Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classification.

Nanoparticles (NPs) are considered as versatile tools in various fields including medicine, electronics, and environmental science. Understanding the structural aspects of surface modifiers in nanoparticles that govern their cellular uptake is crucial for optimizing their efficacy and minimizing potential cytotoxicity. The cellular uptake is influenced by multiple factors, namely, size, shape, and surface charge of NPs, as well as their surface functionalization. In the current study, classification-based ML models (i.e., Bayesian classification, random forest, support vector classifier, and linear discriminant analysis) have been developed to identify the features/fingerprints that significantly contribute to the cellular uptake of ENMOs in multiple cell types, including pancreatic cancer cells (PaCa2), human endothelial cells (HUVEC), and human macrophage cells (U937). The best models have been identified for each cell type and analyzed to detect the structural fingerprints/features governing the cellular uptake of ENMOs. The study will direct scientists in the design of ENMOs of higher cellular uptake efficiency for better therapeutic response.

Derivatives of D(-) glutamine-based MMP-2 inhibitors as an effective remedy for the management of chronic myeloid leukemia-Part-I: Synthesis, biological screening and in silico binding interaction analysis.

Chronic myeloid leukemia (CML) is a global issue and the available drugs such as tyrosine kinase inhibitors (TKIs) comprise various toxic effects as well as resistance and cross-resistance. Therefore, novel molecules targeting specific enzymes may unravel a new direction in antileukemic drug discovery. In this context, targeting gelatinases (MMP-2 and MMP-9) can be an alternative option for the development of novel molecules effective against CML. In this article, some D(-)glutamine derivatives were synthesized and evaluated through cell-based antileukemic assays and tested against gelatinases. The lead compounds, i.e., benzyl analogs exerted the most promising antileukemic potential showing nontoxicity in normal cell line including efficacious gelatinase inhibition. Both these lead molecules yielded effective apoptosis and displayed marked reductions in MMP-2 expression in the K562 cell line. Not only that, but both of them also revealed effective antiangiogenic efficacy. Importantly, the most potent MMP-2 inhibitor, i.e., benzyl derivative of p-tosyl D(-)glutamine disclosed stable binding interaction at the MMP-2 active site correlating with the highly effective MMP-2 inhibitory activity. Therefore, such D(-)glutamine derivatives might be explored further as promising MMP-2 inhibitors with efficacious antileukemic profiles for the treatment of CML in the future.

Identification of structural fingerprints among natural inhibitors of HDAC1 to accelerate nature-inspired drug discovery in cancer epigenetics.

Histone deacetylase 1 (HDAC1), a class I HDAC enzyme, is crucial for histone modification. Currently, it is emerged as one of the important biological targets for designing small molecule drugs through cancer epigenetics. Along with synthetic inhibitors different natural inhibitors are showing potential HDAC1 inhibitions. In order to gain insights into the relationship between the molecular structures of the natural inhibitors and HDAC1, different molecular modelling techniques (Bayesian classification, recursive partitioning, molecular docking and molecular dynamics simulations) have been applied on a dataset of 155 HDAC1 nature-inspired inhibitors with diverse scaffolds. The Bayesian study showed acceptable ROC values for both the training set and test sets. The Recursive partitioning study produced decision tree 1 with 6 leaves. Further, molecular docking study was processed for generating the protein ligand complex which identified some potential amino acid residues such as F205, H28, L271, P29, F150, Y204 for the binding interactions in case of natural inhibitors. Stability of these HDAC1-natutal inhibitors complexes has been also evaluated by molecular dynamics simulation study. The current modelling study is an attempt to get a deep insight into the different important structural fingerprints among different natural compounds modulating HDAC1 inhibition.Communicated by Ramaswamy H. Sarma.

Unraveling HDAC11: Epigenetic orchestra in different diseases and structural insights for inhibitor design.

Histone deacetylase 11 (HDAC11), a member of the HDAC family, has emerged as a critical regulator in numerous physiological as well as pathological processes. Due to its diverse roles, HDAC11 has been a focal point of research in recent times. Different non-selective inhibitors are already approved, and research is going on to find selective HDAC11 inhibitors. The objective of this review is to comprehensively explore the role of HDAC11 as a pivotal regulator in a multitude of physiological and pathological processes. It aims to delve into the intricate details of HDAC11's structural and functional aspects, elucidating its molecular interactions and implications in different disease contexts. With a primary focus on elucidating the structure-activity relationships (SARs) of HDAC11 inhibitors, this review also aims to provide a holistic understanding of how its molecular architecture influences its inhibition. Additionally, by integrating both established knowledge and recent research, the review seeks to contribute novel insights into the potential therapeutic applications of HDAC11 inhibitors. Overall, the scope of this review spans from fundamental research elucidating the complexities of HDAC11 biology to the potential of targeting HDAC11 in therapeutic interventions.

