Teaching and Learning Computational Drug Design: Student Investigations of 3D Quantitative Structure-Activity Relationships through Web Applications

Discovery of putative inhibitors of human Pkd1 enzyme: Molecular docking, dynamics and simulation, QSAR, and MM/GBSA

Polycystic kidney disease is the most prevalent hereditary kidney disease globally and is mainly linked to the overexpression of a gene called PKD1. To date, there is no effective treatment available for polycystic kidney disease, and the practicing treatments only provide symptomatic relief. Discovery of the compounds targeting the PKD1 gene by inhibiting its expression under the disease condition could be crucial for effective drug development. In this study, a molecular docking and molecular dynamic simulation, QSAR, and MM/GBSA-based approaches were used to determine the putative inhibitors of the Pkd1 enzyme from a library of 1379 compounds. Initially, fourteen compounds were selected based on their binding affinities with the Pkd1 enzyme using MOE and AutoDock tools. The selected drugs were further investigated to explore their properties as drug candidates and the stability of their complex formation with the Pkd1 enzyme. Based on the physicochemical and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties, and toxicity profiling, two compounds including olsalazine and diosmetin were selected for the downstream analysis as they demonstrated the best drug-likeness properties and highest binding affinity with Pkd1 in the docking experiment. Molecular dynamic simulation using Gromacs further confirmed the stability of olsalazine and diosmetin complexes with Pkd1 and establishing interaction through strong bonding with specific residues of protein. High biological activity and binding free energies of two complexes calculated using 3D QSAR and Schrodinger module, respectively further validated our results. Therefore, the molecular docking and dynamics simulation-based in-silico approach used in this study revealed olsalazine and diosmetin as potential drug candidates to combat polycystic kidney disease by targeting Pkd1 enzyme.

AI-based 3D-QSAR model of FDA-approved repurposed drugs for inhibiting sclerostin

BACKGROUND

Wnt activation promotes bone formation and prevents bone loss. The Wnt pathway antagonist sclerostin and additional anti-sclerostin antibodies were discovered as a result of the development of the monoclonal antibody romosozumab. These monoclonal antibodies greatly increase the risk of cardiac arrest. Three-dimensional quantitative structure-activity relationships (3D-QSAR) predicts biological activities of ligands based on their three-dimensional features by employing powerful chemometric investigations such as artificial neural networks (ANNs) and partial least squares (PLS).

OBJECTIVE

In this study, ligand-receptor interactions were investigated using 3D-QSAR Comparative molecular field analysis (CoMFA). Estimates of steric and electrostatic characteristics in CoMFA are made using Lennard-Jones and Coulomb potentials.

METHODS

To identify the conditions necessary for the activity of these molecules, fifty Food and Drug Administration (FDA)-approved medications were chosen for 3D-QSAR investigations and done by CoMFA. For QSAR analysis, there are numerous tools available. This study employed Open 3D-QSAR for analysis due to its simplicity of use and capacity to produce trustworthy results. Four tools were used for the analysis on this platform: Py-MolEdit, Py-ConfSearch, and Py-CoMFA.

RESULTS

Maps that were generated were used to determine the screen's r2 (Coefficient of Multiple Determinations) value and q2 (correlation coefficient). These numbers must be fewer than 1, suggesting a good, trustworthy model. Cross-validated (q2) 0.532 and conventional (r2) correlation values of 0.969 made the CoMFA model statistically significant. The model showed that hydroxamic acid inhibitors are significantly more sensitive to the steric field than the electrostatic field (70%) (30%). This hypothesis states that steric (43.1%), electrostatic (26.4%), and hydrophobic (20.3%) qualities were important in the design of sclerostin inhibitors.

CONCLUSION

With 3D-QSAR and CoMFA, statistically meaningful models were constructed to predict ligand inhibitory effects. The test set demonstrated the model's robustness. This research may aid in the development of more effective sclerostin inhibitors that are synthesised using FDA-approved medications.

