds of the COX-2 inhibitor, celecoxib, and ibuprofen. Furthermore, the experimental examination of COX-2 inhibition was also carried out using a cell culture study. Results showed that vismodegib exhibited a highly comparable COX-2 inhibitory activity compared to celecoxib and ibuprofen. In conclusion, the deep-learning model can efficiently improve the virtual screening of drugs, and vismodegib can be used as a novel COX-2 inhibitor.

Development of Masitinib Derivatives with Enhanced Mpro Ligand Efficiency and Reduced Cytotoxicity

Recently, a high-throughput screen of 1900 clinically used drugs identified masitinib, an orally bioavailable tyrosine kinase inhibitor, as a potential treatment for COVID-19. Masitinib acts as a broad-spectrum inhibitor for human coronaviruses, including SARS-CoV-2 and several of its variants. In this work, we rely on atomistic molecular dynamics simulations with advanced sampling methods to develop a deeper understanding of masitinib's mechanism of Mpro inhibition. To improve the inhibitory efficiency and to increase the ligand selectivity for the viral target, we determined the minimal portion of the molecule (fragment) that is responsible for most of the interactions that arise within the masitinib-Mpro complex. We found that masitinib forms highly stable and specific H-bond interactions with Mpro through its pyridine and aminothiazole rings. Importantly, the interaction with His163 is a key anchoring point of the inhibitor, and its perturbation leads to ligand unbinding within nanoseconds. Based on these observations, a small library of rationally designed masitinib derivatives (M1-M5) was proposed. Our results show increased inhibitory efficiency and highly reduced cytotoxicity for the M3 and M4 derivatives compared to masitinib.

Unveiling Neuroprotective Potential of Spice Plant-Derived Compounds against Alzheimer's Disease: Insights from Computational Studies

Alzheimer's disease (AD) is a serious threat to the global health care system and is brought on by a series of factors that cause neuronal dysfunction and impairment in memory and cognitive decline. This study investigated the therapeutic potential of phytochemicals that belong to the ten regularly used spice plants, based on their binding affinity with AD-associated proteins. Comprehensive docking studies were performed using AutoDock Vina in PyRx followed by molecular dynamic (MD) simulations using AMBER 14. The docking study of the chosen molecules revealed the binding energies of their interactions with the target proteins, while MD simulations were carried out to verify the steadiness of bound complexes. Through the Lipinski filter and admetSAR analysis, the chosen compounds' pharmacokinetic characteristics and drug likeness were also examined. The pharmacophore mapping study was also done and analyzed for best selected molecules. Additionally, principal component analysis (PCA) was used to examine how the general motion of the protein changed. The results showed quercetin and myricetin to be potential inhibitors of AChE and alpha-amyrin and beta-chlorogenin to be potential inhibitors of BuChE, exhibiting best binding energies comparable to those of donepezil, used as a positive control. The multiple descriptors from the simulation study, root mean square deviation (RMSD), root mean square fluctuation (RMSF), hydrogen bond, radius of gyration (Rg), and solvent-accessible surface areas (SASA), confirm the stable nature of the protein-ligand complexes. Molecular mechanic Poisson-Boltzmann surface area (MM-PBSA) binding free energy calculations indicated the energetically favorable binding of the ligands to the protein. Finally, according to pharmacokinetic properties and drug likeness, characteristics showed that quercetin and myricetin for AChE and alpha-amyrin and beta-chlorogenin for BuChE were found to be the most effective agents for treating the AD.

Bridging the Gap: Can COVID-19 Research Help Combat African Swine Fever?

