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.

Emerging structures and dynamic mechanisms ofγ-secretase for Alzheimer’s disease

γ-Secretase,called“the proteasome of the membrane,”is a membrane-embedded protease complex that cleaves 150+peptide substrates with central roles in biology and medicine,including amyloid precursor protein and the Notch family of cell-surface receptors.Mutations inγ-secretase and amyloid precursor protein lead to early-onset familial Alzheimer’s disease.γ-Secretase has thus served as a critical drug target for treating familial Alzheimer’s disease and the more common late-onset Alzheimer’s disease as well.However,critical gaps remain in understanding the mechanisms of processive proteolysis of substrates,the effects of familial Alzheimer’s disease mutations,and allosteric modulation of substrate cleavage byγ-secretase.In this review,we focus on recent studies of structural dynamic mechanisms ofγ-secretase.Different mechanisms,including the“Fit-Stay-Trim,”“Sliding-Unwinding,”and“Tilting-Unwinding,”have been proposed for substrate proteolysis of amyloid precursor protein byγ-secretase based on all-atom molecular dynamics simulations.While an incorrect registry of the Notch1 substrate was identified in the cryo-electron microscopy structure of Notch1-boundγ-secretase,molecular dynamics simulations on a resolved model of Notch1-boundγ-secretase that was reconstructed using the amyloid precursor protein-boundγ-secretase as a template successfully capturedγ-secretase activation for proper cleavages of both wildtype and mutant Notch,being consistent with biochemical experimental findings.The approach could be potentially applied to decipher the processing mechanisms of various substrates byγ-secretase.In addition,controversy over the effects of familial Alzheimer’s disease mutations,particularly the issue of whether they stabilize or destabilizeγ-secretase-substrate complexes,is discussed.Finally,an outlook is provided for future studies ofγ-secretase,including pathways of substrate binding and product release,effects of modulators on familial Alzheimer’s disease mutations of theγ-secretase-substrate complexes.Comprehensive understanding of the functional mechanisms ofγ-secretase will greatly facilitate the rational design of effective drug molecules for treating familial Alzheimer’s disease and perhaps Alzheimer’s disease in general.

BACE1 inhibitors:A promising therapeutic approach for the management of Alzheimer’s disease

Alzheimer’s disease is a neurological disorder marked by the accumulation of amyloid beta(Aβ)aggregates,resulting from mutations in the amyloid precursor protein.The enzymeβ-secretase,also known asβ-site amyloid precursor protein cleaving enzyme 1(BACE1),plays a crucial role in generating Aβpeptides.With no targeted therapy available for Alzheimer’s disease,inhibiting BACE1 aspartic protease has emerged as a primary treatment target.Since 1999,compounds demonstrating potential binding to the BACE1 receptor have advanced to human trials.Structural optimization of synthetically derived compounds,coupled with computational approaches,has offered valuable insights for developing highly selective leads with drug-like properties.This review highlights pivotal studies on the design and development of BACE1 inhibitors as anti-Alzheimer’s disease agents.It summarizes computational methods employed in facilitating drug discovery for potential BACE1 inhibitors and provides an update on their clinical status,indicating future directions for novel BACE1 inhibitors.The promising clinical results of Elenbecestat(E-2609)catalyze the development of effective,selective BACE1 inhibitors in the future.

One and the same androgen for all?towards designer androgens

The introduction of designer oestrogens as a treatment modality in hormone replacement in women has invited toconsider the concept of compounds with selective androgenic effects for male hormone replacement therapy. The fullspectrum of the actions of testosterone may not be necessary of even undesired for certain indications for testosteronetreatment. To define for what indications certain androgenic properties are desired and undesired more insight in basicandrogen (patho)physiology is required. There is convincing evidence that aromatization of androgenic compounds tooestrogens might be an advantage for maintenance of bone mass and it might also mitigate negative effects of androgenson biochemical parameters of cardiovascular risks; the potentially negative effects of oestrogens on prostate pathology inageing men needs further elucidation. While the role of dihydro-testosterone (DHT) for the male sexual differentiationand for pubertal sexual maturation is evident, its role in mature and ageing males seems less significant or may even beharmful. It is, however, of note that a negative effect of DHT on prostate pathophysiology is certainly not proven.For male contraception a progestational agent with strong androgenic properties might be an asset. For most of theandrogenic actions the critical levels of androgens are not well established. The latter is relevant since the large amountof androgen molecules required for its biological actions (as compared to oestrogens)is an impediment in androgenreplacement modalities. There may be room for more biopotent androgens since delivery of large amounts of androgenmolecules to the circulation poses problems for treatment modalities.