Py-CoMFA, docking, and molecular dynamics simulations of Leishmania (L.) amazonensis arginase inhibitors

Leishmaniasis is a disease caused by a protozoan of the genus Leishmania, affecting millions of people, mainly in tropical countries, due to poor social conditions and low economic development. First-line chemotherapeutic agents involve highly toxic pentavalent antimonials, while treatment failure is mainly due to the emergence of drug-resistant strains. Leishmania arginase (ARG) enzyme is vital in pathogenicity and contributes to a higher infection rate, thus representing a potential drug target. This study helps in designing ARG inhibitors for the treatment of leishmaniasis. Py-CoMFA (3D-QSAR) models were constructed using 34 inhibitors from different chemical classes against ARG from L. (L.) amazonensis (LaARG). The 3D-QSAR predictions showed an excellent correlation between experimental and calculated pIC50 values. The molecular docking study identified the favorable hydrophobicity contribution of phenyl and cyclohexyl groups as substituents in the enzyme allosteric site. Molecular dynamics simulations of selected protein-ligand complexes were conducted to understand derivatives' interaction modes and affinity in both active and allosteric sites. Two cinnamide compounds, 7g and 7k, were identified, with similar structures to the reference 4h allosteric site inhibitor. These compounds can guide the development of more effective arginase inhibitors as potential antileishmanial drugs.

An interdisciplinary course on computer-aided drug discovery to broaden student participation in original scientific research

We present a new highly interdisciplinary project-based course in computer aided drug discovery (CADD). This course was developed in response to a call for alternative pedagogical approaches during the COVID-19 pandemic, which caused the cancellation of a face-to-face summer research program sponsored by the Louisiana Biomedical Research Network (LBRN). The course integrates guided research and educational experiences for chemistry, biology, and computer science students. We implement research-based methods with publicly available tools in bioinformatics and molecular modeling to identify and prioritize promising antiviral drug candidates for COVID-19. The purpose of this course is three-fold: I. Implement an active learning and inclusive pedagogy that fosters student engagement and research mindset; II. Develop student interdisciplinary research skills that are highly beneficial in a broader scientific context; III. Demonstrate that pedagogical shifts (initially incurred during the COVID-19 pandemic) can furnish longer-term instructional benefits. The course, which has now been successfully taught a total of five times, incorporates four modules, including lectures/discussions, live demos, inquiry-based assignments, and science communication.

Molecular Gas-Phase Conformational Ensembles

Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.

Carbazole derivatives as promising competitive and allosteric inhibitors of human serotonin transporter: computational pharmacology

The human serotonin transporters (hSERTs) are neurotransmitter sodium symporters of the aminergic G protein-coupled receptors, regulating the synaptic serotonin and neuropharmacological processes related to neuropsychiatric disorders, notably, depression. Selective serotonin reuptake inhibitors (SSRIs) such as fluoxetine and (S)-citalopram are competitive inhibitors of hSERTs and are commonly the first-line medications for major depressive disorder (MDD). However, treatment-resistance and unpleasant aftereffects constitute their clinical drawbacks. Interestingly, vilazodone emerged with polypharmacological (competitive and allosteric) inhibitions on hSERTs, amenable to improved efficacy. However, its application usually warrants adjuvant/combination therapy, another subject of critical adverse events. Thus, the discovery of alternatives with polypharmacological potentials (one-drug-multiple-target) and improved safety remains essential. In this study, carbazole analogues from chemical libraries were explored using docking and molecular dynamics (MD) simulation. Selectively, two IBScreen ligands, STOCK3S-30866 and STOCK1N-37454 predictively bound to the active pockets and expanded boundaries (extracellular vestibules) of the hSERTs more potently than vilazodone and (S)-citalopram. For instance, the two ligands showed docking scores of -9.52 and -9.59 kcal/mol and MM-GBSA scores of -92.96 and -65.66 kcal/mol respectively compared to vilazodone's respective scores of -7.828 and -59.27 against the central active site of the hSERT (PDB 7LWD). Similarly, the two ligands also docked to the allosteric pocket (PDB 5I73) with scores of -8.15 and -8.40 kcal/mol and MM-GBSA of -96.14 and -68.46 kcal/mol whereas (S)-citalopram has -6.90 and -69.39 kcal/mol respectively. The ligands also conferred conformational stability on the receptors during 100 ns MD simulations and displayed interesting ADMET profiles, representing promising hSERT modulators for MDD upon experimental validation.Communicated by Ramaswamy H. Sarma.