African swine fever (ASF) is a highly contagious and economically devastating disease affecting domestic pigs and wild boar, caused by African swine fever virus (ASFV). Despite being harmless to humans, ASF poses significant challenges to the swine industry, due to sudden losses and trade restrictions. The ongoing COVID-19 pandemic has spurred an unparalleled global research effort, yielding remarkable advancements across scientific disciplines. In this review, we explore the potential technological spillover from COVID-19 research into ASF. Specifically, we assess the applicability of the diagnostic tools, vaccine development strategies, and biosecurity measures developed for COVID-19 for combating ASF. Additionally, we discuss the lessons learned from the pandemic in terms of surveillance systems and their implications for managing ASF. By bridging the gap between COVID-19 and ASF research, we highlight the potential for interdisciplinary collaboration and technological spillovers in the battle against ASF.

An Updated Review on Developing Small Molecule Kinase Inhibitors Using Computer-Aided Drug Design Approaches

Small molecule kinase inhibitors (SMKIs) are of heightened interest in the field of drug research and development. There are 79 (as of July 2023) small molecule kinase inhibitors that have been approved by the FDA and hundreds of kinase inhibitor candidates in clinical trials that have shed light on the treatment of some major diseases. As an important strategy in drug design, computer-aided drug design (CADD) plays an indispensable role in the discovery of SMKIs. CADD methods such as docking, molecular dynamic, quantum mechanics/molecular mechanics, pharmacophore, virtual screening, and quantitative structure-activity relationship have been applied to the design and optimization of small molecule kinase inhibitors. In this review, we provide an overview of recent advances in CADD and SMKIs and the application of CADD in the discovery of SMKIs.

Pharmacoinformatics studies of coenzyme Q10 and potassium polyacrylate on angiotensin-converting enzyme associated with hypertension

Coenzyme Q10's (CoQ10) favorable impact on cardiovascular diseases risk factors like hypertension and atherosclerosis is linked to the antioxidant action of CoQ10 in these conditions. This study showed the possible effects of CoQ10, potassium polyacrylate (PCK), and valsartan, a reference drug, on the angiotensin-converting enzyme (ACE), a crucial component of the renin-angiotensin system. The Glide tool on Maestro 11.1 was used to calculate the respective binding affinity and binding energy of these compounds towards ACE. The Schrödinger suite was used to run molecular dynamic simulations for 100 ns. The pkCSM tool was used to forecast the pharmacokinetic characteristics and toxicological effects. The SwissADME server was used to estimate the drug-like properties of these compounds. Based on their corresponding scoring values and the negative values of the binding free energies, molecular docking analysis of CoQ10 and PCK revealed that both exhibited favorable binding affinities towards the ACE, with CoQ10 having the highest binding scores. The results showed that both CoQ10 and PCK and the reference drug, valsartan, have some amino acids in common (at the pocket site of ACE) as the key residues for binding to ACE. Both CoQ10 and PCK demonstrated drug-like qualities and were not harmful, according to the predicted pharmacokinetics and toxicology studies. The results of this study suggest that because of its inhibitory interactions with ACE, CoQ10 in particular could be useful in regulating and reducing hypertension.Communicated by Ramaswamy H. Sarma.

Insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment: A protocol for systematic review

There is still unmet medical need in cancer treatment mainly due to drug resistance and adverse drug events. Therefore, the search for better drugs is essential. Computer-aided drug design (CADD) and discovery tools are useful to streamline the lengthy and costly drug development process. Anthraquinones are a group of naturally occurring compounds with unique scaffold that exert various biological properties including anticancer activities. This protocol describes a systematic review that provide insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment. It was prepared in accordance with the "Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 guidelines, and published in the "International prospective register of systematic reviews" database (PROSPERO: CRD42023432904). Search strategies will be developed based on the combination of relevant keywords and executed in PubMed, Scopus, Web of Science and MedRxiv. Only original studies that employed CADD as primary tool in virtual screening for the purpose of designing or discovering anti-cancer drugs involving anthraquinone scaffold published in English language will be included. Two independent reviewers will be involved to screen and select the papers, extract the data and assess the risk of bias. Apart from exploring the trends and types of CADD methods used, the target proteins of these compounds in cancer treatment will also be revealed in this review. It is believed that the outcome of this study could be utilized to support the ongoing research in similar area with better quality and greater probability of success, consequently optimizing the resources in subsequent in vitro, in vivo, non-clinical and clinical development. It will also serve as an evidence based scientific guide for new research to design novel anthraquinone-derived drug with improved efficacy and safety profile for cancer treatment.