Artificial Intelligence in Pharmaceutical Sciences

Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market.However,investments in a new drug often go unrewarded due to the long and complex process of drug research and development(R&D).With the advancement of experimental technology and computer hardware,artificial intelligence(AI)has recently emerged as a leading tool in analyzing abundant and high-dimensional data.Explosive growth in the size of biomedical data provides advantages in applying AI in all stages of drug R&D.Driven by big data in biomedicine,AI has led to a revolution in drug R&D,due to its ability to discover new drugs more efficiently and at lower cost.This review begins with a brief overview of common AI models in the field of drug discovery;then,it summarizes and discusses in depth their specific applications in various stages of drug R&D,such as target discovery,drug discovery and design,preclinical research,automated drug synthesis,and influences in the pharmaceutical market.Finally,the major limitations of AI in drug R&D are fully discussed and possible solutions are proposed.

Big Data Bot with a Special Reference to Bioinformatics

There are quintillions of data on deoxyribonucleic acid(DNA)and protein in publicly accessible data banks,and that number is expanding at an exponential rate.Many scientific fields,such as bioinformatics and drug discovery,rely on such data;nevertheless,gathering and extracting data from these resources is a tough undertaking.This data should go through several processes,including mining,data processing,analysis,and classification.This study proposes software that extracts data from big data repositories automatically and with the particular ability to repeat data extraction phases as many times as needed without human intervention.This software simulates the extraction of data from web-based(point-and-click)resources or graphical user interfaces that cannot be accessed using command-line tools.The software was evaluated by creating a novel database of 34 parameters for 1360 physicochemical properties of antimicrobial peptides(AMP)sequences(46240 hits)from various MARVIN software panels,which can be later utilized to develop novel AMPs.Furthermore,for machine learning research,the program was validated by extracting 10,000 protein tertiary structures from the Protein Data Bank.As a result,data collection from the web will become faster and less expensive,with no need for manual data extraction.The software is critical as a first step to preparing large datasets for subsequent stages of analysis,such as those using machine and deep-learning applications.

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.

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.

Future of the current anticoronaviral agents: A viewpoint on thevalidation for the next COVIDs and pandemics

Despite the global decline in the severity of the coronavirus disease 2019 (COVID-19) cases, the disease stillrepresents a major concern to the relevant scientific and medical communities. The primary concern of drug scientists,virologists, and other concerned specialists in this respect is to find ready-to-use suitable and potent anticoronaviraltherapies that are broadly effective against the different species/strains of the coronaviruses in general, not only againstthe current and previous coronaviruses (e.g., the recently-appeared severe acute respiratory syndrome coronavirus 2“SARS-CoV-2”), i.e., effective antiviral agents for treatment and/or prophylaxis of any coronaviral infections, includingthose of the coming ones from the next species and strains (if any). As an expert in this field, I tried, in this up-to-dateperspective “viewpoint” article, to evaluate the suitability and applicability of using the currently-availableanticoronaviral agents for the next coronavirus diseases (COVIDs) and coronaviral pandemics, highlighting the mostimportant general guidelines that should be considered in the next pandemics from the therapeutic points of view.

Psychotic due to bath salts and methamphetamines:emergency cesarean section under general anesthesia

The use of ＂bath salts＂ or other new psychoactive substances,otherwise known as ＂legal highs＂,is increasing.Illicit drug use during pregnancy is not uncommon.Nevertheless,literature reporting bath salts and their effect on pregnancy is scant.Besides,there seems to be no literature about bath salts and conduct of general anesthesia.This case report describes a general anesthetic for the surgical delivery of an infant to a woman under the acute influence of bath salts and methamphetamines.

基于G-蛋白偶联受体激酶2抑制剂治疗心力衰竭的合理药物设计（英文）

G protein-coupled receptors(GPCRs)convert extracellular stimuli in the form of hormones,odorants and light into profound changes in cell homeostasis.Their timely desensitization is critical for cells to rapidly respond to changes in their environment and to avoid damage from sustained signaling.Seven GPCR kinases(GRKs)phosphorylate and regulate the activity of most of the^800 GPCRs in the human genome.Although GRKs normally play an adaptive role,in conditions such as chronic heart failure they are overexpressed and linked to disease progression.GRK2 and GRK5 have thus become important targets for the treatment of heart failure and pathological cardiac hypertrophy,respectively.Our lab has determined atomic structures representing all three vertebrate GRK subfamilies,and is now in the midst of a campaign to develop selective inhibitors of these enzymes using structure-based rational design.We have identified the FDA approved drug paroxetine as a selective GRK2 inhibitor,determined the crystal structure of the GRK2·paroxetine complex and,in collaboration with the Koch lab,showed that the drug improves contractility in myocytes and,most impressively,recovery in postmyocardial infarcted mice.Since then,we have identified additional chemical scaffolds that exhibit even higher potency and/or selectivity for GRK5.Using a"hybrid"inhibitor design approach we have generated GRK selective chemical probes that exhibit improved potency and stability and are able to increase inotropy and dampen the hypertrophic response in cardiomyocytes and small animal models.Structural analysis has revealed the molecular basis for selectivity and potency in many of these compounds,allowing for the design of future generations of GRK chemical probes.

