Insights into the structural biology of G-protein coupled receptors impacts drug design for central nervous system neurodegenerative processes

In the last few years, there have been important new insights into the structural biology of G-protein coupled receptors. It is now known that allosteric binding sites are involved in the affinity and selec- tivity of ligands for G-protein coupled receptors, and that signaling by these receptors involves both G-protein dependent and independent pathways. The present review outlines the physiological and pharmacological implications of this perspective for the design of new drugs to treat disorders of the central nervous system. Specifically, new possibilities are explored in relation to allosteric and or- thosteric binding sites on dopamine receptors for the treatment of Parkinson＇s disease, and on muscarinic receptors for Alzheimer＇s disease. Future research can seek to identify ligands that can bind to more than one site on the same receptor, or simultaneously bind to two receptors and form a dimer. For example, the design of bivalent drugs that can reach homo/hetero-dimers of D2 dopa- mine receptor holds promise as a relevant therapeutic strategy for Parkinson＇s disease. Regarding the treatment of Alzheimer＇s disease, the design of dualsteric ligands for mono-oligomeric mus- carinic receptors could increase therapeutic effectiveness by generating potent compounds that could activate more than one signaling pathway.

Comparative and Computer Assisted Drug Designing of Fatty Acids Isolated from Flowers, Leaves, Stem Bark, Root Bark and Nuts of Semecarpus anacardium L. f. （Anacardiaceae）

Objectives： Computational study will help us in reducing the experimental work. The process of drug discovery involves the designing of molecules with appropriate pharmacophores with the help of various soft wares. The purpose of this paper is to study the probable binding modes of fatty acids on fatty acids after enzymatic hydrolysis of the FAAH （fatty acid amide hydrolase） in different extracts of flowers, leaves, stem bark, root bark and nuts of Semecarpus anacardiurn L. f. by using molecular modeling study and computer assisted drug designing. Nuts yielded 20 fatty acids including saturated, ω-3 unsaturated, ω-6 unsaturated, ω-7 unsaturated and ω-9 unsaturated fatty acids. Based on IR, IH NMR, 13C NMR, MS （mass） spectrometry, GC analysis, the structural elucidation of these isolated fatty acids was established. Methods： A dataset comprising of 20 fatty acids were drawn in ChemDraw and converted into 3D-molecules with all possible tautomers and chiral centers. The minimization of molecules was carried out using PRCG （Polak-Ribiere Conjugate Gradient） method with maximum of 5000 iterations. The minimized compounds were used for protein preparation. The crystal structure of human FAAH （PDB ID： 3K84） is prepared and selected for the docking studies of 20 fatty acids using Schr6dinger docking program module.. Conclusions： In this study, we carried out the molecular docking studies in order to understand the probable binding mode of 20 fatty acids in FAAH from which we identified key active site residues for FAAH, thereby it can be used to design the novel compounds for FAAH targets.

Application of Computer-Aided Drug Design to Traditional Chinese Medicine

Computer-aided drug design (CADD) is an interdisciplinary subject, playing a pivotal role during new drug research and development, especially the discovery and optimization of lead compounds. Traditional Chinese Medicine (TCM) modernization is the only way of TCM development and also an effective approach to the development of new drugs and the discovery of potential drug targets (PDTs). Discovery and validation of PTDs has become the “bottle-neck” restricted new drug research and development and is urgently solved. Innovative drug research is of great significance and bright prospects. This paper mainly discusses the “druggability” and specificity of PTDs, the “druglikeness” of drug candidates, the methods and technologies of the discovery and validation of PTDs and their application. It is very important to achieve the invention and innovation strategy “from gene to drug”. In virtue of modern high-new technology, especially CADD, combined with TCM theory, research and develop TCM and initiate an innovating way fitting our country progress. This paper mainly discusses CADD and their application to drug research, especially TCM modernization.