Developing an international concept-based curriculum for pharmacology education: The promise of core concepts and concept inventories

Over recent years, studies have shown that science and health profession graduates demonstrate gaps in their fundamental pharmacology knowledge and ability to apply pharmacology concepts in practice. This article reviews the current challenges faced by pharmacology educators, including the exponential growth in discipline knowledge and competition for curricular time. We then argue that pharmacology education should focus on essential concepts that enable students to develop beyond 'know' towards 'know how to'. A concept-based approach will help educators prioritize and benchmark their pharmacology curriculum, facilitate integration of pharmacology with other disciplines in the curriculum, create alignment between universities and improve application of pharmacology knowledge to professional contexts such as safe prescribing practices. To achieve this, core concepts first need to be identified and unpacked, and methods for teaching and assessment using concept inventories developed. The International Society for Basic and Clinical Pharmacology Education Section (IUPHAR-Ed) Core Concepts of Pharmacology (CCP) initiative involves over 300 educators from the global pharmacology community. CCP has identified and defined the core concepts of pharmacology, together with key underpinning sub-concepts. To realize these benefits, pharmacology educators must develop methods to teach and assess core concepts. Work to develop concept inventories is ongoing, including identifying student misconceptions of the core concepts and creating a bank of multiple-choice questions to assess student understanding. Future work aims to develop and validate materials and methods to help educators embed core concepts within curricula. Potential strategies that educators can use to overcome factors that inhibit adoption of core concepts are presented.

Convergent QSAR Models for the Prediction of Cruzain Inhibitors

Chagas disease is a parasitosis caused by Trypanosoma cruzi. Cruzain, the major cysteine protease from T. cruzi, is an excellent therapeutic target in the search for antichagasic drugs. It is important in the role of cell invasion, replication, differentiation, and metabolism of the parasite. In this work, we developed and assessed multiple quantitative structure-activity relationship (QSAR) models for a set of 61 cruzain inhibitors. These models include two-dimensional (2D) QSAR, three-dimensional (3D) QSAR, such as comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), and Hologram QSAR (HQSAR). In total, we generated 10 major and 114 minor model variations. Molecular docking was used to successfully align the molecules. All CoMFA and CoMSIA models, which incorporate multiple fields, demonstrated robustness in our analysis. Steric fields exhibited satisfactory convergence in the contour maps, while the electrostatic field converged into a single small region. The HQSAR model taking into consideration only Atoms and Connectivity, with fragment sizes ranging from two to five atoms, was considered the best of the HQSAR variations, despite exhibiting a higher level of deviance. In total, 78 model variations meet the minimum requirements to be considered acceptable. We found that using as few as five descriptors it is possible to obtain robust results with 2D-QSAR. Models such as Random Forest, Tree Ensemble, Linear Regression, and HQSAR are recommended for working with large data sets, while the 3D-QSAR models are intended to study the geometry of the ligands, to optimize them into new and better performing antichagasics. Virtual Screening of a set of hydrazones, guided by the top-performing models, identified promising candidates for experimental validation. Among them, dv007 and dv015 exhibited consistently high predicted pIC50 values (7.26 and 7.24, respectively), making them compelling candidates for further drug development.

Molecular Docking Assessment of Cathinones as 5-HT2AR Ligands: Developing of Predictive Structure-Based Bioactive Conformations and Three-Dimensional Structure-Activity Relationships Models for Future Recognition of Abuse Drugs

Commercially available cathinones are drugs of long-term abuse drugs whose pharmacology is fairly well understood. While their psychedelic effects are associated with 5-HT2AR, the enclosed study summarizes efforts to shed light on the pharmacodynamic profiles, not yet known at the receptor level, using molecular docking and three-dimensional quantitative structure-activity relationship (3-D QSAR) studies. The bioactive conformations of cathinones were modeled by AutoDock Vina and were used to build structure-based (SB) 3-D QSAR models using the Open3DQSAR engine. Graphical inspection of the results led to the depiction of a 3-D structure analysis-activity relationship (SAR) scheme that could be used as a guideline for molecular determinants by which any untested cathinone molecule can be predicted as a potential 5-HT2AR binder prior to experimental evaluation. The obtained models, which showed a good agreement with the chemical properties of co-crystallized 5-HT2AR ligands, proved to be valuable for future virtual screening campaigns to recognize unused cathinones and similar compounds, such as 5-HT2AR ligands, minimizing both time and financial resources for the characterization of their psychedelic effects.