Density functional theory (DFT) studies in HDAC-based chemotherapeutics: Current findings, case studies and future perspectives.

Density Functional Theory (DFT) is a quantum chemical computational method used to predict and analyze the electronic properties of atoms, molecules, and solids based on the density of electrons rather than wavefunctions. It provides insights into the structure, bonding, and behavior of different molecules, including those involved in the development of chemotherapeutic agents, such as histone deacetylase inhibitors (HDACis). HDACs are a wide group of metalloenzymes that facilitate the removal of acetyl groups from acetyl-lysine residues situated in the N-terminal tail of histones. Abnormal HDAC recruitment has been linked to several human diseases, especially cancer. Therefore, it has been recognized as a prospective target for accelerating the development of anticancer therapies. Researchers have studied HDACs and its inhibitors extensively using a combination of experimental methods and diverse in-silico approaches such as machine learning and quantitative structure-activity relationship (QSAR) methods, molecular docking, molecular dynamics, pharmacophore mapping, and more. In this context, DFT studies can make significant contribution by shedding light on the molecular properties, interactions, reaction pathways, transition states, reactivity and mechanisms involved in the development of HDACis. This review attempted to elucidate the scope in which DFT methodologies may be used to enhance our comprehension of the molecular aspects of HDAC inhibitors, aiding in the rational design and optimization of these compounds for therapeutic applications in cancer and other ailments. The insights gained can guide experimental efforts toward developing more potent and selective HDAC inhibitors.

Exploration of Fingerprints and Data Mining-based Prediction of Some Bioactive Compounds from Allium sativum as Histone Deacetylase 9 (HDAC9) Inhibitors.

BACKGROUND: Histone deacetylase 9 (HDAC9) is an important member of the class IIa family of histone deacetylases. It is well established that over-expression of HDAC9 causes various types of cancers including gastric cancer, breast cancer, ovarian cancer, liver cancer, lung cancer, lymphoblastic leukaemia, etc. The important role of HDAC9 is also recognized in the development of bone, cardiac muscles, and innate immunity. Thus, it will be beneficial to find out the important structural attributes of HDAC9 inhibitors for developing selective HDAC9 inhibitors with higher potency. METHODS: The classification QSAR-based methods namely Bayesian classification and recursive partitioning method were applied to a dataset consisting of HADC9 inhibitors. The structural features strongly suggested that sulphur-containing compounds can be a good choice for HDAC9 inhibition. For this reason, these models were applied further to screen some natural compounds from Allium sativum. The screened compounds were further accessed for the ADME properties and docked in the homology-modelled structure of HDAC9 in order to find important amino acids for the interaction. The best-docked compound was considered for molecular dynamics (MD) simulation study. RESULTS: The classification models have identified good and bad fingerprints for HDAC9 inhibition. The screened compounds like ajoene, 1,2 vinyl dithiine, diallyl disulphide and diallyl trisulphide had been identified as compounds having potent HDAC9 inhibitory activity. The results from ADME and molecular docking study of these compounds show the binding interaction inside the active site of the HDAC9. The best-docked compound ajoene shows satisfactory results in terms of different validation parameters of MD simulation study. CONCLUSION: This in-silico modelling study has identified the natural potential lead (s) from Allium sativum. Specifically, the ajoene with the best in-silico features can be considered for further in-vitro and in-vivo investigation to establish as potential HDAC9 inhibitors.

HDAC9 as a Privileged Target: Reviewing its Role in Different Diseases and Structure-activity Relationships (SARs) of its Inhibitors.