Virtual Screening of Human O-GIcNAc Transferase Inhibitors

O-GlcNAc transferase （OGT） is one of essential mammalian enzymes, which catalyze the transfer of N-acetylglucosamine from UDP-N-acetylglucosamine （UDP-GlcNAc） to hydroxyl groups of serines and threonines （Ser/Thr） in proteins. Dysregulations of cellular O-GlcNAc have been implicated in diabetes, neurodegenerative disease, and cancer, which brings great interest in developing potent and specific small-molecular OGT inhibitors. In this work, we performed virtual screening on OGT catalytic site to identify potential inhibitors. 7134792 drug-like compounds from ZINC （a free database of commercially available compounds for virtual screening） and 4287550 compounds generated by FOG （fragment optimized growth program） were screened and the top 116 compounds ranked by docking score were analyzed. By comparing the screening results, we found FOG program can generate more compounds with better docking scores than ZINC. The top ZINC compounds ranked by docking score were grouped into two classes, which held the binding positions of UDP and GlcNAc of UDP- GlcNAc. Combined with individual fragments in binding pocket, de novo compounds were designed and proved to have better docking score. The screened and designed compounds may become a starting point for developing new drugs.

Design,synthesis,and evaluation of fluoroquinolone derivatives as microRNA-21 small-molecule inhibitors

MicroRNA-21(miRNA-21)is highly expressed in various tumors.Small-molecule inhibition of miRNA-21 is considered to be an attractive novel cancer therapeutic strategy.In this study,fluoroquinolone derivatives A1eA43 were synthesized and used as miRNA-21 inhibitors.Compound A36 showed the most potent inhibitory activity and specificity for miRNA-21 in a dual-luciferase reporter assay in HeLa cells.Compound A36 significantly reduced the expression of mature miRNA-21 and increased the protein expression of miRNA-21 target genes,including programmed cell death protein 4(PDCD4)and phosphatase and tensin homology deleted on chromosome ten(PTEN),at 10 μM in HeLa cells.The Cell Counting Kit-8 assay(CCK-8)was used to evaluate the antiproliferative activity of A36;the results showed that the IC_(50) value range of A36 against six tumor cell lines was between 1.76 and 13.0 μM.Meanwhile,A36 did not display cytotoxicity in BEAS-2B cells(lung epithelial cells from a healthy human donor).Furthermore,A36 significantly induced apoptosis,arrested cells at the G_(0)/G_(1) phase,and inhibited cell-colony formation in HeLa cells.In addition,mRNA deep sequencing showed that treatment with A36 could generate 171 dysregulated mRNAs in HeLa cells,while the expression of miRNA-21 target gene dual-specificity phosphatase 5(DUSP5)was significantly upregulated at both the mRNA and protein levels.Collectively,these findings demonstrated that A36 is a novel miRNA-21 inhibitor.

Utilization of kinase inhibitors as novel therapeutic drug targets:A review

Kinase inhibitors are a significant and continuously developing division of target therapeutics.The drug discovery and improvement efforts have examined numerous attempts to target the signaling pathway of kinases.The Kinase inhibitors have been heralded as a game-changer in cancer treatment.For developing kinase inhibitors as a treatment for various non-malignant disorders like auto-immune diseases,is currently undergoing extensive research.It may be beneficial to investigate whether cell-specific kinase inhibitor administration enhances therapeutic efficacy and decreases adverse effects.The goal of the current review is to gain insight into the role of kinase inhibitors in facilitating effective target drug delivery for the treatment of various anti-inflammatory,auto-immune,and anticancer disorders.The aim of this review is also to shed light on drug discovery approaches for kinase inhibitors,their mode of action,and delivery approaches.The variation in the binding of kinases bestows different target approaches in drug design,which can be employed for designing the targeted molecules.Several target sites have been studied,exceeding the design of drugs for various diseases like cancer,Alzheimer’s,rheumatoid arthritis,etc.Diverse delivery approaches have also been studied for the targeted application of kinase inhibitors.

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.