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.

Computational Methods for Molecular Understanding of the Antibiotic-Aminoacyl tRNA Synthetase Interaction

Developing an understanding of the interactions between an antibiotic and its binding site in a pathogen cell is the key to antibiotic design-an important cost-saving methodology compared to the costly and time-consuming random trial-and-error approach. The rapid development of antibiotic resistance provides an impetus for such studies. Recent years have witnessed the beginning of the use of combined computational techniques, including computer simulations and quantum mechanical computations, to understand how antibiotics bind at the active site of aminoacyl tRNA synthetases (aaRSs) from pathogens. Such computational protocols assist the knowledge-based design of antibiotics targeting aaRSs, which are their validated targets. After the ideas behind the protocols and their strategic planning are discussed, the protocols are described along with their major outcomes. This is followed by an integration of results from the different basic protocols. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Analysis of active-site residues from primary sequence of synthetase and transfer RNAs Basic Protocol 2: Molecular dynamics simulation-based protocol to study the structure and dynamics of the aaRS active site:antibiotic complex Basic Protocol 3: Quantum mechanical method-based protocol to study the structure and dynamics of the aaRS active site:antibiotic complex.

Hansch-Type QSAR Models for the Rational Design of MAO Inhibitors: Basic Principles and Methodology

Hansch-type regression analysis enables the derivation of quantitative structure-activity relationship (QSAR) equations correlating bioactivity data with physicochemical parameters accounting for hydrophobicity, electronic properties, and steric effects of molecules or functional groups (substituents). Two datasets of MAO A and B inhibitors were enrolled in prototypical workflows employing multiparametric stepwise regression analysis, which includes linear and nonlinear (generally quadratic) terms. The optimal choice of variables (and/or combinations thereof) along with statistical validation yielded two robust equations describing MAO B potency and B/A selectivity, which included three and one parameter(s), respectively, and explained more than 80% of y-variance (r²) with low standard deviation (s) and good statistical significance (F, Fisher value).

Bovine Serum Amine Oxidase and Polyamine Analogues: Chemical Synthesis and Biological Evaluation Integrated with Molecular Docking and 3-D QSAR Studies

Natural polyamines (PAs) are key players in cellular homeostasis by regulating cell growth and proliferation. Several observations highlight that PAs are also implicated in pathways regulating cell death. Indeed, the PA accumulation cytotoxic effect, maximized with the use of bovine serum amine oxidase (BSAO) enzyme, represents a valuable strategy against tumor progression. In the present study, along with the design, synthesis, and biological evaluation of a series of new spermine (Spm) analogues (1-23), a mixed structure-based (SB) and ligand-based (LB) protocol was applied. Binding modes of BSAO-PA modeled complexes led to clarify electrostatic and steric features likely affecting the BSAO-PA biochemical kinetics. LB and SB three-dimensional quantitative structure-activity relationship (Py-CoMFA and Py-ComBinE) models were developed by means of the 3d-qsar.com portal, and their analysis represents a strong basis for future design and synthesis of PA BSAO substrates for potential application in oxidative stress-induced chemotherapy.

Human Estrogen Receptor Alpha Antagonists, Part 3: 3-D Pharmacophore and 3-D QSAR Guided Brefeldin A Hit-to-Lead Optimization toward New Breast Cancer Suppressants

The estrogen receptor α (ERα) is an important biological target mediating 17β-estradiol driven breast cancer (BC) development. Aiming to develop innovative drugs against BC, either wild-type or mutated ligand-ERα complexes were used as source data to build structure-based 3-D pharmacophore and 3-D QSAR models, afterward used as tools for the virtual screening of National Cancer Institute datasets and hit-to-lead optimization. The procedure identified Brefeldin A (BFA) as hit, then structurally optimized toward twelve new derivatives whose anticancer activity was confirmed both in vitro and in vivo. Compounds as SERMs showed picomolar to low nanomolar potencies against ERα and were then investigated as antiproliferative agents against BC cell lines, as stimulators of p53 expression, as well as BC cell cycle arrest agents. Most active leads were finally profiled upon administration to female Wistar rats with pre-induced BC, after which 3DPQ-12, 3DPQ-3, 3DPQ-9, 3DPQ-4, 3DPQ-2, and 3DPQ-1 represent potential candidates for BC therapy.