HDAC9 is a histone deacetylase enzyme belonging to the class IIa of HDACs which catalyses histone deacetylation. HDAC9 inhibit cell proliferation by repairing DNA, arresting the cell cycle, inducing apoptosis, and altering genetic expression. HDAC9 plays a significant part in human physiological system and are involved in various type of diseases like cancer, diabetes, atherosclerosis and CVD, autoimmune response, inflammatory disease, osteoporosis and liver fibrosis. This review discusses the role of HDAC9 in different diseases and structure-activity relationships (SARs) of various hydroxamate and non-hydroxamate-based inhibitors. SAR of compounds containing several scaffolds have been discussed in detail. Moreover, structural requirements regarding the various components of HDAC9 inhibitor (cap group, linker and zinc-binding group) has been highlighted in this review. Though, HDAC9 is a promising target for the treatment of a number of diseases including cancer, a very few research are available. Thus, this review may provide useful information for designing novel HDAC9 inhibitors to fight against different diseases in the future.

Are inhibitors of histone deacetylase 8 (HDAC8) effective in hematological cancers especially acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL)?

Histone deacetylase 8 (HDAC8) aberrantly deacetylates histone and non-histone proteins. These include structural maintenance of chromosome 3 (SMC3) cohesin protein, retinoic acid induced 1 (RAI1), p53, etc and thus, regulating diverse processes such as leukemic stem cell (LSC) transformation and maintenance. HDAC8, one of the crucial HDACs, affects the gene silencing process in solid and hematological cancer progressions especially on acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). A specific HDAC8 inhibitor PCI-34051 showed promising results against both T-cell lymphoma and AML. Here, we summarize the role of HDAC8 in hematological malignancies, especially in AML and ALL. This article also introduces the structure/function of HDAC8 and a special attention has been paid to address the HDAC8 enzyme selectivity issue in hematological cancer especially against AML and ALL.

Interaction of systemic drugs causing ocular toxicity with organic cation transporter: an artificial intelligence prediction.

Chronic disease patients (cancer, arthritis, cardiovascular diseases) undergo long-term systemic drug treatment. Membrane transporters in ocular barriers could falsely recognize these drugs and allow their trafficking into the eye from systemic circulation. Hence, despite their pharmacological activity, these drugs accumulate and cause toxicity at the non-target site, such as the eye. Since around 40% of clinically used drugs are organic cation in nature, it is essential to understand the role of organic cation transporter (OCT1) in ocular barriers to facilitate the entry of systemic drugs into the eye. We applied machine learning techniques and computer simulation models (molecular dynamics and metadynamics) in the current study to predict the potential OCT1 substrates. Artificial intelligence models were developed using a training dataset of a known substrates and non-substrates of OCT1 and predicted the potential OCT1 substrates from various systemic drugs causing ocular toxicity. Computer simulation studies was performed by developing the OCT1 homology model. Molecular dynamic simulations equilibrated the docked protein-ligand complex. And metadynamics revealed the movement of substrates across the transporter with minimum free energy near the binding pocket. The machine learning model showed an accuracy of about 80% and predicted the potential substrates for OCT1 among systemic drugs causing ocular toxicity - not known earlier, such as cyclophosphamide, bupivacaine, bortezomib, sulphanilamide, tosufloxacin, topiramate, and many more. However, further invitro and invivo studies are required to confirm these predictions.Communicated by Ramaswamy H. Sarma.

Exploring structural requirements of HDAC10 inhibitors through comparative machine learning approaches.

Histone deacetylase (HDAC) inhibitors are in the limelight of anticancer drug development and research. HDAC10 is one of the class-IIb HDACs, responsible for cancer progression. The search for potent and effective HDAC10 selective inhibitors is going on. However, the absence of human HDAC10 crystal/NMR structure hampers the structure-based drug design of HDAC10 inhibitors. Different ligand-based modeling techniques are the only hope to speed up the inhibitor design. In this study, we applied different ligand-based modeling techniques on a diverse set of HDAC10 inhibitors (n = 484). Machine learning (ML) models were developed that could be used to screen unknown compounds as HDAC10 inhibitors from a large chemical database. Moreover, Bayesian classification and Recursive partitioning models were used to identify the structural fingerprints regulating the HDAC10 inhibitory activity. Additionally, a molecular docking study was performed to understand the binding pattern of the identified structural fingerprints towards the active site of HDAC10. Overall, the modeling insight might offer helpful information for medicinal chemists to design and develop efficient HDAC10 inhibitors.

A review of MMP-2 structures and binding mode analysis of its inhibitors to strategize structure-based drug design.

