Multi-target dopamine D3 receptor modulators: Actionable knowledge for drug design from molecular dynamics and machine learning

Biochemical and molecular docking-based strategies of Acalypha indica and Boerhavia diffusa extract by targeting bacterial strains and cancer proteins

Antibiotic-resistant microbes have emerged around the world, presenting a risk to health. Plant-derived drugs have become a potential source for the production of antibiotic-resistant drugs and cancer therapies. In this study, we investigated the antibacterial, cytotoxic and antioxidant properties of Acalypha indica and Boerhavia diffusa, and conducted in silico molecular docking experiments against EGFR and VEGFR-2 proteins. The metabolic extract of A. indica inhibited Streptococcus iniae and Staphylococcus sciuri with inhibition zones of 21.66 ± 0.57 mm and 20.33 ± 0.57 mm, respectively. The B. diffusa leaf extract produced inhibition zones of 20.3333 ± 0.5773 mm and 20.33 ± 0.57 mm against Streptococcus iniae and Edwardsiella anguillarum, respectively. A. indica and B. diffusa extracts had toxicities of 162.01 μg/ml and 175.6 μg/ml, respectively. Moreover, B. diffusa (IC50 =154.42 µg/ml) leaf extract exhibited moderately higher antioxidant activity compared with the A. indica (IC50 = 218.97 µg/ml) leaf extract. Multiple interactions were observed at Leu694, Met769 and Leu820 sites for EGFR and at Asp1046 and Cys1045 sites for VEGFR during the molecular docking study. CID-235030, CID-70825 and CID-156619353 had binding energies of -7.6 kJ/mol, -7.5 kJ/mol and -7.6 kJ/mol, respectively, with EGFR protein. VEGFR-2 protein had docking energies of -7.5 kJ/mol, -7.6 kJ/mol and -7.3 kJ/mol, respectively, for CID-6420353, CID-156619353 and CID-70825 compounds. The MD simulation trajectories revealed the hit compound; CID-235030 and EGFR complex, CID-6420353 and VEGFR-2 exhibit stable profile in the root mean square deviation (RMSD), radius of gyration (Rg), solvent accessible surface area (SASA), hydrogen bond and root mean square fluctuation (RMSF) and the binding free energy by MM-PBSA method. This study indicates that methanol extracts of A. indica and B. diffusa may play a crucial role in developing antibiotic-resistant and cancer drugs.Communicated by Ramaswamy H. Sarma.

Machine-learning-based predictions of imprinting quality using ensemble and non-linear regression algorithms

The molecularly imprinted polymers are artificial polymers that, during the synthesis, create specific sites for a definite purpose. These polymers due to their characteristics such as stability, easy of synthesis, reproducibility, reusability, high accuracy, and selectivity have many applications. However, the variety of the functional monomers, templates, solvents, and synthesis conditions like pH, temperature, the rate of stirring, and time, limit the selectivity of imprinting. The Practical optimization of the synthetic conditions has many drawbacks, including chemical compound usage, equipment requirements, and time costs. The use of machine learning (ML) for the prediction of the imprinting factor (IF), which indicates the quality of imprinting is a very interesting idea to overcome these problems. The ML has many advantages, for example a lack of human error, high accuracy, high repeatability, and prediction of a large amount of data in the minimum time. In this research, ML was used to predict the IF using non-linear regression algorithms, including classification and regression tree, support vector regression, and k-nearest neighbors, and ensemble algorithms, like gradient boosting (GB), random forest, and extra trees. The data sets were obtained practically in the laboratory, and inputs, included pH, the type of the template, the type of the monomer, solvent, the distribution coefficient of the MIP (K_MIP), and the distribution coefficient of the non-imprinted polymer (K_NIP). The mutual information feature selection method was used to select the important features affecting the IF. The results showed that the GB algorithm had the best performance in predicting the IF, and using this algorithm, the maximum R² value (R² = 0.871), and the minimum mean absolute error (MAE = - 0.982), and mean square error were obtained (MSE = - 2.303).

GLOW: A Workflow Integrating Gaussian-Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling

We introduce a Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and free energy profiling workflow (GLOW) to predict molecular determinants and map free energy landscapes of biomolecules. All-atom GaMD-enhanced sampling simulations are first performed on biomolecules of interest. Structural contact maps are then calculated from GaMD simulation frames and transformed into images for building DL models using a convolutional neural network. Important structural contacts are further determined from DL models of attention maps of the structural contact gradients, which allow us to identify the system reaction coordinates. Finally, free energy profiles are calculated for the selected reaction coordinates through energetic reweighting of the GaMD simulations. We have also successfully demonstrated GLOW for the characterization of activation and allosteric modulation of a G protein-coupled receptor, using the adenosine A₁ receptor (A₁AR) as a model system. GLOW findings are highly consistent with previous experimental and computational studies of the A₁AR, while also providing further mechanistic insights into the receptor function. In summary, GLOW provides a systematic approach to mapping free energy landscapes of biomolecules. The GLOW workflow and its user manual can be downloaded at http://miaolab.org/GLOW.

