SAP30, an oncogenic driver of progression, poor survival, and drug resistance in neuroblastoma.

Neuroblastoma is the most devastating extracranial solid malignancy in children. Despite an intense treatment regimen, the prognosis for high-risk neuroblastoma patients remains poor, with less than 40% survival. So far, MYCN amplification status is considered the most prognostic factor but corresponds to only ∼25% of neuroblastoma patients. Therefore, it is essential to identify a better prognosis and therapy response marker in neuroblastoma patients. We applied robust bioinformatic data mining tools, such as weighted gene co-expression network analysis, cisTarget, and single-cell regulatory network inference and clustering on two neuroblastoma patient datasets. We found Sin3A-associated protein 30 (SAP30), a driver transcription factor positively associated with high-risk, progression, stage 4, and poor survival in neuroblastoma patient cohorts. Tumors of high-risk neuroblastoma patients and relapse-specific patient-derived xenografts showed higher SAP30 levels. The advanced pharmacogenomic analysis and CRISPR-Cas9 screens indicated that SAP30 essentiality is associated with cisplatin resistance and further showed higher levels in cisplatin-resistant patient-derived xenograft tumor cell lines. Silencing of SAP30 induced cell death in vitro and led to a reduced tumor burden and size in vivo. Altogether, these results indicate that SAP30 is a better prognostic and cisplatin-resistance marker and thus a potential drug target in high-risk neuroblastoma.

Immune checkpoint molecules in neuroblastoma: A clinical perspective.

High-risk neuroblastoma (NB) is challenging to treat with 5-year long-term survival in patients remaining below 50% and low chances of survival after tumor relapse or recurrence. Different strategies are being tested or under evaluation to destroy resistant tumors and improve survival outcomes in NB patients. Immunotherapy, which uses certain parts of a person's immune system to recognize or kill tumor cells, effectively improves patient outcomes in several types of cancer, including NB. One of the immunotherapy strategies is to block immune checkpoint signaling in tumors to increase tumor immunogenicity and anti-tumor immunity. Immune checkpoint proteins put brakes on immune cell functions to regulate immune activation, but this activity is exploited in tumors to evade immune surveillance and attack. Immune checkpoint proteins play an essential role in NB biology and immune escape mechanisms, which makes these tumors immunologically cold. Therapeutic strategies to block immune checkpoint signaling have shown promising outcomes in NB but only in a subset of patients. However, combining immune checkpoint blockade with other therapies, including conjugated antibody-based immunotherapy, radioimmunotherapy, tumor vaccines, or cellular therapies like modified T or natural killer (NK) cells, has shown encouraging results in enhancing anti-tumor immunity in the preclinical setting. An analysis of publicly available dataset using computational tools has unraveled the complexity of multiple cancer including NB. This review comprehensively summarizes the current information on immune checkpoint molecules, their biology, role in immune suppression and tumor development, and novel therapeutic approaches combining immune checkpoint inhibitors with other therapies to combat high-risk NB.

miR-15a and miR-15b modulate natural killer and CD8+T-cell activation and anti-tumor immune response by targeting PD-L1 in neuroblastoma.

Neuroblastoma (NB) is an enigmatic and deadliest pediatric cancer to treat. The major obstacles to the effective immunotherapy treatments in NB are defective immune cells and the immune evasion tactics deployed by the tumor cells and the stromal microenvironment. Nervous system development during embryonic and pediatric stages is critically mediated by non-coding RNAs such as micro RNAs (miR). Hence, we explored the role of miRs in anti-tumor immune response via a range of data-driven workflows and in vitro & in vivo experiments. Using the TARGET, NB patient dataset (n=249), we applied the robust bioinformatic workflows incorporating differential expression, co-expression, survival, heatmaps, and box plots. We initially demonstrated the role of miR-15a-5p (miR-15a) and miR-15b-5p (miR-15b) as tumor suppressors, followed by their negative association with stromal cell percentages and a statistically significant negative regulation of T and natural killer (NK) cell signature genes, especially CD274 (PD-L1) in stromal-low patient subsets. The NB phase-specific expression of the miR-15a/miR-15b-PD-L1 axis was further corroborated using the PDX (n=24) dataset. We demonstrated miR-15a/miR-15b mediated degradation of PD-L1 mRNA through its interaction with the 3'-untranslated region and the RNA-induced silencing complex using sequence-specific luciferase activity and Ago2 RNA immunoprecipitation assays. In addition, we established miR-15a/miR-15b induced CD8+T and NK cell activation and cytotoxicity against NB in vitro. Moreover, injection of murine cells expressing miR-15a reduced tumor size, tumor vasculature and enhanced the activation and infiltration of CD8+T and NK cells into the tumors in vivo. We further established that blocking the surface PD-L1 using an anti-PD-L1 antibody rescued miR-15a/miR-15b induced CD8+T and NK cell-mediated anti-tumor responses. These findings demonstrate that miR-15a and miR-15b induce an anti-tumor immune response by targeting PD-L1 in NB.