Deep Learning in Medical Imaging and Drug Design

Over the last decade,deep learning(DL)methods have been extremely successful and widely used in almost every domain.Researchers are now focusing on the convergence of medical imaging and drug design using deep learning to revolutionize medical diagnostic and improvement in the monitoring from response to therapy.DL a new machine learning paradigm that focuses on learning with deep hierarchical models of data.Medical imaging has transformed healthcare science,it was thought of as a diagnostic tool for disease,but now it is also used in drug design.Advances in medical imaging technology have enabled scientists to detect events at the cellular level.The role of medical imaging in drug design includes identification of likely responders,detection,diagnosis,evaluation,therapy monitoring,and follow-up.A qualitative medical image is transformed into a quantitative biomarker or surrogate endpoint useful in drug design decision-making.For this,a parameter needs to be identified that characterizes the disease baseline and its subsequent response to treatment.The result is a quantifiable improvement in healthcare quality in most therapeutic areas,resulting in improvements in quality and life duration.This paper provides an overview of recent studies on applying the deep learning method in medical imaging and drug design.We briefly discuss the fields related to the history of deep learning,medical imaging,and drug design.

Complications and comorbidities associated with antineoplastic chemotherapy:Rethinking drug design and delivery for anticancer therapy

Despite the considerable advancements in chemotherapy as a cornerstone modality in cancer treatment,the prevalence of complications and pre-existing diseases is on the rise among cancer patients along with prolonged survival and aging population.The relationships between these disorders and cancer are intricate,bearing significant influence on the survival and quality of life of individuals with cancer and presenting challenges for the prognosis and outcomes of malignancies.Herein,we review the prevailing complications and comorbidities that often accompany chemotherapy and summarize the lessons to learn from inadequate research and management of this scenario,with an emphasis on possible strategies for reducing potential complications and alleviating comorbidities,as well as an overview of current preclinical cancer models and practical advice for establishing bio-faithful preclinical models in such complex context.

Emerging drug design strategies in anti-influenza drug discovery

Influenza is an acute respiratory infection caused by influenza viruses(IFV),According to the World Health Organization(WHO),seasonal IFV epidemics result in approximately 3-5 million cases of severe illness,leading to about half a million deaths worldwide,along with severe economic losses and social burdens.Unfortunately,frequent mutations in IFV lead to a certain lag in vaccine development as well as resistance to existing antiviral drugs.Therefore,it is of great importance to develop anti-IFV drugs with high efficiency against wild-type and resistant strains,needed in the fight against current and future outbreaks caused by different IFV strains.In this review,we summarize general strategies used for the discovery and development of antiviral agents targeting multiple IFV strains(including those resistant to available drugs).Structure-based drug design,mechanism-based drug design,multivalent interaction-based drug design and drug repurposing are amongst the most relevant strategies that provide a framework for the development of antiviral drugs targeting IFV.

Drug design based on the function of membrane transporters in drug absorption,distribution,metabolism and excretion

Membrane transporters mediate the influx and efflux of various drugs,and play essential roles in drug absorption,distribution,metabolism and excretion（ADME）.The unique characteristics of membranes transporters potentiate them as targets for developing drugs with ideal pharmacokinetics profiles,including targeted distribution,improved clinical efficacy and low adverse reaction.In this review,we summarize the tissue-specific expression,transport functions and substrates profiles of the major influx and efflux transporters,including solute carrier（SLC） superfamily and adenosine triphosphate（ATP）-binding cassette（ABC） superfamily.Moreover,we describe examples of successful drug or prodrug design based on the function of transporters that yielded drugs with excellent ADME properties.Lastly,we discuss the in vitro and in vivo methods that are broadly applied in the drug designing process to study the interactions between the drugs and the transporters.

A New Strategy in Drug Design of Chinese Medicine:Theory,Method and Techniques

The research and development （R＆D） process of Chinese medicine, with one notable feature, clinical application based, is significantly different from which of chemical and biological medicine, from laboratory research to clinics. Besides, compound prescription is another character. Therefore, according to different R＆D theories between Chinese and Western medicine, we put forward a new strategy in drug design of Chinese medicine, which focuses on ＂combination- activity relationship （CAR）＂, taking prescription discovery, component identification and formula optimization as three key points to identify the drugs of high efficacy and low toxicity. The method of drug design of Chinese medicine includes： new prescription discovery based on clinical data and literature information, component identification based on computing and experimental research, as well as formula optimization based on system modeling. This paper puts forward the concept,research framework and techniques of drug design of Chinese medicine, which embodies the R＆D model of Chinese medicine, hoping to support the drug design of Chinese medicine theoretically and technologically.

