Computer-aided Drug Design Research Articles

A Deep Dive into PDE5 Inhibition: Innovative Discoveries via Virtual Screening

Background: PDE5 inhibitors have had a surge in popularity over the last decade owing to their efficacy in the treatment of erectile dysfunction, coronary vasculopathy, and pulmonary arterial hypertension. These inhibitors exhibit competitive binding with phosphodiesterase type 5 and inhibit the hydrolysis of cyclic guanosine monophosphate, hence elevating the levels of cGMP in smooth muscle cells and prolonging the duration of an erection. However, due to production costs and side effects, further research is needed to discover new PDE5 inhibitors. Objective: The study aimed to identify potent PDE5 inhibitors by employing the extensive application of computer-aided drug design. Methods: Three different databases, named Million Molecules Database, Natural Product Database, and NCI Database, have been screened, which has been followed by filtering based on various druglikeness rules, docking, ADME, toxicity, consensus molecular docking, and 100 ns molecular dynamics simulation. Results: Three compounds (ZINC05351336, ZINC12030898, and ZINC17949426) have exhibited stable-binding characteristics at the active site of PDE5, demonstrating a robust binding affinity. These molecules have been found to possess drug-like capabilities, effective ADME features, low toxicity, and high stability. Conclusion: The study has delved into the realm of PDE5 inhibitors, which have been found to be effective in treating erectile dysfunction, but high production costs and side effects necessitate new ones. Through computer-aided drug design and screening, three compounds have been identified with promising binding characteristics, drug-appropriate properties, effective ADME profiles, minimal toxicity, and stability, making them potential candidates for future PDE5 inhibitors

Revolutionizing Antiviral Therapeutics: In silico Approaches for Emerging and Neglected RNA Viruses.

The 21st century has shown us how rapidly the pandemic can evolve and devastate the life of human beings without differentiating between the continents. Even after the global investment of billions of dollars into the healthcare sector, we are still lacking multiple therapeutics against emerging viruses. World Health Organization (WHO) has listed a number of viruses that could take the form of pandemics at anytime, depending upon their mutations. Among those listed, the SARS-CoV, Ebola, Zika, Nipah, and Chikungunya virus (CHIKV) are the most known viruses in terms of their number of outbreaks. The common feature among these viruses is their RNA-based genome. Developing a new therapeutic candidate for these RNA viruses in a short period of time is challenging. In silico drug designing techniques offer a simple solution to these problems by implementing supercomputers and complicated algorithms that can evaluate the inhibition activity of proposed synthetic compounds without actually doing the bioassays. A vast collection of protein crystal structures and the data on binding affinity are useful tools in this process. Taking this into account, we have summarized the in silico based therapeutic advances against SARS-CoV, Ebola, Zika, Nipah, and CHIKV viruses by encapsulating state-of-art research articles into different sections. Specifically, we have shown that computer- aided drug design (CADD) derived synthetic molecules are the pillars of upcoming therapeutic strategies against emerging and neglected viruses.

The Potential Roles of Ficus carica Extract in the Management of COVID-19 Viral Infections: A Computer-aided Drug Design Study.

The conventional processes of drug discovery are too expensive, timeconsuming and the success rate is limited. Searching for alternatives that have evident safety and potential efficacy could save money, time and improve the current therapeutic regimen outcomes. Clinical phytotherapy implies the use of extracts of natural origin for prophylaxis, treatment, or management of human disorders. In this work, the potential role of common Fig (Ficus carica) in the management of COVID-19 infections has been explored. The antiviral effects of Cyanidin 3-rhamnoglucoside which is abundant in common Figs have been illustrated on COVID-19 targets. The immunomodulatory effect and the ability to ameliorate the cytokine storm associated with coronavirus infections have also been highlighted. This work involves various computational studies to investigate the potential roles of common figs in the management of COVID-19 viral infections. Two molecular docking studies of all active ingredients in common Figs were conducted starting with MOE to provide initial insights, followed by Autodock Vina for further confirmation of the results of the top five compounds with the best docking score. Finally, Molecular dynamic simulation alongside MMPBSA calculations were conducted using GROMACS to endorse and validate the entire work.