The emerging role of non-coding RNAs in the epigenetic regulation of pediatric cancers.

Epigenetics is a process that involves the regulation of gene expression without altering the sequence of DNA. Numerous studies have documented that epigenetic mechanisms play a critical role in cell growth, differentiation, and cancer over the past decade. The well-known epigenetic modifications are either on DNA or at the histone proteins. Although several studies have focused on regulating gene expression by non-coding RNAs, the current understanding of their biological functions in various human diseases, particularly in cancers, is inadequate. Only about two percent of DNA is involved in coding the protein-coding genes, and leaving the rest 98 percent is non-coding and the scientific community regarded as junk or noise with no known purpose. Most non-coding RNAs are derived from such junk DNA and are known to be involved in various signaling pathways involving cancer initiation, progression, and the development of therapy resistance in many human cancer types. Recent studies have suggested that non-coding RNAs, especially microRNAs, piwi-interactingRNAs, and long non-coding RNAs, play a significant role in controlling epigenetic mechanism(s), indicating the potential effect of epigenetic modulation of non-coding RNAs on cancer progression. In this review article, we briefly presented epigenetic marks' characteristics, crosstalk between epigenetic modifications and microRNAs, piwi-interactingRNAs, and long non-coding RNAs to uncover the effect on the phenotype of pediatric cancers. Further, current knowledge on understanding the RNA epigenetics will help design novel therapeutics that target epigenetic regulatory networks to benefit cancer patients in the clinic.

New insights into exosome mediated tumor-immune escape: Clinical perspectives and therapeutic strategies.

Recent advances in extracellular vesicle biology have uncovered a substantial role in maintaining cell homeostasis in health and disease conditions by mediating intercellular communication, thus catching the scientific community's attention worldwide. Extracellular microvesicles, some called exosomes, functionally transfer biomolecules such as proteins and non-coding RNAs from one cell to another, influencing the local environment's biology. Although numerous advancements have been made in treating cancer patients with immune therapy, controlling the disease remains a challenge in the clinic due to tumor-driven interference with the immune response and inability of immune cells to clear cancer cells from the body. The present review article discusses the recent findings and knowledge gaps related to the role of exosomes derived from tumors and the tumor microenvironment cells in tumor escape from immunosurveillance. Further, we highlight examples where exosomal non-coding RNAs influence immune cells' response within the tumor microenvironment and favor tumor growth and progression. Therefore, exosomes can be used as a therapeutic target for the treatment of human cancers.

COVID-19 and Cancer Comorbidity: Therapeutic Opportunities and Challenges.

The coronavirus disease 2019 (COVID-19) is a viral disease caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that affects the respiratory system of infected individuals. COVID-19 spreads between humans through respiratory droplets produced when an infected person coughs or sneezes. The COVID-19 outbreak originated in Wuhan, China at the end of 2019. As of 29 Sept 2020, over 235 countries, areas or territories across the globe reported a total of 33,441,919 confirmed cases, and 1,003,497 confirmed deaths due to COVID-19. Individuals of all ages are at risk for infection, but in most cases disease severity is associated with age and pre-existing diseases that compromise immunity, like cancer. Numerous reports suggest that people with cancer can be at higher risk of severe illness and related deaths from COVID-19. Therefore, managing cancer care under this pandemic is challenging and requires a collaborative multidisciplinary approach for optimal care of cancer patients in hospital settings. In this comprehensive review, we discuss the impact of the COVID-19 pandemic on cancer patients, their care, and treatment. Further, this review covers the SARS-CoV-2 pandemic, genome characterization, COVID-19 pathophysiology, and associated signaling pathways in cancer, and the choice of anticancer agents as repurposed drugs for treating COVID-19.