Synthesis, molecular docking and extensive structure activity relationship of substituted DHP derivatives: a new class of herbicides

In the present study, twenty-two derivatives of dihydropyridine (DHP) have been synthesized using the Boric acid catalyst in solventless conditions. The synthesis was confirmed by FTIR analysis, 1HNMR, and 13CNMR analysis. The quantitative structure-activity relationship for all the synthesized derivatives was performed using an artificial neural network with correlation coefficient (R²) 0.8611, mean standard error 0.19, and Comparative molecular field analysis (CoMFA) with correlation coefficient (R²) 0.713, mean standard error 0.27. The molecular docking activity of synthesized compounds was tested using "AUTODOCK VINA" against "Acetohydroxyacid synthase protein receptors (PDB code 1YHZ)" acquired from the "RCSB Protein Data Bank". Docking experiments demonstrated favorable interaction among synthesized DHP derivatives and protein receptors with significant binding energy values. These synthesized derivatives have been screened for their pre-emergence herbicidal bioassay against weed species Echinochola crus galli, and the IC50 value were calculated and activity was compared with Butachlor, significant activity was exhibited by all the derivatives. All the synthesized compounds were also screened for their post emergence herbicidal activity against Echinochola crus galli, and the activity of DHPs were compared with penoxulum. All the synthesized compounds show good to moderate activity. Thus, it is concluded that substituted DHP derivatives may be developed as potential herbicides.

Human Estrogen Receptor α Antagonists. Part 1: 3-D QSAR-Driven Rational Design of Innovative Coumarin-Related Antiestrogens as Breast Cancer Suppressants through Structure-Based and Ligand-Based Studies

The estrogen receptor α (ERα) represents a 17β-estradiol-inducible transcriptional regulator that initiates the RNA polymerase II-dependent transcriptional machinery, pointed for breast cancer (BC) development via either genomic direct or genomic indirect (i.e., tethered) pathway. To develop innovative ligands, structure-based (SB) three-dimensional (3-D) quantitative structure-activity relationship (QSAR) studies have been undertaken from structural data taken from partial agonists, mixed agonists/antagonists (selective estrogen receptor modulators (SERMs)), and full antagonists (selective ERα downregulators (SERDs)) correlated with either wild-type or mutated ERα receptors. SB and ligand-based (LB) alignments allow us to rule out guidelines for the SB/LB alignment of untested compounds. 3-D QSAR models for ERα ligands, coupled with SB/LB alignment, were revealed to be useful tools to dissect the chemical determinants for ERα-based anticancer activity as well as to predict their potency. The herein developed protocol procedure was verified through the design and potency prediction of 12 new coumarin-based SERMs, namely, 3DQ-1a to 3DQ-1e, that upon synthesis turned to be potent ERα antagonists by means of either in vitro or in vivo assays (described in the second part of this study).

Development and use of augmented reality models to teach medicinal chemistry

BACKGROUND AND PURPOSE

Students in the doctor of pharmacy curriculum have varied backgrounds in their chemical training and also their ability to make mental conversions from two-dimensional chemical representations, on lecture slides or textbook images, to three-dimensional cognitive understanding. In order to bridge the gap, augmented reality (AR) models were developed to provide an alternative learning medium for the students. AR was selected to take advantage of the ubiquitous presence of smartphones, without incurring the expense of Virtual Reality hardware.

EDUCATIONAL ACTIVITY AND SETTING

AR models were developed and introduced in the classroom in three phases. Student survey responses were used to improve the utility of the models in between phases. Active learning exercises were developed that required both individual and group interactions to complete.

FINDINGS

An optimized AR model creation workflow was developed that allowed each AR model to be created and posted in about 30 min. Depending on the phase of the study, 69% to 88% of the students found the AR models easy to use and 58% to 83% wanted to see more AR models used in future lectures. A majority (76%) of the students viewed the AR models on their smartphones.

SUMMARY

Augmented reality modules were created for use in medicinal chemistry courses in the pharmacy curriculum. Models were introduced in phases and included iterative improvements based on student feedback. The AR exercises provided active learning opportunities and were well received. The majority of students would like additional AR modules used in the course.