Discovery of α-amylase and α-glucosidase dual inhibitors from NPASS database for management of Type 2 Diabetes Mellitus: A chemoinformatic approach.

Postprandial hyperglycemia, typical manifestation of Type 2 Diabetes Mellitus (T2DM), is associated with notable global morbidity and mortality. Preventing the advancement of this condition by delaying the rate of glucose absorption through inhibition of α-amylase and α-glucosidase enzymatic activities is of utmost importance. Finding a safe antidiabetic drug is essential since those that are currently on the market have drawbacks like unpleasant side effects. The current study utilized computer-aided drug design (CADD), as a quick and affordable method to find a substitute drug template that can be used to control postprandial hyperglycemia by modulating the activity of α-amylase and α-glucosidase enzymes. The Natural Products Activity and Species database (NPASS) (30,926 compounds) was screened in silico, with a focus on evaluating drug-likeness, toxicity profiles and ability to bind on a target protein. Two molecules NPC204580 (Chrotacumine C) and NPC137813 (1-O-(2-Methoxy-4-Acetylphenyl)-6-O-(E-Cinnamoyl)-Beta-D-Glucopyranoside) were identified as potential dual inhibitors for α-amylase and α-glucosidase with free binding energies of -14.46 kcal/mol and -12.58 kcal/mol for α-amylase, and -8.42 kcal/mol and -8.76 kcal/mol for α-glucosidase, respectively. The molecules showed ionic, H-bonding and hydrophobic interactions with critical amino acid residues of both enzymes. Moreover, 100 ns molecular dynamic simulations showed that both molecules are stable on the receptors' active sites based on root mean square deviation (RMSD), root mean square fluctuation (RMSF), and the Generalized Born surface area (GBSA) energy calculated. The two compounds are thus promising therapeutic agents for T2DM that merit further investigation due to their excellent binding energies, encouraging pharmacokinetics, toxicity profiles, and stability as demonstrated in simulated studies.

Host-Guest Binding Free Energies à la Carte: An Automated OneOPES Protocol.

Estimating absolute binding free energies from molecular simulations is a key step in computer-aided drug design pipelines, but the agreement between computational results and experiments is still very inconsistent. Both the accuracy of the computational model and the quality of the statistical sampling contribute to this discrepancy, yet disentangling the two remains a challenge. In this study, we present an automated protocol based on OneOPES, an enhanced sampling method that exploits replica exchange and can accelerate several collective variables to address the sampling problem. We apply this protocol to 37 host-guest systems. The simplicity of setting up the simulations and producing well-converged binding free energy estimates without the need to optimize simulation parameters provides a reliable solution to the sampling problem. This, in turn, allows for a systematic force field comparison and ranking according to the correlation between simulations and experiments, which can inform the selection of an appropriate model. The protocol can be readily adapted to test more force field combinations and study more complex protein-ligand systems, where the choice of an appropriate physical model is often based on heuristic considerations rather than systematic optimization.

Insilico discovery of novel Phosphodiesterase 4 (PDE4) inhibitors for the treatment of psoriasis: Insights from computer aided drug design approaches.

Psoriasis is chronic immune-mediated inflammatory disorder characterized by various comorbidities, erythematous plaques with silvery scale which can lead to psoriatic arthritis. The phosphodiesterase 4 (PDE4) protein is a potential drug target to control Psoriasis. In the current study, pharmacophore-based virtual screening of Diversity library of ChemDiv database was first performed, and then the screened hits were docked to the active site of PDE4 to choose the best binding modes. Forty-six hits generated during the virtual screening were prepared and docked to the PDE4 receptor by SP docking module of glide. The binding affinities of the selected hits were calculated by molecular docking and based on the affinities, ten hits were selected for the bioactivity scores prediction and ADMET analysis. Based on the ADMET profiling, four hits D356-2630, C700-2058, G842-0420 and F403-0203 were processed to MD simulations for stability analysis. The outcomes showed that these compounds showed strong binding with proteins with better binding free energies. Based on the results of our study, we proposed that these hits can function as lead in the biological assays and in vitro studies are required to develop the novel drug candidates.