Amino Acids Regulate Cisplatin Insensitivity in Neuroblastoma.

Neuroblastoma are pediatric, extracranial malignancies showing alarming survival prognosis outcomes due to their resilience to current aggressive treatment regimens, including chemotherapies with cisplatin (CDDP) provided in the first line of therapy regimens. Metabolic deregulation supports tumor cell survival in drug-treated conditions. However, metabolic pathways underlying cisplatin-resistance are least studied in neuroblastoma. Our metabolomics analysis revealed that cisplatin-insensitive cells alter their metabolism; especially, the metabolism of amino acids was upregulated in cisplatin-insensitive cells compared to the cisplatin-sensitive neuroblastoma cell line. A significant increase in amino acid levels in cisplatin-insensitive cells led us to hypothesize that the mechanisms upregulating intracellular amino acid pools facilitate insensitivity in neuroblastoma. We hereby report that amino acid depletion reduces cell survival and cisplatin-insensitivity in neuroblastoma cells. Since cells regulate their amino acids levels through processes, such as autophagy, we evaluated the effects of hydroxychloroquine (HCQ), a terminal autophagy inhibitor, on the survival and amino acid metabolism of cisplatin-insensitive neuroblastoma cells. Our results demonstrate that combining HCQ with CDDP abrogated the amino acid metabolism in cisplatin-insensitive cells and sensitized neuroblastoma cells to sub-lethal doses of cisplatin. Our results suggest that targeting of amino acid replenishing mechanisms could be considered as a potential approach in developing combination therapies for treating neuroblastomas.

A prospective compound screening contest identified broader inhibitors for Sirtuin 1.

Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using in silico methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified.

Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism.

DNA-binding proteins (DBPs) perform diverse biological functions ranging from transcription to pathogen sensing. Machine learning methods can not only identify DBPs de novo but also provide insights into their DNA-recognition dynamics. However, it remains unclear whether available methods that can accurately predict DNA-binding sites in known DBPs can also identify novel DBPs. Moreover, sequence information is blind to the cellular- and disease-specific contexts of DBP activities, whereas the under-utilized knowledge from public gene expression data offers great promise. To address these issues, we have developed novel methods for predicting DBPs by integrating sequence and gene expression-derived features and applied them to explore human, mouse and Arabidopsis proteomes. While our sequence-based models outperformed the gene expression-based ones, some proteins with weaker DBP-like sequence features were correctly predicted by gene expression-based features, suggesting that these proteins acquire a tangible DBP functionality in a conducive gene expression environment. Analysis of motif enrichment among the co-expressed genes of top 100 candidates DBPs from hitherto unannotated genes provides further avenues to explore their functional associations.

Network analysis and in silico prediction of protein-protein interactions with applications in drug discovery.

Protein-protein interactions (PPIs) are vital to maintaining cellular homeostasis. Several PPI dysregulations have been implicated in the etiology of various diseases and hence PPIs have emerged as promising targets for drug discovery. Surface residues and hotspot residues at the interface of PPIs form the core regions, which play a key role in modulating cellular processes such as signal transduction and are used as starting points for drug design. In this review, we briefly discuss how PPI networks (PPINs) inferred from experimentally characterized PPI data have been utilized for knowledge discovery and how in silico approaches to PPI characterization can contribute to PPIN-based biological research. Next, we describe the principles of in silico PPI prediction and survey the existing PPI and PPI site prediction servers that are useful for drug discovery. Finally, we discuss the potential of in silico PPI prediction in drug discovery.

Exploring the epigenetic drug discovery landscape.

INTRODUCTION: Epigenetic modification has been implicated in a wide range of diseases and the ability to modulate such systems is a lucrative therapeutic strategy in drug discovery. Areas covered: This article focuses on the concepts and drug discovery aspects of epigenomics. This is achieved by providing a survey of the following concepts: (i) factors influencing epigenetics, (ii) diseases arising from epigenetics, (iii) epigenetic enzymes as druggable targets along with coverage of existing FDA-approved drugs and pharmacological agents, and (iv) drug repurposing/repositioning as a means for rapid discovery of pharmacological agents targeting epigenetics. Expert opinion: Despite significant interests in targeting epigenetic modifiers as a therapeutic route, certain classes of target proteins are heavily studied while some are less characterized. Thus, such orphan target proteins are not yet druggable with limited report of active modulators. Current research points towards a great future with novel drugs directed to the many complex multifactorial diseases of humans, which are still often poorly understood and difficult to treat.