In silico identification of potential protein kinase C alpha inhibitors from phytochemicals from IMPPAT database for anticancer therapeutics: a virtual screening approach

Protein Kinase C alpha (PKCα) is a critical signaling molecule that plays a crucial role in various physiological processes, including cell growth, differentiation, and survival. Over the years, there has been a growing interest in targeting PKCα as a promising drug target for the treatment of various diseases, including cancer. Targeting PKCα can, therefore, serve as a potential strategy to prevent cancer progression and enhance the efficacy of conventional anticancer therapies. We conducted a systematic search for promising compounds for their anticancer potential that target PKCα using natural compounds from the IMPPAT database. The initial compounds were screened through various tests, including analysis of their physical and chemical properties, PAINS filter, ADMET analysis, PASS analysis, and specific interaction analysis. We selected those that showed high binding affinity and specificity to PKCα from the screened compounds, and we further analyzed them using molecular dynamics simulations (MDS) and principal component analysis (PCA). Various systematic parameters from the MDS analyses suggested that the protein-ligand complexes were stabilized throughout the simulation trajectories of 100 nanoseconds (ns). Our findings indicated that compounds Nicandrenone and Withaphysalin D bind to PKCα with high stability and affinity, making them potential candidates for further research in cancer therapeutics innovation in clinical contexts.Communicated by Ramaswamy H. Sarma.

Evaluation of the Effects of Heteroaryl Ethylene Molecules in Combination with Antibiotics: A Preliminary Study on Control Strains

The discovery of compounds with antibacterial activity is crucial in the ongoing battle against antibiotic resistance. We developed two QSAR models to design six novel heteroaryl drug candidates and assessed their antibacterial properties against nine ATCC strains, including Enterococcus faecalis, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and also Salmonella enterica and Escherichia coli, many of which belong to the ESKAPE group. We combined PB4, a previously tested compound from published studies, with GC-VI-70, a newly discovered compound, with the best cytotoxicity/MIC profile. By testing sub-MIC concentrations of PB4 with five antibiotics (linezolid, gentamycin, ampicillin, erythromycin, rifampin, and imipenem), we evaluated the combination's efficacy against the ATCC strains. To assess the compounds' cytotoxicity, we conducted a 24 h and 48 h 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay on colorectal adenocarcinoma (CaCo-2) cells. We tested the antibiotics alone and in combination with PB4. Encouragingly, PB4 reduced the MIC values for GC-VI-70 and for the various clinically used antibiotics. However, it is essential to note that all the compounds studied in this research exhibited cytotoxic activity against cells. These findings highlight the potential of using these compounds in combination with antibiotics to enhance their effectiveness at lower concentrations while minimizing cytotoxic effects.

Identification of potent anti-immunogenic agents through virtual screening, 3D-QSAR studies, and in vitro experiments

A wealth of literature has highlighted the discovery of various immune modulators, frequently used in clinical practice, yet associated with numerous drawbacks. In light of this pharmacological deficiency, medical scientists are motivated to develop new immune modulators with minimized adverse effects yet retaining the improved therapeutic potential. T-cell differentiation and growth are central to human defense and are regulated by interleukin-2 (IL-2), an immune-modulatory cytokine. However, scientific investigation is hindered due to its flat binding site and widespread hotspot residues. In this regard, a prompt and logical investigation guided by integrated computational techniques was undertaken to unravel new and potential leads against IL-2. In particular, the combination of score-based and pharmacophore-based virtual screening approaches were employed, reducing the data from millions of small molecules to a manageable number. Subsequent docking and 3D-QSAR prediction via CoMFA further helped remove false positives from the data. The reliability of the model was assessed via standard metrics, which explain the model's fitness and the robustness of the model in predicting the activity of new compounds. The extensive virtual screening herein led to the identification of a total of 24 leads with potential anti-IL-2 activity. Furthermore, the theoretical findings were corroborated with in vitro testing, further endorsing the anti-inflammatory potential of the identified leads.