In Silico Pharmacokinetics Studies for Quinazolines Proposed as EGFR Inhibitors

In silico pharmacokinetics studies can aid the search for molecules with potential ability to be drug candidates. In this paper, a number of quinazoline candidates for epidermal growth factor receptor inhibitors—EGFR, important targets for the treatment of cancer, are computationally analyzed. The literature described that 69 quinazoline molecules were synthesized and the respective half maximum inhibitory concentrations (IC50) were obtained. A bilinear parabolic model was built to investigate the druglikeness by correlating the corresponding lipophilicities, which can be represented by the ideal Log P , with the optimal biological activity in terms of pIC50 values. Structural characteristics leading to improved pharmacokinetics parameters were then analyzed. Compound 56 exhibited the lowest IC50 and, therefore, it had the highest ability to inhibit the EGFR. In the present work, the most potent inhibitor 56 is not calculated to be the most promising drug candidate, since it’s out of the parabolic model obtained due to a Log P above 5, which is not within the expected optimum range. Finally, this work is an example of computational prediction that an experimentally, highly active EGFR inhibitor can be unsuccessful as drug candidate because of pitfalls in pharmacokinetics parameters.

Computational Analyses of Docosahexaenoic Acid (DHA, C22:6, n-3) with Alzheimer’s Disease-Causing Amyloid Peptide Aβ1-42 Reassures Its Therapeutic Utility

The accumulation of amyloid β peptide1-42 (Aβ1-42) masses in the brains of Alzheimer’s Disease (AD) patients is associated with neuronal loss and memory deficits. We have previously reported that oral administration of docosahexaenoic acid (DHA, C22:6, n-3) significantly decreases Aβ burden in the brains of AD model rats and that direct in vitro incubation of DHA with Aβ1-42 curbs the progression of amyloid fibrillation. In the present in silico study, we investigated whether DHA computationally binds with amyloid peptides. The NMR solution structures of Aβ1-42 were downloaded from the Protein Data Bank (PDB IDs: 1Z0Q and 2BEG). The binding of DHA to Aβ peptides was assessed by molecular docking using both a flexible and rigid docking system. Thioflavin T (ThT) was used as positive control. The chemical structures of ThT and DHA were modeled and converted to the PDB format using PRODRUG. Drug-like properties of DHA were evaluated by ADME (Absorption, Distribution, Metabolism, and Excretion). DHA was found to successfully dock with Aβ1-42. Computational analyses of the binding of DHA to Aβ1-42, as evaluated by docking studies, further corroborated the inhibitory effect of DHA on in vitro Aβ1-42 fibrillogenesis and might explain the in vivo reduction of amyloid burden observed in the brains of DHA-administered AD model rats demonstrated in our previous study. These computational data suggest the potential utility of DHA as a preventive medication in Aβ-induced neurodegenerative diseases, including AD.

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.

Molecular modeling of alkaloids bouchardatine and orirenierine binding to sirtuin-1(SIRT1)

Objective Bouchardatine(1)is a β-indoloquinazoline alkaloid isolated from the plant Bouchardatia neurococca,acting as a modulator of adipogenesis and lipogenesis,and as an anticancer agent.The natural product functions as an activator of proteins adenosine 5’-monophosphate(AMP)-activated protein kinase(AMPK)and sirtuin 1(SIRT1).We used molecular modeling to investigate the SIRT1-binding capacity of compound 1 and various structural analogues,such as orirenierine A(2)and orirenierine B(3)isolated from the medicinal plant Oricia renieri.Methods We investigated the binding to human SIRT1(hSIRT1)of 25 natural products including theβ-indoloquinazoline alkaloids 1−3 and analogues,in comparison with the reference product sirtinol(R and S isomers).A sirtinol binding model was elaborated starting from the closed and open state conformations of the catalytic domain of hSIRT1(PDB structures 4KXQ and 4IG9).For each compound bound to SIRT1,the empirical energy of interaction(ΔE)was calculated and compared to that of sirtinol.Results In our model,compound 1 was found to bind modestly to the sirtinol site of SIRT1.In contrast,the presence of a phenolic OH group at position 7 on the quinazolinone moiety conferred a much higher binding capacity.Compound 2 provided SIRT1 protein complexes as stable as those observed with sirtinol.The replacement of the hydroxy substituent(2)with a methoxy group(3)reduced the SIRT1 binding capacity.Other SIRT1-binding natural products were identified,such as the alkaloids orisuaveolines A and B.Structure-binding relationships were discussed.Conclusion The study underlines the capacity of β-indoloquinazoline alkaloids to interact with SIRT1.This deacetylase enzyme could represent a molecular target for the alkaloid 2.This compound merits further attention for the design of drugs active against SIRT1-dependent pathologies.

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.