Rational design, synthesis, and structure-activity relationships of 5-amino-1H-pyrazole-4-carboxylic acid derivatives as protein tyrosine phosphatase 1B inhibitors.

A series of novel amino-carboxylic based pyrazole as protein tyrosine phosphatase 1B (PTP1B) inhibitors were designed on the basis of structure-based pharmacophore model and molecular docking. Compounds containing different hydrophobic tail (1,2-diphenyl ethanone, oxdiadizole and dibenzyl amines) were synthesized and evaluated in PTP1B enzymatic assay. Structure-activity relationship based optimization resulted in identification of several potent, metabolically stable and cell permeable PTP1B inhibitors.

Towards Predicting the Cytochrome P450 Modulation: From QSAR to Proteochemometric Modeling.

Drug metabolism determines the fate of a drug when it enters the human body and is a critical factor in defining their absorption, distribution, metabolism, excretion and toxicity (ADMET) characteristics. Among the various drug metabolizing enzymes, cytochrome P450s (CYP450) constitute an important protein family that aside from functioning in xenobiotic metabolism, is also responsible for a diverse array of other roles encompassing steroid and cholesterol biosynthesis, fatty acid metabolism, calcium homeostasis, neuroendocrine functions and growth regulation. Although CYP450 typically converts xenobiotics into safe metabolites, there are some situations whereby the metabolite is more toxic than its parent molecule. Computational modeling has been instrumental in CYP450 research by rationalizing the nature of the binding event (i.e. inhibit or induce CYP450s) or metabolic stability of query compounds of interest. A plethora of computational approaches encompassing ligand, structure and systems based approaches have been utilized to model CYP450-ligand interactions. This review provides a brief background on the CYP450 family (i.e. its roles, advantages and disadvantages as well as its modulators) and then discusses the various computational approaches that have been used to model CYP450-ligand interaction. Particular focus was given to the use of quantitative structure-activity relationship (QSAR) and more recent proteochemometric modeling studies. Finally, a perspective on the current state of the art and future trends of the field is also provided.

Improved pose and affinity predictions using different protocols tailored on the basis of data availability.

The D3R 2015 grand drug design challenge provided a set of blinded challenges for evaluating the applicability of our protocols for pose and affinity prediction. In the present study, we report the application of two different strategies for the two D3R protein targets HSP90 and MAP4K4. HSP90 is a well-studied target system with numerous co-crystal structures and SAR data. Furthermore the D3R HSP90 test compounds showed high structural similarity to existing HSP90 inhibitors in BindingDB. Thus, we adopted an integrated docking and scoring approach involving a combination of both pharmacophoric and heavy atom similarity alignments, local minimization and quantitative structure activity relationships modeling, resulting in the reasonable prediction of pose [with the root mean square deviation (RMSD) values of 1.75 Å for mean pose 1, 1.417 Å for the mean best pose and 1.85 Å for the mean all poses] and affinity (ROC AUC = 0.702 at 7.5 pIC50 cut-off and R = 0.45 for 180 compounds). The second protein, MAP4K4, represents a novel system with limited SAR and co-crystal structure data and little structural similarity of the D3R MAP4K4 test compounds to known MAP4K4 ligands. For this system, we implemented an exhaustive pose and affinity prediction protocol involving docking and scoring using the PLANTS software which considers side chain flexibility together with protein-ligand fingerprints analysis assisting in pose prioritization. This protocol through fares poorly in pose prediction (with the RMSD values of 4.346 Å for mean pose 1, 4.69 Å for mean best pose and 4.75 Å for mean all poses) and produced reasonable affinity prediction (AUC = 0.728 at 7.5 pIC50 cut-off and R = 0.67 for 18 compounds, ranked 1st among 80 submissions).

Integration of Ligand and Structure Based Approaches for CSAR-2014.