Investigation of Novel Quinoline Derivatives Targeting Epidermal Growth Factor Receptors as Anticancer Agents a Computational Approach

Background: Newer chemical entities are created and synthesis has been made feasible by a variety of computer-aided drug design (CAAD) techniques. In addition to facilitating the visualisation of the ligand-target binding process, the application of in silico methodologies and structure-based drug design (SBDD) allows for the prediction of receptor affinities and significant binding pocket locations. Objective: The goal of the current study was to identify new quinoline derivatives by computational methods specially designed to bind the EGFR receptor in the treatment of breast cancer. Materials and Methods: ChemAxon Marvin Sketch 5.11.5 was used to create derivatives of quinolines. The admetSAR online web tools and SwissADME were utilised to forecast the toxicity and pharmacokinetic characteristics of several substances. A multitude of software programmes, such as Autodock 1.1.2, MGL Tools 1.5.6, Procheck, Protparam ExPasy tool, PyMOL, and Biovia Discovery Studio Visualizer v20.1.0.19295 were also employed to ascertain the ligand-receptor interactions between quinoline derivatives and the target receptor (PDB -5GNK). Result: Almost all components were shown to be less hazardous, orally consumable and to have the appropriate pharmacokinetic characteristics based on in silico study. All newly generated derivative compounds have higher docking scores when compared to the widely used medication sorafenib. Conclusion: Interactions with quinoline analogues boost binding energy and the number of Hbonds produced, making them a suitable place to start when creating compounds for further exploration. The quinoline moiety increases its potential as a novel therapy alternative for breast cancer and could facilitate more comprehensive in vivo, in vitro, chemical-based, and pharma studies by medicinal chemists.

Latin American Natural Product Database (LANaPDB): An Update.

Natural product (NP) databases are crucial tools in computer-aided drug design (CADD). Over the past decade, there has been a worldwide effort to assemble information regarding natural products (NPs) isolated and characterized in certain geographical regions. In 2023, it was published LANaPDB, and to our knowledge, this is the first attempt to gather and standardize all the NP databases of Latin America. Herein, we present and analyze in detail the contents of an updated version of LANaPDB, which includes 619 newly added compounds from Colombia, Costa Rica, and Mexico. The present version of LANaPDB has a total of 13 578 compounds, coming from ten databases of seven Latin American countries. A chemoinformatic characterization of LANaPDB was carried out, which includes the structural classification of the compounds, calculation of six physicochemical properties of pharmaceutical interest, and visualization of the chemical space by employing and comparing two different fingerprints (MACCS keys (166-bit) and Morgan2 (2048-bit)). Furthermore, additional analyses were made, and valuable information not included in the first version of LANaPDB was added, which includes structural diversity, molecular complexity, synthetic feasibility, commercial availability, and reported and predicted biological activity. In addition, the LANaPDB compounds were cross-referenced to two of the largest public chemical compound databases annotated with biological activity: ChEMBL and PubChem.

In silico exploration of phytochemicals as inhibitors for acute myeloid leukemia by targeting LIN28A gene: A cheminformatics study