Dopamine D3 receptor ligands: a patent review (2014-2020)

INTRODUCTION

Compelling evidence identified D3 dopamine receptor (D3R) as a suitable target for therapeutic intervention on CNS-associated disorders, cancer and other conditions. Several efforts have been made toward developing potent and selective ligands for modulating signalling pathways operated by these GPCRs. The rational design of D3R ligands endowed with a pharmacologically relevant profile has traditionally not encountered much support from computational methods due to a very limited knowledge of the receptor structure and of its conformational dynamics. We believe that recent progress in structural biology will change this state of affairs in the next decade.

AREAS COVERED

This review provides an overview of the recent (2014-2020) patent literature on novel classes of D3R ligands developed within the framework of CNS-related diseases, cancer and additional conditions. When possible, an in-depth description of both in vitro and in vivo generated data is presented. New therapeutic applications of known molecules with activity at D3R are discussed.

EXPERT OPINION

Building on current knowledge, future D3R-focused drug discovery campaigns will be propelled by a combination of unprecedented availability of structural information with advanced computational and analytical methods. The design of D3R ligands with the sought activity, efficacy and selectivity profile will become increasingly more streamlined.

Computational anti-COVID-19 drug design: progress and challenges

Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments.

Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications

Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional "one-target, one disease" paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice face of its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated to the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.

In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis

We have performed theoretical calculations with 70 drugs that have been considered in 231 clinical trials as possible candidates to repurpose drugs for schizophrenia based on their interactions with the dopaminergic system. A hypothesis of shared pharmacophore features was formulated to support our calculations. To do so, we have used the crystal structure of the D2-like dopamine receptor in complex with risperidone, eticlopride, and nemonapride. Linagliptin, citalopram, flunarizine, sildenafil, minocycline, and duloxetine were the drugs that best fit with our model. Molecular docking calculations, molecular dynamics outcomes, blood-brain barrier penetration, and human intestinal absorption were studied and compared with the results. From the six drugs selected in the shared pharmacophore features input, flunarizine showed the best docking score with D2, D3, and D4 dopamine receptors and had high stability during molecular dynamics simulations. Flunarizine is a frequently used medication to treat migraines and vertigo. However, its antipsychotic properties have been previously hypothesized, particularly because of its possible ability to block the D2 dopamine receptors.

Machine Learning of Allosteric Effects: The Analysis of Ligand-Induced Dynamics to Predict Functional Effects in TRAP1

Allosteric molecules provide a powerful means to modulate protein function. However, the effect of such ligands on distal orthosteric sites cannot be easily described by classical docking methods. Here, we applied machine learning (ML) approaches to expose the links between local dynamic patterns and different degrees of allosteric inhibition of the ATPase function in the molecular chaperone TRAP1. We focused on 11 novel allosteric modulators with similar affinities to the target but with inhibitory efficacy between the 26.3 and 76%. Using a set of experimentally related local descriptors, ML enabled us to connect the molecular dynamics (MD) accessible to ligand-bound (perturbed) and unbound (unperturbed) systems to the degree of ATPase allosteric inhibition. The ML analysis of the comparative perturbed ensembles revealed a redistribution of dynamic states in the inhibitor-bound versus inhibitor-free systems following allosteric binding. Linear regression models were built to quantify the percentage of experimental variance explained by the predicted inhibitor-bound TRAP1 states. Our strategy provides a comparative MD–ML framework to infer allosteric ligand functionality. Alleviating the time scale issues which prevent the routine use of MD, a combination of MD and ML represents a promising strategy to support in silico mechanistic studies and drug design.

Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation

Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.

Data-Driven Molecular Dynamics: A Multifaceted Challenge

The big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental data.

An up-to-date overview of computational polypharmacology in modern drug discovery

INTRODUCTION

In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success.

AREAS COVERED

In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies.

EXPERT OPINION

Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.

Dopamine Receptor Subtypes, Physiology and Pharmacology: New Ligands and Concepts in Schizophrenia

Dopamine receptors are widely distributed within the brain where they play critical modulator roles on motor functions, motivation and drive, as well as cognition. The identification of five genes coding for different dopamine receptor subtypes, pharmacologically grouped as D1- (D1 and D5) or D2-like (D2S, D2L, D3, and D4) has allowed the demonstration of differential receptor function in specific neurocircuits. Recent observation on dopamine receptor signaling point at dopamine-glutamate-NMDA neurobiology as the most relevant in schizophrenia and for the development of new therapies. Progress in the chemistry of D1- and D2-like receptor ligands (agonists, antagonists, and partial agonists) has provided more selective compounds possibly able to target the dopamine receptors homo and heterodimers and address different schizophrenia symptoms. Moreover, an extensive evaluation of the functional effect of these agents on dopamine receptor coupling and intracellular signaling highlights important differences that could also result in highly differentiated clinical pharmacology. The review summarizes the recent advances in the field, addressing the relevance of emerging new targets in schizophrenia in particular in relation to the dopamine - glutamate NMDA systems interactions.