The prediction of binding poses and affinities is an area of active interest in computer-aided drug design (CADD). Given the documented limitations with either ligand or structure based approaches, we employed an integrated approach and developed a rapid protocol for binding mode and affinity predictions. This workflow was applied to the three protein targets of Community Structure-Activity Resource-2014 (CSAR-2014) exercise: Factor Xa (FXa), Spleen Tyrosine Kinase (SYK), and tRNA (guanine-N(1))-methyltransferase (TrmD). Our docking and scoring workflow incorporates compound clustering and ligand and protein structure based pharmacophore modeling, followed by local docking, minimization, and scoring. While the former part of the protocol ensures high-quality ligand alignments and mapping, the subsequent minimization and scoring provides the predicted binding modes and affinities. We made blind predictions of docking pose for 1, 5, and 14 ligands docked into 1, 2, and 12 crystal structures of FXa, SYK, and TrmD, respectively. The resulting 174 poses were compared with cocrystallized structures (1, 5, and 14 complexes) made available at the end of CSAR. Our predicted poses were related to the experimentally determined structures with a mean root-mean-square deviation value of 3.4 Å. Further, we were able to classify high and low affinity ligands with the area under the curve values of 0.47, 0.60, and 0.69 for FXa, SYK, and TrmD, respectively, indicating the validity of our approach in at least two of the three systems. Detailed critical analysis of the results and CSAR methodology ranking procedures suggested that a straightforward application of our workflow has limitations, as some of the performance measures do not reflect the actual utility of pose and affinity predictions in the biological context of individual systems.

Systems Biology Approaches to a Rational Drug Discovery Paradigm.

Ligand- and structure-based drug design approaches complement phenotypic and target screens, respectively, and are the two major frameworks for guiding early-stage drug discovery efforts. Since the beginning of this century, the advent of the genomic era has presented researchers with a myriad of high throughput biological data (parts lists and their interaction networks) to address efficacy and toxicity, augmenting the traditional ligand- and structure-based approaches. This data rich era has also presented us with challenges related to integrating and analyzing these multi-platform and multi-dimensional datasets and translating them into viable hypotheses. Hence in the present paper, we review these existing approaches to drug discovery research and argue the case for a new systems biology based approach. We present the basic principles and the foundational arguments/underlying assumptions of the systems biology based approaches to drug design. Also discussed are systems biology data types (key entities, their attributes and their relationships with each other, and data models/representations), software and tools used for both retrospective and prospective analysis, and the hypotheses that can be inferred. In addition, we summarize some of the existing resources for a systems biology based drug discovery paradigm (open TG-GATEs, DrugMatrix, CMap and LINCs) in terms of their strengths and limitations.

Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target.

A search of broader range of chemical space is important for drug discovery. Different methods of computer-aided drug discovery (CADD) are known to propose compounds in different chemical spaces as hit molecules for the same target protein. This study aimed at using multiple CADD methods through open innovation to achieve a level of hit molecule diversity that is not achievable with any particular single method. We held a compound proposal contest, in which multiple research groups participated and predicted inhibitors of tyrosine-protein kinase Yes. This showed whether collective knowledge based on individual approaches helped to obtain hit compounds from a broad range of chemical space and whether the contest-based approach was effective.

A robust tool for discriminative analysis and feature selection in paired samples impacts the identification of the genes essential for reprogramming lung tissue to adenocarcinoma.