BackgroundRecent discoveries have illustrated that Lin28A is an oncogene in various cancers, particularly acute myeloid leukemia (AML). The upregulation of Lin28A can actively contribute to tumorigenesis and migration processes in multiple organs. Hence, the inhibition of Lin28A can be achieved by applying phytochemical herbals and targeting Lin28A protein using a computer-aided drug design (CAAD) approach. MethodsIn this study, we comprehensively applied several bioinformatics tools, including gene ontologies, gene enrichment analysis, and protein-protein interactions (PPI), to determine the biological pathways, functional gene ontology, and biological pathway. Furthermore, we investigated a list of phytochemical herbs as a candidate drug by applying a computation technique involving molecular docking, density functional theory (DFT), molecular dynamics simulation (MDs), and pharmacokinetic and physiochemical properties by applying the SwissADME, pkCSM, and Molsoft LLC web-servers. ResultsThe Lin28A gene is related to two significant enrichment pathways, including proteoglycans in cancer and the pluripotency of stem cells through interactions with different genes such as MAPK12, MYC, MTOR, and PIK3CA. Interestingly, limonin, 18β Glycyrrhetic Acid, and baicalein have the highest binding energy scores of −8.4, −8.2, and −7.3 kcal/mol, respectively. The DFT study revealed that baicalein has a higher reactivity than limonin and 18β-Glycyrrhetic due to a small energy gap between LUMO and HUMO. Molecular dynamics simulation exhibited that baicalein complex with Lin28A protein is more stable than other complexes during simulation time due to low fluctuation with simulation periods as compared with other complexes, which indicated that baicalein was more fitting to docking and combining in the protein cave because of the largest number of H-bonds available for the docking simulation process. Furthermore, the drug-likeness and ADMET profiles revealed the activity of limonin, baicalein, and 18β-glycyrrhizic Acid, which possess significant inhibiting Lin28A proteins. ConclusionThis study elucidated that baicalein, 18β-glycyrrhizic, and limonin may be applied as potential candidates for targeting Lin28A as an active oncogene for acute myeloid leukemia.

Computer-aided Drug Discovery of Epigenetic Modulators in Dual-target Therapy of Multifactorial Diseases.

Numerous studies suggest that common genetic and epigenetic factors such as p53, histone deacetylase (HDAC), brain-derived neurotrophic factor (BDNF), the (Ataxia Telangiectasia mutated) ATM gene, cyclin-dependent kinase 5 (CDK5), glycogen synthase kinase 3 (GSK3) and altered expression of microRNA (miRNA) play a crucial role in cancer and neurodegeneration. As there is growing evidence that epigenetic aberrations in cancer and neurological diseases lead to complex pathophysiological changes, the simultaneous targeting of epigenetic and other related pathways by dual-target inhibitors may contribute to the discovery of more effective and personalized therapeutic options. Computer-Aided Drug Design (CADD) provides comprehensive bioinformatic, chemoinformatic, and chemometric approaches for the design of novel chemotypes of epigenetic dual-target inhibitors, enabling efficient discovery of new drug candidates for innovative treatments of these multifactorial diseases. The detailed anticancer mechanisms by which the epigenetic dual-target inhibitors alter metastatic and tumorigenic properties, influence the tumor microenvironment, or regulate the immune response are also presented and discussed in the review. To improve our understanding of the pathogenesis of cancer and neurodegeneration, this review discusses novel therapeutic agents targeting different molecular mechanisms involved in these multifactorial diseases.

Molecular modeling aided design, synthesis, and activity evaluation of N-arylindole derivatives as GPR52 agonists

G protein-coupled receptor 52 (GPR52) is considered to be a promising target to improve the symptoms of psychiatric disorders and its agonists are expected to treat schizophrenia without traditional side effects. Several research institutions have reported some small molecule GPR52 agonists, which can be the starting point for rational drug development. In this study, a series of N-arylindole derivatives were designed and synthesized based on 3g according to classical pharmacochemical methods and computer aided drug design (CADD). The designed compounds exhibited good to excellent activities and the structure-activity relationship (SAR) study was explored. The results show that the connection mode between the hydrophilic head (Part I) and the indole ring (Part II) plays an important role in the GPR52 agonist activity. Among these compounds, compounds 16 and 21 have good GPR52 agonist activity (EC50 = 93 nM and 75 nM) and can inhibit hyperactive behavior in mice induced by MK-801 (EC50 =7.94 mg/kg and 6.64 mg/kg). These designed small molecules will provide new options for the development of novel GPR52 agonists.