A generalized protein-ligand scoring framework with balanced scoring, docking, ranking and screening powers

Applying machine learning algorithms to protein-ligand scoring functions has aroused widespread attention in recent years due to the high predictive accuracy and affordable computational cost. Nevertheless, most machine learning-based scoring functions are only applicable to a specific task, e.g., binding affinity prediction, binding pose prediction or virtual screening, suggesting that the development of a scoring function with balanced performance in all critical tasks remains a grand challenge. To this end, we propose a novel parameterization strategy by introducing an adjustable binding affinity term that represents the correlation between the predicted outcomes and experimental data into the training of mixture density network. The resulting residue-atom distance likelihood potential not only retains the superior docking and screening power over all the other state-of-the-art approaches, but also achieves a remarkable improvement in scoring and ranking performance. We emphatically explore the impacts of several key elements on prediction accuracy as well as the task preference, and demonstrate that the performance of scoring/ranking and docking/screening tasks of a certain model could be well balanced through an appropriate manner. Overall, our study highlights the potential utility of our innovative parameterization strategy as well as the resulting scoring framework in future structure-based drug design.

A perspective on the sharing of docking data

Computational approaches are nowadays largely applied in drug discovery projects. Among these, molecular docking is the most used for hit identification against a drug target protein. However, many scientists in the field shed light on the lack of availability and reproducibility of the data obtained from such studies to the whole community. Consequently, sustaining and developing the efforts toward a large and fully transparent sharing of those data could be beneficial for all researchers in drug discovery. The purpose of this article is first to propose guidelines and recommendations on the appropriate way to conduct virtual screening experiments and second to depict the current state of sharing molecular docking data. In conclusion, we have explored and proposed several prospects to enhance data sharing from docking experiment that could be developed in the foreseeable future.

Antioxidant Chimeric Molecules: Are Chemical Motifs Additive? The Case of a Selenium-Based Ligand

We set up an in silico experiment and designed a chimeric compound integrating molecular features from different efficient ROS (Reactive Oxygen Species) scavengers, with the purpose of investigating potential relationships between molecular structure and antioxidant activity. Furthermore, a selenium centre was inserted due to its known capacity to reduce hydroperoxides, acting as a molecular mimic of glutathione peroxidase; finally, since this organoselenide is a precursor of a N-heterocyclic carbene ligand, its Au(I) carbene complex was designed and examined. A validated protocol based on DFT (Density Functional Theory) was employed to investigate the radical scavenging activity of available sites on the organoselenide precursor ((SMD)-M06-2X/6-311+G(d,p)//M06-2X/6-31G(d)), as well as on the organometallic complex ((SMD)-M06-2X/SDD (Au), 6-311+G(d,p)//ZORA-BLYP-D3(BJ)/TZ2P), considering HAT (Hydrogen Atom Transfer) and RAF (Radical Adduct Formation) regarding five different radicals. The results of this case study suggest that the antioxidant potential of chemical motifs should not be considered as an additive property when designing a chimeric compound, but rather that the relevance of a molecular topology is derived from a chemical motif combined with an opportune chemical space of the molecule. Thus, the direct contributions of single functional groups which are generally thought of as antioxidants per se do not guarantee the efficient radical scavenging potential of a molecular species.

Elucidating shared biomarkers and pathways in kidney stones and diabetes: insights into novel therapeutic targets and the role of resveratrol

BACKGROUND

The pathogenic mechanisms shared between kidney stones and diabetes at the transcriptional level remain elusive, and the molecular mechanisms by which resveratrol exerts its protective effects against these conditions require further investigation.