The protease enzyme, matrix metalloproteinase-2 (MMP-2) has been a target of choice for the drug development due to its multi-façade involvement in numerous diseased conditions including cancer. To find a selective MMP-2 inhibitor several computational strategies are employed in its design and discovery. In these strategies, protein structure of MMP-2 is an inevitable part to formulate effective structure-based drug design (SBDD) of selective MMP-2 inhibitors. In the present communication, several crystal structures of MMP-2 have been analyzed with different statistical parameters and their implementations in SBDD of inhibitors are scrutinized. In addition, binding mode analyses of various classes of inhibitors are discussed to pinpoint the effective design of selective inhibitors by maximizing its interaction with the MMP-2 enzyme binding site. This may provide a crucial insight for exploring the numerous possibilities for SBDD of MMP-2 inhibitors to accelerate anticancer drug discovery efforts.

Recent trends in fragment-based anticancer drug design strategies against different targets: A mini-review.

Cancer is a rapidly growing disease in modern society. Chemotherapy is the first choice for cancer treatment. Design and development of new chemotherapeutic drugs by targeting specific proteins are put down by a high attrition rate at different stages. Fragment-based drug design (FBDD) is one of the successful structure-based drug design processes to avoid attrition-related problems. This review highlighted the computational and experimental FBDD techniques used to design molecules with anticancer properties. This study describes FBBD strategies for different targets like aurora kinase, phosphoinositide-dependent protein kinase-1 (PDK1), signal transducer and activator of transcription 3 (STAT3), myeloid cell leukemia-1 (Mcl-1), tankyrase (TNKS), choline kinase, protein kinase, tyrosine kinase and lysine-specific demethylase 1 (LSD1) which are vital targets for cancer treatments. This review will enrich the scientific community to understand the fragment-based design strategies for finding suitable leads over high throughput screening (HTS) in the future.

Inonotus obliquus aqueous extract prevents histopathological alterations in liver induced by environmental toxicant Microcystin.

Environmental toxicants like microcystins are known to adversely impact liver physiology and lead to the increased risk for abnormal liver function and even liver carcinoma. Chaga mushroom (Inonotus obliquus) is reported for various properties mainly antibacterial, antiallergic, anti-inflammatory, antioxidant, and anticancer properties. This study was aimed to assess the effect microcystin (MC-LR) on histopathology of liver in mice and a preventive measure by using aqueous extract of Inonotus obliquus (IOAE). Adult Balb/c mice were administered with MC-LR at 20 â€‹µg/kg body weight, per day, intraperitoneal (i.p.) for 4 weeks. IOAE was treated to one group of MC-LR mice at 200 â€‹mg/kg body weight, per oral, for 4 weeks. Histological staining for liver structural details and biochemical assays for functions were assessed. The results of the study showed that MC-LR drastically reduced the body weight of mice which were restored close to the range of control by IOAE treatment. MC-LR exposed mice showed 1.9, 1.7 and 2.2-fold increase in the levels of SGOT, SGPT and LDH which were restored by IOAE treatment as compared to control (one-fold). MC-LR exposed mice showed reduced level of GSH (19.83 â€‹± â€‹3.3 â€‹µM) which were regained by IOAE treatment (50.83 â€‹± â€‹3.0 â€‹µM). Similar observations were noted for catalase activity. Histological examinations show that MC-LR exposed degenerative changes in the liver sections which were restored by IOAE supplementation. The immunofluorescence analysis of caspase-3 counterstained with DAPI showed that MC-LR led to the increased expression of caspase-3 which were comparatively reduced by IOAE treatment. The cell viability decreased on increasing the concentration of MC-LR with 5% cell viability at concentration of 10 â€‹µg MC-LR/mL as that of control 100% Cell viability. The IC50 was calculated to be 3.6 â€‹µg/ml, indicating that MC-LR is chronic toxic to AML12 mouse hepatocytes. The molecular docking interaction of NF-κB-NIK with ergosterol peroxidase showed binding interaction between the two and showed the plausible molecular basis for the effects of IOAE in MC-LR induced liver injury. Collectively, this study revealed the deleterious effects of MC-LR on liver through generation of oxidative stress and activation of caspase-3, which were prevented by treatment with IOAE.

Updated chemical scaffolds of ABCG2 inhibitors and their structure-inhibition relationships for future development.