Structural optimization and bioactivity evaluation of 2-(Methylcarbonylamino) thiazole derivatives as novel PDE4B inhibitors

Phosphodiesterase-4 (PDE4) is a protease belonging to the phosphodiesterase family, with a specific function of hydrolyzing intracellular cyclic adenosine monophosphate (cAMP). PDE4 is widely distributed across various human tissues and cells, where it plays a pivotal role in modulating intracellular cAMP levels, particularly in immune and inflammatory cells. Consequently, PDE4 inhibitors have been proven to effectively dampen inflammatory responses in these cells, leading to a reduction in the release of pro-inflammatory factors such as lipid mediators, reactive oxygen species (ROS) hydrolases, cytokines, and chemokines. Despite the considerable interest from both academia and pharmaceutical industries in exploiting this target for drug development, only a handful of PDE4 inhibitors are available in the market. The aim of this study was to identify novel PDE4B inhibitors through a combined approach of computer-aided drug design, synthesis, and activity evaluation. The study implemented three phases of structure optimization from the hit compound MR9, which was previously obtained by virtual screening, with reference to structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches. The optimized compound MR9-302 (PDE4B IC50=2.02±0.2888 μM) exhibited enhanced inhibitory activity compared to MR9.

Pharmacological Potential of Sulindac and Its Active Metabolite: A Comprehensive Review

: In this review, we describe and discuss the pharmaceutical aspects, pharmacokinetic profile, and preclinical and clinical studies of sulindac and its active metabolite and emphasise their potential activity not only in anti-inflammation strategies but also as chemoprevention drug candidates. Though they are widely validated through in vitro and in vivo models, to date, no efforts have been made to compile in a single review on their pharmacologically potential, pharmacokinetics and toxicity profiles. Key databases such as PubMed, Science Direct, Scopus, and Google Scholar, among others, were probed for a systematic search using keywords to retrieve relevant publications. An exhaustive electronic survey of the related literature on the pharmacologically potential activity and the pharmacokinetic and toxicity profiles of sulindac resulted in around 200 articles (1975 and 2023) being included. The studies conducted on sulindac sulphide and sulindac sulfone metabolites reported a varied range of biological effects deployed in this review. The review concluded that there is scope for repurposing sulindac using computer-aided drug design and biological study to find out possible new targets for strengthening the potency and selectivity of the metabolites.

Application of computer-aided drug design in drug discovery and development: Updating knowledge

Coronavirus (CoV) diseases are widespread throughout the world and have caused considerable socio-economic disruptions. For this reason, efforts have been made to develop a direct or indirect antiviral drugs against these diseases. However, no specific antiviral drug has yet been approved by the Food and Drug Administration (FDA) for CoV infections. Thus, the challenge in discovering therapeutic molecules against these infections remains pertinent. Computer-aided drug design (CADD) is one of the modern techniques for drug discovery and development. It accelerates the process, minimizes costs, and reduces research time. In this article, we present the three CADD approaches, namely structure-based drug discovery (SBDD), ligand-based drug discovery (LBDD) and high-throughput virtual screening (HTVS). The different methods used in these three approaches CADD, such as molecular modelling, target structure analysis, molecular docking, molecular dynamics simulation, pharmacophore modelling, quantitative structure-activity relationship (QSAR), ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) are detailed. In addition, the bioinformatics tools and databases commonly used in these different CADD techniques are also described.

OpenDock: a pytorch-based open-source framework for protein-ligand docking and modelling.