BACKGROUND: Lung cancer is the leading cause of cancer deaths in the world. The most common type of lung cancer is lung adenocarcinoma (AC). The genetic mechanisms of the early stages and lung AC progression steps are poorly understood. There is currently no clinically applicable gene test for the early diagnosis and AC aggressiveness. Among the major reasons for the lack of reliable diagnostic biomarkers are the extraordinary heterogeneity of the cancer cells, complex and poorly understudied interactions of the AC cells with adjacent tissue and immune system, gene variation across patient cohorts, measurement variability, small sample sizes and sub-optimal analytical methods. We suggest that gene expression profiling of the primary tumours and adjacent tissues (PT-AT) handled with a rational statistical and bioinformatics strategy of biomarker prediction and validation could provide significant progress in the identification of clinical biomarkers of AC. To minimise sample-to-sample variability, repeated multivariate measurements in the same object (organ or tissue, e.g. PT-AT in lung) across patients should be designed, but prediction and validation on the genome scale with small sample size is a great methodical challenge. RESULTS: To analyse PT-AT relationships efficiently in the statistical modelling, we propose an Extreme Class Discrimination (ECD) feature selection method that identifies a sub-set of the most discriminative variables (e.g. expressed genes). Our method consists of a paired Cross-normalization (CN) step followed by a modified sign Wilcoxon test with multivariate adjustment carried out for each variable. Using an Affymetrix U133A microarray paired dataset of 27 AC patients, we reviewed the global reprogramming of the transcriptome in human lung AC tissue versus normal lung tissue, which is associated with about 2,300 genes discriminating the tissues with 100% accuracy. Cluster analysis applied to these genes resulted in four distinct gene groups which we classified as associated with (i) up-regulated genes in the mitotic cell cycle lung AC, (ii) silenced/suppressed gene specific for normal lung tissue, (iii) cell communication and cell motility and (iv) the immune system features. The genes related to mutagenesis, specific lung cancers, early stage of AC development, tumour aggressiveness and metabolic pathway alterations and adaptations of cancer cells are strongly enriched in the AC PT-AT discriminative gene set. Two AC diagnostic biomarkers SPP1 and CENPA were successfully validated on RT-RCR tissue array. ECD method was systematically compared to several alternative methods and proved to be of better performance and as well as it was validated by comparison of the predicted gene set with literature meta-signature. CONCLUSIONS: We developed a method that identifies and selects highly discriminative variables from high dimensional data spaces of potential biomarkers based on a statistical analysis of paired samples when the number of samples is small. This method provides superior selection in comparison to conventional methods and can be widely used in different applications. Our method revealed at least 23 hundreds patho-biologically essential genes associated with the global transcriptional reprogramming of human lung epithelium cells and lung AC aggressiveness. This gene set includes many previously published AC biomarkers reflecting inherent disease complexity and specifies the mechanisms of carcinogenesis in the lung AC. SPP1, CENPA and many other PT-AT discriminative genes could be considered as the prospective diagnostic and prognostic biomarkers of lung AC.

Fishing the target of antitubercular compounds: in silico target deconvolution model development and validation.

An in silico target prediction protocol for antitubercular (antiTB) compounds has been proposed in this work. This protocol is the extension of a recently published 'domain fishing model' (DFM), validating its predicted targets on a set of 42 common antitubercular drugs. For the 23 antiTB compounds of the set which are directly linked to targets (see text for definition), the DFM exhibited a very good target prediction accuracy of 95%. For 19 compounds indirectly linked to targets also, a reasonable pathway/embedded pathway prediction accuracy of 84% was achieved. Since mostly eukaryotic ligand binding data was used for the DFM generation, the high target prediction accuracy for prokaryotes (which is an extrapolation from the training data) was unexpected and provides an additional proof of concept of the DFM. To estimate the general applicability of the model, ligand-target coverage analysis was performed. Here, it was found that, although the DFM only modestly covers the entire TB proteome (32% of all proteins), it captures 70% of the proteome subset targeted by 42 common antiTB compounds, which is in agreement with the good predictive ability of the DFM for the targets of the compounds chosen here. In a prospective validation, the model successfully predicted the targets of new antiTB compounds, CBR-2092 and Amiclenomycin. Together, these findings suggest that in silico target prediction tools may be a useful supplement to existing, experimental target deconvolution strategies.

Global Bayesian models for the prioritization of antitubercular agents.

To aid the creation of novel antituberculosis (antiTB) compounds, Bayesian models were derived and validated on a data set of 3779 compounds which have been measured for minimum inhibitory concentration (MIC) in the Mycobacterium tuberculosis H37Rv strain. The model development and validation involved exploring six different training sets and 15 fingerprint types which resulted in a total of 90 models, with active compounds defined as those with MIC < 5 microM. The best model was derived using Extended Class Fingerprints of maximum diameter 12 (ECFP_12) and a few global descriptors on a training set derived using Functional Class Fingerprints of maximum diameter 4 (FCFP_4). This model demonstrated very good discriminant ability in general, with excellent discriminant statistics for the training set (total accuracy: 0.968; positive recall: 0.967) and a good predictive ability for the test set (total accuracy: 0.869; positive recall: 0.789). The good predictive ability was maintained when the model was applied to a well-separated test set of 2880 compounds derived from a commercial database (total accuracy: 0.73; positive recall: 0.72). The model revealed several conserved substructures present in the active and inactive compounds which are believed to have incremental and detrimental effects on the MIC, respectively. Strategies for enhancing the repertoire of antiTB compounds with the model, including virtual screening of large databases and combinatorial library design, are proposed.