ATP-binding cassette (ABC) transporters are pivotal for cell detoxification and survival. Overexpression of ABC transporter in tumor cells lead to chemoresistance through the efflux of chemotherapeutic agents. P-glycoprotein (Pgp/ABCB1), multidrug resistance protein 1 (MRP1/ABCC1) and breast cancer resistance protein (BCRP/ABCG2) are the major ABC transporters involved in multidrug resistance (MDR) of cancer cells against anticancer drugs. ABCG2 is one of the major transporters involved in the efflux of different cytotoxic agents. Hence, inhibition of ABCG2-mediated transport is considered a prime target to resist MDR of cancer cells. Here, brief structural biology and functions of ABCG2 were discussed with the aim to identify key pharmacophoric elements to design potent and selective as well as non-toxic ABCG2 inhibitors. Structure-inhibition relationships (SIRs) of the earlier reported compounds were also explored. Taken together, this study offers insight for further development of ABCG2 inhibitors.

How the structural properties of the indole derivatives are important in kinase targeted drug design?: A case study on tyrosine kinase inhibitors.

Kinases are considered as important signalling enzymes that illustrate 20% of the druggable genome. Human kinase family comprises >500 protein kinases and about 20 lipid kinases. Protein kinases are responsible for the mechanism of protein phosphorylation. These are necessary for regulation of various cellular activities including proliferation, cell cycle, apoptosis, motility, growth, differentiation, etc. Their deregulation leads to disruption of many cellular processes leading to different diseases most importantly cancer. Thus, kinases are considered as valuable targets in different types of cancer as well as other diseases. Researchers around the world are actively engaged in developing inhibitors based on distinct chemical scaffolds. Indole represents as a versatile scaffold in the naturally occurring and bioactive molecules. It is also used as a privileged scaffold for the target-based drug design against different diseases. This present article aim to review the applications of indole scaffold in the design of inhibitors against different tyrosine kinases such as epidermal growth factor receptors (EGFRs), vascular endothelial growth factor receptors (VEGFRs), platelet-derived growth factor receptors (PDGFRs), etc. Important structure activity relationships (SARs) of indole derivatives were discussed. The present work is an attempt to summarize all the crucial structural information which is essential for the development of indole based tyrosine kinase inhibitors with improved potency.

Exploring sodium glucose cotransporter (SGLT2) inhibitors with machine learning approach: A novel hope in anti-diabetes drug discovery.

Conventional anti-diabetes agents exhibit some undesirable side effects. Recently, lactic acidosis and/or bladder cancer were also reported with the use of these agents. Hence, there is an urgent need for alternative anti-diabetes in order to reduce/avoid the unwanted effects. In this scenario sodium glucose cotransporter 2 (SGLT2) inhibitors has already been established as an important class of anti-diabetic drug. The search for new generation SGLT2 inhibitors with high affinity is still an ongoing process. Here, we aim to develop computational models to predict the SGLT2 inhibitory activity of small molecules based on chemical structures. This work provides in-silico analysis to propose possible fragment/fingerprint identification recommended for SGLT2 inhibitors. Up-to-our knowledge, this study is an initiative to propose fingerprints responsible for SGLT2 inhibition. Furthermore, we used nine different algorithms to build machine learning (ML) models that could be used to prioritize compounds as SGLT2 inhibitors from large libraries. The best performing ML models were applied to virtually screen a large collection of FDA approved drugs. The best predicted compounds have been recommended to be biologically investigated in future in order to identify next generation SGLT2 inhibitors with different chemical structure.

Identification of molecular fingerprints of natural products for the inhibition of breast cancer resistance protein (BCRP).

BACKGROUND: Extensive research over the past several decades, explored that the natural compounds contain different plant secondary metabolites and have the potential to inhibit breast cancer resistance protein (BCRP). PURPOSE: To identify crucial molecular fingerprints of some natural products for the inhibition of breast cancer resistance protein and also to screen out some potent natural BCRP inhibitors. STUDY DESIGN: Multiple modelling strategies were applied with three main mottos: (a) Generation of robust classification models to identify the linear and non-linear relationships among the natural compounds and the inhibition of BCRP, (b) Identification of important structural fingerprints that modulate BCRP inhibition and screening of natural database to find the probable hit molecules, (c) Comprehensive ligand-receptor interactions analysis of those against the putative breast cancer resistant protein through molecular docking analysis. METHODS: Monte Carlo optimization and SPCI analysis was used to identify important structural fingerprints. QSARCo. and swissADME analysis were used for screening and prediction of hits. Finally, docking analysis was performed for interaction study. RESULTS: In this study, some important structural fingerprints of BCRP inhibitors were identified. Additionally, eleven natural anti-cancer compounds were predicted to be active against the BCRP and also satisfy the different drug-likeliness properties. Among them, apigenin was found to have better binding affinities against the putative target as obtained from molecular docking analysis. CONCLUSION: This study is an attempt to understand about the molecular fingerprints of natural compounds for the inhibition of BCRP and also to dig out some novel natural inhibitors against BCRP.