Molecular docking is an invaluable computational tool with broad applications in computer-aided drug design and enzyme engineering. However, current molecular docking tools are typically implemented in languages such as C++ for calculation speed, which lack flexibility and user-friendliness for further development. Moreover, validating the effectiveness of external scoring functions for molecular docking and screening within these frameworks is challenging, and implementing more efficient sampling strategies is not straightforward. To address these limitations, we have developed an open-source molecular docking framework, OpenDock, based on Python and PyTorch. This framework supports the integration of multiple scoring functions; some can be utilized during molecular docking and pose optimization, while others can be used for post-processing scoring. In terms of sampling, the current version of this framework supports simulated annealing and Monte Carlo optimization. Additionally, it can be extended to include methods such as genetic algorithms and particle swarm optimization for sampling docking poses and protein side chain orientations. Distance constraints are also implemented to enable covalent docking, restricted docking or distance map constraints guided pose sampling. Overall, this framework serves as a valuable tool in drug design and enzyme engineering, offering significant flexibility for most protein-ligand modelling tasks. OpenDock is publicly available at: https://github.com/guyuehuo/opendock.

Molecule discovery and optimization via evolutionary swarm intelligence

Since the advent of computational analysis and visualization of chemical compounds, Computer-Aided Drug Design has made significant contributions to drug discovery. Recently, de novo drug design and molecular optimization have garnered considerable attention. Traditional optimization methods often struggle with the discrete nature of molecular space, but evolutionary computations have demonstrated their versatility across various optimization problems, regardless of the nature of the objective functions. This paper introduces a novel evolutionary algorithm, the Swarm Intelligence-Based Method for Single-Objective Molecular Optimization. Several experiments were conducted to showcase the efficiency of the proposed method, which identifies near-optimal solutions in a remarkably short time. The results were then compared with those of other state-of-the-art methods in the field.

Combined machine learning models, docking analysis, ADMET studies and molecular dynamics simulations for the design of novel FAK inhibitors against glioblastoma

Gliomas, particularly glioblastoma (GBM), are highly aggressive brain tumors with poor prognosis and high recurrence rates. This underscores the urgent need for novel therapeutic approaches. One promising target is Focal adhesion kinase (FAK), a key regulator of tumor progression currently in clinical trials for glioma treatment. Drug development, however, is both challenging and costly, necessitating efficient strategies. Computer-Aided Drug Design (CADD), especially when combined with machine learning (ML), streamlines the processes of virtual screening and optimization, significantly enhancing the efficiency and accuracy of drug discovery. Our study integrates ML, docking analysis, ADMET (absorption, distribution, metabolism, elimination, and toxicity) studies to identify novel FAK inhibitors specific to GBM. Predictive models showed strong performance, with an R2 of 0.892, MAE of 0.331, and RMSE of 0.467 using protein-level IC50 data in combined CDK, CDK extended fingerprints, and substructure fingerprint counts derived from 1280 FAK inhibitors. Another model, based on IC50 data from 2608 compounds tested on U87-MG cells, achieved an R2 of 0.789, MAE of 0.395, and RMSE of 0.536. Using these models, we efficiently identified 275 potentially active compounds out of 5107 candidates. Subsequent ADMET analysis narrowed this down to 16 potential FAK inhibitors that meet the established drug-likeness criteria. Moreover, molecular dynamics (MD) simulations validated the stable binding interactions between the selected compounds and the FAK protein. This study highlights the effectiveness of combining ML, docking analysis, and ADMET studies to rapidly identify potential FAK inhibitors from large databases, providing valuable insights for the systematic design of FAK inhibitors.

Structure-based computer-aided drug design to identify potential lead molecules for Asparaginyl Endopeptidase inhibitors.