Artificial intelligence in drug design

Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials.Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence(AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening,activity scoring, quantitative structure-activity relationship(QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity(ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability,deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules,which will further promote the application of AI technologies in the field of drug design.

Structure-based protein-protein interaction networks and drug design

Proteins carry out their functions by interacting with other proteins and small molecules, forming a complex interaction network. In this review, we briefly introduce classical graph theory based protein-protein interaction networks. We also describe the commonly used experimental methods to construct these networks, and the insights that can be gained from these networks. We then discuss the recent transition from graph theory based networks to structure based protein-protein interaction networks and the advantages of the latter over the former, using two networks as examples. We further discuss the usefulness of structure based protein-protein interaction networks for drug discovery, with a special emphasis on drug repositioning.

Insight into the deamination mechanism of 6-cyclopropylamino guanosine analogs for anti-HIV drug design

Deamination is a crucial step in the transformation of 6-cyclopropylamino guanosine prodrug to its active form. A convenient method using capillary electrophoresis （CE） without sample labeling was developed to analyze the deamination of a series of D-/L-6-cyclopropylamino guanosine analogs by mouse liver homogenate, mouse liver microsome, and adenosine deaminase （ADA）. A two-step process involving a 6-amino guanosine intermediate formed by oxidative N-dealkylation was demonstrated in the metabolism of 6-cyclopropylamino guanosine to 6-hydroxy guanosine. The results indicated that the transformation rates of different prodrugs to the active form varied greatly, which were closely correlated with the configuration of nucleosides and the structure of glycosyl groups. Most importantly, D-form analogs were metabolized much faster than their L-counterparts, thus clearly pointed out that compared to guanine, modification of glycosyl part might be a better choice for the development of L-Kuanosine analogs for the treatment of HIV,

Research Progress of Structure Based Antitumor Drug Design

At present,the development of antineoplastic drugs has been highly concerned.Many strategies have been developed to explore the safety and effectiveness of antitumor drugs.In recent years,the progress of structural analysis of tumor-associated proteins provides a solid foundation for the design of new targeted drugs through efficient prediction and screening technology.In this review,we briefly summarize the research and development of new antitumor drugs based on structure to enhance tumor targeting property and reduce side effects.

Computational Modeling of Solvent Effects on Protein-Ligand Interactions Using Fully Polarizable Continuum Model and Rational Drug Design

This is a brief review of the computational modeling of protein-ligand interactions using a recently developed fully polarizable continuum model(FPCM)and rational drug design.Computational modeling has become a powerful tool in understanding detailed protein-ligand interactions at molecular level and in rational drug design.To study the binding of a protein with multiple molecular species of a ligand,one must accurately determine both the relative free energies of all of the molecular species in solution and the corresponding microscopic binding free energies for all of the molecular species binding with the protein.In this paper,we aim to provide a brief overview of the recent development in computational modeling of the solvent effects on the detailed protein-ligand interactions involving multiple molecular species of a ligand related to rational drug design.In particular,we first briefly discuss the main challenges in computational modeling of the detailed protein-ligand interactions involving the multiple molecular species and then focus on the FPCM model and its applications.The FPCM method allows accurate determination of the solvent effects in the first-principles quantum mechanism(QM)calculations on molecules in solution.The combined use of the FPCM-based QM calculations and other computational modeling and simulations enables us to accurately account for a protein binding with multiple molecular species of a ligand in solution.Based on the computational modeling of the detailed protein-ligand interactions,possible new drugs may be designed rationally as either small-molecule ligands of the protein or engineered proteins that bind/metabolize the ligand.The computational drug design has successfully led to discovery and development of promising drugs.