Protease targeted COVID-19 drug discovery: What we have learned from the past SARS-CoV inhibitors?

The fascinating similarity between the SARS-CoV and SARS-CoV-2, inspires scientific community to investigate deeper into the SARS-CoV proteases such as main protease (Mpro) and papain-like protease (PLpro) and their inhibitors for the discovery of SARS-CoV-2 protease inhibitors. Because of the similarity in the proteases of these two corona viruses, there is a greater chance for the previous SARS-CoV Mpro and PLpro inhibitors to provide effective results against SARS-CoV-2. In this context, the molecular fragments from the SARS-CoV protease inhibitors through the fragment-based drug design and discovery technique can be useful guidance for COVID-19 drug discovery. Here, we have focused on the structure-activity relationship studies of previous SARS-CoV protease inhibitors and discussed about crucial fragments generated from previous SARS-CoV protease inhibitors important for the lead optimization of SARS-CoV-2 protease inhibitors. This study surely offers different strategic options of lead optimization to the medicinal chemists to discover effective anti-viral agent against the devastating disease, COVID-19.

Protease targeted COVID-19 drug discovery and its challenges: Insight into viral main protease (Mpro) and papain-like protease (PLpro) inhibitors.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) brutally perils physical and mental health worldwide. Unavailability of effective anti-viral drug rendering global threat of COVID-19 caused by SARS-CoV-2. In this scenario, viral protease enzymes are crucial targets for drug discovery. This extensive study meticulously focused on two viral proteases such as main protease (Mpro) and papain-like protease (PLpro), those are essential for viral replication. This review provides a detail overview of the targets (Mpro and PLpro) from a structural and medicinal chemistry point of view, together with recently reported protease inhibitors. An insight into the challenges in the development of effective as well as drug like protease inhibitors is discussed. Peptidomimetic and/or covalent coronavirus protease inhibitors possessed potent and selective active site inhibition but compromised in pharmacokinetic parameters to be a drug/drug like molecule. Lead optimization of non-peptidomimetic and/or low molecular weight compounds may be a better option for oral delivery. A masterly combination of adequate pharmacokinetic properties with coronavirus protease activity as well as selectivity will provide potential drug candidates in future. This study is a part of our endeavors which surely dictates medicinal chemistry efforts to discover effective anti-viral agent for this devastating disease.

Exploration of good and bad structural fingerprints for inhibition of indoleamine-2,3-dioxygenase enzyme in cancer immunotherapy using Monte Carlo optimization and Bayesian classification QSAR modeling.

Indoleamine-2,3-dioxygenase 1 (IDO1) is an extrahepatic, heme-containing and tryptophan-catalyzing enzyme responsible for causing blockade of T-cell proliferation and differentiation by depleting tryptophan level in cancerous cells. Therefore, inhibition of IDO1 may be a useful strategy for immunotherapy against cancer. In this study, 448 structurally diverse IDO1 inhibitors with a wide range of activity has been taken into consideration for classification QSAR analysis through Monte Carlo Optimization by using different splits as well as different combinations of SMILES-based, graph-based and hybrid descriptors. The best model from Monte Carlo optimization was interpreted to find out the good and bad structural fingerprints for IDO1 and further justified by using Bayesian classification QSAR modeling. Among the three splits in Monte Carlo optimization, the statistics of the best model was obtained from Split 3: sensitivity = 0.87, specificity = 0.91, accuracy = 0.89 and MCC = 0.78. In Bayesian classification modeling, the ROC scores for training and test set were found to be 0.91 and 0.86, respectively. The combined modeling analysis revealed that the presence of aryl hydrazyl sulphonyl moiety, furazan ring, halogen substitution, nitro group and hetero atoms in aromatic system can be very useful in designing IDO1 inhibitors. All the good and bad structural fingerprints for IDO1 were identified and are justified by correlating these fragments to the inhibition of IDO1 enzyme. These structural fingerprints will guide the researchers in this field to design better inhibitors against IDO1 enzyme for cancer immunotherapy.Communicated by Ramaswamy H. Sarma.