METHODS

To address these gaps in knowledge, we conducted a comprehensive analysis of microarray and RNA-seq datasets to elucidate shared biomarkers and biological pathways involved in the pathogenesis of kidney stones and diabetes. An assortment of bioinformatic approaches was employed to illuminate the common molecular markers and associated pathways, thereby contributing to the identification of innovative therapeutic targets. Further investigation into the molecular mechanisms of resveratrol in preventing these conditions was conducted using molecular docking simulation and first-principles calculations.

RESULTS

The study identified 11 potential target genes associated with kidney stones and diabetes through the intersection of genes from weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) screening. Among these, Interleukin 11 (IL11) emerged as a pivotal hub gene and a potential diagnostic biomarker for both conditions, particularly in males. Expression analysis of IL11 demonstrated elevated levels in kidney stones and diabetes groups compared to controls. Additionally, IL11 exhibited correlations with specific cell types and differential expression in normal and pathological conditions. Gene set enrichment analysis (GSEA) highlighted significant disparities in biological processes, pathways, and immune signatures associated with IL11. Moreover, molecular docking simulation of resveratrol towards IL11 and a first-principles investigation of Ca adsorption on the resveratrol surface provided structural evidence for the development of resveratrol-based drugs for these conditions.

CONCLUSIONS

Overall, this investigation illuminates the discovery of common molecular mechanisms underlying kidney stones and diabetes, unveils potential diagnostic biomarkers, and elucidates the significance of IL11 in these conditions. It also provides insights into IL11 as a promising therapeutic target and highlights the role of resveratrol. Nonetheless, further research is warranted to enhance our understanding of IL11 targeting mechanisms and address any limitations in the study.

Investigation of Methyl-5-(pentan-3-yloxy)-7-oxabicyclo[4.1.0]hept-3-ene-3-carboxyhydrazide Derivatives as Potential Inhibitors of COVID-19 Main Protease: DFT and Molecular Docking Study

The search for effective therapeutics to combat COVID-19 has led to the exploration of the biological activity of numerous compounds. In this study, hydrazones derived from oseltamivir intermediate, methyl 5-(pentan-3-yloxy)-7-oxabicyclo[4.1.0]hept-3-ene-3-carboxylate have been investigated for their potential as drug candidates against the COVID-19 virus using computational methods, including density functional theory (DFT) studies, molecular docking, and absorption, distribution, metabolism, excretion and toxicity (ADMET) analysis. The DFT studies provide information on the electronic properties of the compounds while the molecular docking results using AutoDock reported the binding energies between the main protease of COVID-19 and the compounds. The DFT results revealed that the energy gap of the compounds ranged from 4.32 to 5.82 eV while compound HC had the highest energy gap (5.82 eV) and chemical potential (2.90 eV). The electrophilicity index values of the 11 compounds ranged from 2.49 to 3.86, thus they were classified as strong electrophiles. The molecular electrostatic potential (MESP) revealed electron-rich and electron-deficient regions of the compounds. The docking results reveal that all the compounds had better docking scores than remdesivir and chloroquine, frontline drugs employed in combating COVID-19, with HC having the best docking score of -6.5. The results were visualized using Discovery studio, which revealed hydrogen bonding, pi-alkyl interaction, alkyl interaction, salt bridge interaction, halogen interaction as being responsible for the docking scores. The drug-likeness results showed that the compounds qualify as oral drug candidates as none of them violated Vebers and Lipinski's rule. Thus, they could serve as potential inhibitors of COVID-19.