Algorithmic challenges in structure-based drug design and NMR structural biology

The three-dimensional structure of a biomolecule rather than its one-dimensionM sequence determines its biological function. At present, the most accurate structures are derived from experimental data measured mainly by two techniques： X-ray crystallog- raphy and nuclear magnetic resonance （NMR） spec- troscopy. Because neither X-ray crystallography nor NMR spectroscopy could directly measure the positions of atoms in a biomolecule, algorithms must be designed to compute atom coordinates from the data. One salient feature of most NMR structure computation algorithms is their reliance on stochastic search to find the lowest energy conformations that satisfy the experimentally- derived geometric restraints. However, neither the cor- rectness of the stochastic search has been established nor the errors in the output structures could be quantified. Though there exist exact algorithms to compute struc- tures from angular restraints, similar algorithms that use distance restraints remain to be developed. An important application of structures is rational drug design where protein-ligand docking plays a crit- ical role. In fact, various docking programs that place a compound into the binding site of a target protein have been used routinely by medicinal chemists for both lead identification and optimization. Unfortunately, de- spite ongoing methodological advances and some success stories, the performance of current docking algorithms is still data-dependent. These algorithms formulate the docking problem as a match of two sets of feature points. Both the selection of feature points and the search for the best poses with the minimum scores are accomplished through some stochastic search methods. Both the un- certainty in the scoring function and the limited sam- pling space attained by the stochastic search contribute to their failures. Recently, we have developed two novel docking algorithms： a data-driven docking algorithm and a general docking algorithm that does not rely on experimental data. Our algorithms search the pose space exhaustively with the pose space itself being limited to a set of hierarchical manifolds that represent, respectively, surfaces, curves and points with unique geometric and energetic properties. These algorithms promise to be es- pecially valuable for the docking of fragments and small compounds as well as for virtual screening.

Applications and prospects of cryo-EM in drug discovery

Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time-and effort-consuming. Structural biology has been demonstrated as a powerful tool to accelerate drug development. Among different techniques, cryo-electron microscopy(cryo-EM) is emerging as the mainstream of structure determination of biomacromolecules in the past decade and has received increasing attention from the pharmaceutical industry. Although cryo-EM still has limitations in resolution, speed and throughput, a growing number of innovative drugs are being developed with the help of cryo-EM. Here, we aim to provide an overview of how cryo-EM techniques are applied to facilitate drug discovery. The development and typical workflow of cryo-EM technique will be briefly introduced, followed by its specific applications in structure-based drug design, fragment-based drug discovery, proteolysis targeting chimeras, antibody drug development and drug repurposing. Besides cryo-EM, drug discovery innovation usually involves other state-of-the-art techniques such as artificial intelligence(AI), which is increasingly active in diverse areas. The combination of cryo-EM and AI provides an opportunity to minimize limitations of cryo-EM such as automation, throughput and interpretation of mediumresolution maps, and tends to be the new direction of future development of cryo-EM. The rapid development of cryo-EM will make it as an indispensable part of modern drug discovery.

Modern drug discovery for inflammatory bowel disease: The role of computational methods

Inflammatory bowel diseases(IBDs)comprising ulcerative colitis,Crohn’s disease and microscopic colitis are characterized by chronic inflammation of the gastrointestinal tract.IBD has spread around the world and is becoming more prevalent at an alarming rate in developing countries whose societies have become more westernized.Cell therapy,intestinal microecology,apheresis therapy,exosome therapy and small molecules are emerging therapeutic options for IBD.Currently,it is thought that low-molecular-mass substances with good oral bio-availability and the ability to permeate the cell membrane to regulate the action of elements of the inflammatory signaling pathway are effective therapeutic options for the treatment of IBD.Several small molecule inhibitors are being developed as a promising alternative for IBD therapy.The use of highly efficient and time-saving techniques,such as computational methods,is still a viable option for the development of these small molecule drugs.The computeraided(in silico)discovery approach is one drug development technique that has mostly proven efficacy.Computational approaches when combined with traditional drug development methodology dramatically boost the likelihood of drug discovery in a sustainable and cost-effective manner.This review focuses on the modern drug discovery approaches for the design of novel IBD drugs with an emphasis on the role of computational methods.Some computational approaches to IBD genomic studies,target identification,and virtual screening for the discovery of new drugs and in the repurposing of existing drugs are discussed.