The enzyme Asparaginyl Endopeptidase (AEP) is associated with proteinopathy-related pathologies such as Alzheimer's disease (AD) and Frontal Temporal Dementia (FTD). The onset of pathologies by AEP is due to cleaved fragments forming protein aggregates resulting in neurodegeneration. Unfortunately, there are no clinically approved small molecule inhibitors for AEP, and therefore, it serves as an unmet medical need for the design and development of potential novel small molecules. In developing potential inhibitors for proteolytic activity, a structured approach utilizing structure-based computer-aided drug design (SB-CADD) parameters was employed. This involved virtual high throughput screening (vHTS) across various CNS-focused databases enriched with diverse functionality. We identified the top sixty ligands based on the glide XP-docking score out of 10 million ligands. The free binding energy was then calculated using MM-GBSA for all top selected molecules which resulted in discovering that AEPI-1 to AEPI-6 (Asparaginyl Endopeptidase inhibitors) displayed high affinity towards the catalytic triad. Further investigation determined that all top six hits form stable complexes during 50 ns molecular dynamic simulations. We also observed that AEPI-2 demonstrated the highest stability within the binding pockets. Post-MD analyses such as DCCM, PCA, PDF, and ADMET properties were also evaluated. By bridging all the observations, we observed these six molecules occupy the active site of the β-helix (β1, β3, and β4) of the S1 pocket and additional binding sites in α1 and β5, suggesting its suitability as a potential candidate for drug discovery against Asparaginyl Endopeptidase.

Data-augmented machine learning scoring functions for virtual screening of YTHDF1 m6A reader protein

Machine learning is rapidly advancing the drug discovery process, significantly enhancing speed and efficiency. Innovation in computer-aided drug design is primarily driven by structure- and ligand-based approaches. When the number of known inhibitors for a target is limited, data augmentation strategies are often preferred to enhance model performance. In this study, we developed predictive machine learning models for structure-based drug discovery leveraging multiple traditional machine learning algorithms trained with target and ligand dynamics-aware datasets.To illustrate our approach, we present a composite model that combines classification and regression to predict YTHDF1 inhibitors, utilizing PLEC features. YTHDF1, a key m6A reader protein involved in mRNA translation, is implicated in various cancers, making it a promising therapeutic target. Traditional structure-based virtual screening (SBVS) using generic scoring functions has struggled to identify potent YTHDF1 inhibitors due to the protein's unique binding characteristics. To overcome this, we developed YTHDF1-specific machine learning scoring functions (MLSFs) to enhance SBVS efficacy.We employed various data augmentation techniques to generate a comprehensive dataset, incorporating multiple conformations of ligands and the YTHDF1 protein. We have trained 64 YTHDF1-specific MLSFs using four machine learning algorithms and evaluated them on ten test sets, focusing on their predictive and ranking power. Our results demonstrate that the artificial neural network with protein-ligand extended connectivity fingerprints (ANN-PLEC) outperforms other MLSFs, consistently achieving high area under the precision-recall curve (PR-AUC) of 0.87. This method shows promise for targets with limited quantities of active molecules, providing a viable path forward for drug discovery research. The ANN-PLEC scoring function is made freely available on GitHub for other researchers to access and utilize https://github.com/JuniML/SBVS-YTHDF1/.

Computational exploration of novel ketoprofen derivatives: Molecular dynamics simulations and MM-PBSA calculations for COX-2 inhibition as promising anti-inflammatory drugs

Computer-aided drug design is widely employed to identify novel compounds for therapeutic applications. Ketoprofen (KTP), a commonly used and marketed nonsteroidal anti-inflammatory drug (NSAID), is effective in treating pain, fever, inflammation, and some cancers. In this research, we explored the behavior of six analogues designed by structurally modifying the KTP molecule. Specifically, KTP-A and KTP-B contain a –CN group at the ortho position, KTP-C and KTP-D have a –CN group at the meta position, and KTP-E and KTP-F feature a –CF3 group at the meta position. To assess these analogues, we conducted molecular dynamics simulations (MD) to study their inhibitory effects on human cyclooxygenase 2 (COX-2), providing detailed insights into the structure and dynamics of the protein both with and without ligands. MD simulation, enhanced by technological advances, has proven to be a powerful tool for new drug discovery. We further quantified the binding affinity of these drug molecules toward COX-2 using molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) binding free energy calculations. The dynamic properties were evaluated through analyses of root mean square deviations (RMSD), root mean square fluctuations (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), covariance matrix, principal component analysis (PCA), and Gibbs free energy landscapes (FEL). Ultimately, this study confirms that the six KTP derivatives are promising candidates for the treatment of inflammation, with KTP-B standing out as particularly effective.