Contrastive learning in protein language space predicts interactions between drugs and protein targets

Sequence-based prediction of drug-target interactions has the potential to accelerate drug discovery by complementing experimental screens. Such computational prediction needs to be generalizable and scalable while remaining sensitive to subtle variations in the inputs. However, current computational techniques fail to simultaneously meet these goals, often sacrificing performance of one to achieve the others. We develop a deep learning model, ConPLex, successfully leveraging the advances in pretrained protein language models ("PLex") and employing a protein-anchored contrastive coembedding ("Con") to outperform state-of-the-art approaches. ConPLex achieves high accuracy, broad adaptivity to unseen data, and specificity against decoy compounds. It makes predictions of binding based on the distance between learned representations, enabling predictions at the scale of massive compound libraries and the human proteome. Experimental testing of 19 kinase-drug interaction predictions validated 12 interactions, including four with subnanomolar affinity, plus a strongly binding EPHB1 inhibitor (KD = 1.3 nM). Furthermore, ConPLex embeddings are interpretable, which enables us to visualize the drug-target embedding space and use embeddings to characterize the function of human cell-surface proteins. We anticipate that ConPLex will facilitate efficient drug discovery by making highly sensitive in silico drug screening feasible at the genome scale. ConPLex is available open source at https://ConPLex.csail.mit.edu.

Computer-aided drug design approaches applied to screen natural product's structural analogs targeting arginase in Leishmania spp

Introduction: Leishmaniasis is a disease with high mortality rates and approximately 1.5 million new cases each year. Despite the new approaches and advances to fight the disease, there are no effective therapies. Methods: Hence, this study aims to screen for natural products' structural analogs as new drug candidates against leishmaniasis. We applied Computer-aided drug design (CADD) approaches, such as virtual screening, molecular docking, molecular dynamics simulation, molecular mechanics-generalized Born surface area (MM-GBSA) binding free estimation, and free energy perturbation (FEP) aiming to select structural analogs from natural products that have shown anti-leishmanial and anti-arginase activities and that could bind selectively against the Leishmania arginase enzyme. Results: The compounds 2H-1-benzopyran, 3,4-dihydro-2-(2-methylphenyl)-(9CI), echioidinin, and malvidin showed good results against arginase targets from three parasite species and negative results for potential toxicities. The echioidinin and malvidin ligands generated interactions in the active center at pH 2.0 conditions by MM-GBSA and FEP methods. Conclusions: This work suggests the potential anti-leishmanial activity of the compounds and thus can be further in vitro and in vivo experimentally validated.

PeruNPDB: the Peruvian Natural Products Database for in silico drug screening

Since the number of drugs based on natural products (NPs) represents a large source of novel pharmacological entities, NPs have acquired significance in drug discovery. Peru is considered a megadiverse country with many endemic species of plants, terrestrial, and marine animals, and microorganisms. NPs databases have a major impact on drug discovery development. For this reason, several countries such as Mexico, Brazil, India, and China have initiatives to assemble and maintain NPs databases that are representative of their diversity and ethnopharmacological usage. We describe the assembly, curation, and chemoinformatic evaluation of the content and coverage in chemical space, as well as the physicochemical attributes and chemical diversity of the initial version of the Peruvian Natural Products Database (PeruNPDB), which contains 280 natural products. Access to PeruNPDB is available for free ( https://perunpdb.com.pe/ ). The PeruNPDB's collection is intended to be used in a variety of tasks, such as virtual screening campaigns against various disease targets or biological endpoints. This emphasizes the significance of biodiversity protection both directly and indirectly on human health.

Synthesis and Investigation of Anti-Inflammatory Activity of New Thiourea Derivatives of Naproxen

The aim of the study was a synthesis and investigation of the dose-dependent anti-inflammatory effect of new thiourea derivatives of naproxen with selected aromatic amines and esters of aromatic amino acids. The results of the in vivo study indicate that derivatives of m-anisidine (4) and N-methyl tryptophan methyl ester (7) showed the most potent anti-inflammatory activity four hours after injection of carrageenan, with the percentage of inhibition of 54.01% and 54.12%, respectively. In vitro assays of COX-2 inhibition demonstrated that none of the tested compounds achieved 50% inhibition at concentrations lower than 100 µM. On the other hand, the aromatic amine derivatives (1-5) accomplished significant inhibition of 5-LOX, and the lowest IC₅₀ value was observed for compound 4 (0.30 μM). High anti-edematous activity of compound 4 in the rat paw edema model, together with potent inhibition of 5-LOX, highlight this compound as a promising anti-inflammatory agent.