Exploring unbinding mechanism of drugs from SERT via molecular dynamics simulation and its implication in antidepressants

The human serotonin transporter(SERT)terminates neurotransmission by removing serotonin from the synaptic cleft,which is an essential process that plays an important role in depression.In addition to natural substrate serotonin,SERT is also the target of the abused drug cocaine and,clinically used antidepressants,escitalopram,and paroxetine.To date,few studies have attempted to investigate the unbinding mechanism underlying the orthosteric and allosteric modulation of SERT.In this article,the conserved property of the orthosteric and allosteric sites(S1 and S2)of SERT was revealed by combining the high resolutions of x-ray crystal structures and molecular dynamics(MD)simulations.The residues Tyr95 and Ser438 located within the S1 site,and Arg104 located within the S2 site in SERT illustrate conserved interactions(hydrogen bonds and hydrophobic interactions),as responses to selective serotonin reuptake inhibitors.Van der Waals interactions were keys to designing effective drugs inhibiting SERT and further,electrostatic interactions highlighted escitalopram as a potent antidepressant.We found that cocaine,escitalopram,and paroxetine,whether the S1 site or the S2 site,were more competitive.According to this potential of mean force(PMF)simulations,the new insights reveal the principles of competitive inhibitors that lengths of trails from central SERT to an opening were~18A for serotonin and~22 A for the above-mentioned three drugs.Furthermore,the distance between the natural substrate serotonin and cocaine(or escitalopram)at the allosteric site was~3A.Thus,it can be inferred that the potent antidepressants tended to bind at deeper positions of the S1 or the S2 site of SERT in comparison to the substrate.Continuing exploring the processes of unbinding four ligands against the two target pockets of SERT,this study observed a broad pathway in which serotonin,cocaine,escitalopram(at the S1 site),and paroxetine all were pulled out to an opening between MT1b and MT6a,which may be helpful to understand the dissociation mechanism of antidepressants.

Screen efficiency comparisons of decision tree and neural network algorithms in machine learning assisted drug design

In view of huge search space in drug design, machine learning has become a powerful method to predict the affinity between small molecular drug and targeting protein with the development of artificial intelligence technology. However, various machine learning algorithms including massive different parameters make the prediction framework choice to be quite difficult. In this work, we took a recent drug design competition(from XtalPi company on the DataCastle platform) as the typical case to find the optimized parameters for different machines learning algorithms and the most effective algorithm. After the parameter optimizations, we compared the typical machine learning methods as decision tree(XGBoost, LightGBM) and artificial neural network(MLP, CNN) with root-mean-square error(RMSE) and coefficient of determination(R^2) evaluation. As a result, decision tree is more effective than the neural network as LightGBM>XGBoost>CNN>MLP in the affinity prediction of the specific drug design problem with ～160000 samples. For a much larger screening task in a more complicated drug design study, the sophisticated neural network model may go beyond the decision tree algorithm after generalization enhancing and overfitting reducing. The advanced machine learning methods could extract more information of protein-ligand bindings than traditional ones and improve the screen efficiency of drug design up to 200–1000 times.

Organs-on-a-Chip: A Future of Rational Drug-Design

Many recent advances in biomedical research are related to the combination of biology and microengineering. Microfluidic devices, such as organ-on-a-chip systems, integrate with living cells to allow for the detailed in vitro study of human physiology and pathophysiology. With the poor translation from animal models to human models, the organ-on-a-chip technology has become a promising substitute for animal testing, and their small scale enables precise control of culture conditions and high-throughput experiments, which would not be an economically sound model on a macroscopic level. These devices are becoming more and more common in research centers, clinics, and hospitals, and are contributing to more accurate studies and therapies, making them a staple technology for future drug design.