Multitargeted Virtual Screening and Molecular Simulation of Natural Product-like Compounds against GSK3β, NMDA-Receptor, and BACE-1 for the Management of Alzheimer's Disease

The complexity of Alzheimer's disease (AD) and several side effects of currently available medication inclined us to search for a novel natural cure by targeting multiple key regulatory proteins. We initially virtually screened the natural product-like compounds against GSK3β, NMDA receptor, and BACE-1 and thereafter validated the best hit through molecular dynamics simulation (MDS). The results demonstrated that out of 2029 compounds, only 51 compounds exhibited better binding interactions than native ligands, with all three protein targets (NMDA, GSK3β, and BACE) considered multitarget inhibitors. Among them, F1094-0201 is the most potent inhibitor against multiple targets with binding energy -11.7, -10.6, and -12 kcal/mol, respectively. ADME-T analysis results showed that F1094-0201 was found to be suitable for CNS drug-likeness in addition to their other drug-likeness properties. The MDS results of RMSD, RMSF, Rg, SASA, SSE and residue interactions indicated the formation of a strong and stable association in the complex of ligands (F1094-0201) and proteins. These findings confirm the F1094-0201's ability to remain inside target proteins' binding pockets while forming a stable complex of protein-ligand. The free energies (MM/GBSA) of BACE-F1094-0201, GSK3β-F1094-0201, and NMDA-F1094-0201 complex formation were -73.78 ± 4.31 kcal mol^-1, -72.77 ± 3.43 kcal mol^-1, and -52.51 ± 2.85 kcal mol^-1, respectively. Amongst the target proteins, F1094-0201 have a more stable association with BACE, followed by NMDA and GSK3β. These attributes of F1094-0201 indicate it as a possible option for the management of pathophysiological pathways associated with AD.

A QSAR Study for Antileishmanial 2-Phenyl-2,3-dihydrobenzofurans †

Leishmaniasis, a parasitic disease that represents a threat to the life of millions of people around the globe, is currently lacking effective treatments. We have previously reported on the antileishmanial activity of a series of synthetic 2-phenyl-2,3-dihydrobenzofurans and some qualitative structure-activity relationships within this set of neolignan analogues. Therefore, in the present study, various quantitative structure-activity relationship (QSAR) models were created to explain and predict the antileishmanial activity of these compounds. Comparing the performance of QSAR models based on molecular descriptors and multiple linear regression, random forest, and support vector regression with models based on 3D molecular structures and their interaction fields (MIFs) with partial least squares regression, it turned out that the latter (i.e., 3D-QSAR models) were clearly superior to the former. MIF analysis for the best-performing and statistically most robust 3D-QSAR model revealed the most important structural features required for antileishmanial activity. Thus, this model can guide decision-making during further development by predicting the activity of potentially new leishmanicidal dihydrobenzofurans before synthesis.

Ringtail: A Python Tool for Efficient Management and Storage of Virtual Screening Results

Virtual screening using molecular docking is now routinely used for the rapid evaluation of very large ligand libraries in early stage drug discovery. As the size of compound libraries which can feasibly be screened grows, so do the challenges in result management and storage. Here we introduce Ringtail, a new Python tool in the AutoDock Suite for efficient storage and analysis of virtual screening data based on portable SQLite databases. Ringtail is designed to work with AutoDock-GPU and AutoDock Vina out-of-the-box. Its modular design also allows for easy extension to support input file types from other docking software, different storage solutions, and incorporation into other applications. Ringtail's SQLite database output can dramatically reduce the required disk storage (36-46 fold) by selecting individual poses to store and by taking advantage of the relational database format. Filtering times are also dramatically reduced, requiring minutes to filter millions of ligands. Thus, Ringtail is a tool that can immediately integrate into existing virtual screening pipelines using AutoDock-GPU and Vina, and is scriptable and modifiable to fit specific user needs.

Computational approaches streamlining drug discovery

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.