Vinod Kumar

Bio: Vinod Kumar is an academic researcher from Central University of Punjab. The author has contributed to research in topics: Medicine & Chemistry. The author has an hindex of 30, co-authored 100 publications receiving 3264 citations. Previous affiliations of Vinod Kumar include Council of Scientific and Industrial Research & University of Siena.

Thousands of chemical starting points for antimalarial lead identification

Abstract: Malaria is a devastating infection caused by protozoa of the genus Plasmodium. Drug resistance is widespread, no new chemical class of antimalarials has been introduced into clinical practice since 1996 and there is a recent rise of parasite strains with reduced sensitivity to the newest drugs. We screened nearly 2 million compounds in GlaxoSmithKline's chemical library for inhibitors of P. falciparum, of which 13,533 were confirmed to inhibit parasite growth by at least 80% at 2 microM concentration. More than 8,000 also showed potent activity against the multidrug resistant strain Dd2. Most (82%) compounds originate from internal company projects and are new to the malaria community. Analyses using historic assay data suggest several novel mechanisms of antimalarial action, such as inhibition of protein kinases and host-pathogen interaction related targets. Chemical structures and associated data are hereby made public to encourage additional drug lead identification efforts and further research into this disease.

Selective targeting of BD1 and BD2 of the BET proteins in cancer and immunoinflammation

Abstract: The two tandem bromodomains of the BET (bromodomain and extraterminal domain) proteins enable chromatin binding to facilitate transcription. Drugs that inhibit both bromodomains equally have shown efficacy in certain malignant and inflammatory conditions. To explore the individual functional contributions of the first (BD1) and second (BD2) bromodomains in biology and therapy, we developed selective BD1 and BD2 inhibitors. We found that steady-state gene expression primarily requires BD1, whereas the rapid increase of gene expression induced by inflammatory stimuli requires both BD1 and BD2 of all BET proteins. BD1 inhibitors phenocopied the effects of pan-BET inhibitors in cancer models, whereas BD2 inhibitors were predominantly effective in models of inflammatory and autoimmune disease. These insights into the differential requirement of BD1 and BD2 for the maintenance and induction of gene expression may guide future BET-targeted therapies.

New Compound Sets Identified from High Throughput Phenotypic Screening Against Three Kinetoplastid Parasites: An Open Resource

Abstract: Using whole-cell phenotypic assays, the GlaxoSmithKline high-throughput screening (HTS) diversity set of 18 million compounds was screened against the three kinetoplastids most relevant to human disease, ie Leishmania donovani, Trypanosoma cruzi and Trypanosoma brucei Secondary confirmatory and orthogonal intracellular anti-parasiticidal assays were conducted, and the potential for non-specific cytotoxicity determined Hit compounds were chemically clustered and triaged for desirable physicochemical properties The hypothetical biological target space covered by these diversity sets was investigated through bioinformatics methodologies Consequently, three anti-kinetoplastid chemical boxes of ~200 compounds each were assembled Functional analyses of these compounds suggest a wide array of potential modes of action against kinetoplastid kinases, proteases and cytochromes as well as potential host–pathogen targets This is the first published parallel high throughput screening of a pharma compound collection against kinetoplastids The compound sets are provided as an open resource for future lead discovery programs, and to address important research questions

Recent Developments on 1,2,4-Triazole Nucleus in Anticancer Compounds: A Review

Abstract: 1,2,4-triazole is an important nucleus present in a large number of compounds. More than thirty-five compounds containing this nucleus are introduced into the market. 1,2,4-triazole nucleus is stable to metabolism and acts as an important pharmacophore by interacting at the active site of a receptor as hydrogen bond acceptor and as a donor. Due to its polar nature, the triazole nucleus can increase the solubility of the ligand and it can significantly improve the pharmacological profile of the drug. A large number of 1,2,4-triazole derivatives are reported to possess a wide range of bioactivities including anti-cancer activity. This review article describes the role of 1,2,4-triazole nucleus in different types of anti-cancer agents such as nucleoside based anti-cancer agents, kinase inhibitors, tubulin modulators, aromatase and steroid sulfatase inhibitors, methionine aminopeptidase inhibitors, tankyrase inhibitors and metal complex based anti-cancer agents. It is expected that the current review article will provide insight into various ligand-receptor interactions and help in the rational design and development of novel 1,2,4-triazole based anti-cancer drugs with improved selectivity for cancer cells.

ChEMBL: a large-scale bioactivity database for drug discovery

Abstract: ChEMBL is an Open Data database containing binding, functional and ADMET information for a large number of drug-like bioactive compounds. These data are manually abstracted from the primary published literature on a regular basis, then further curated and standardized to maximize their quality and utility across a wide range of chemical biology and drug-discovery research problems. Currently, the database contains 5.4 million bioactivity measurements for more than 1 million compounds and 5200 protein targets. Access is available through a web-based interface, data downloads and web services at: https://www.ebi.ac.uk/chembldb.

Convolutional networks on graphs for learning molecular fingerprints

Abstract: We introduce a convolutional neural network that operates directly on graphs. These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape. The architecture we present generalizes standard molecular feature extraction methods based on circular fingerprints. We show that these data-driven features are more interpretable, and have better predictive performance on a variety of tasks.

The ChEMBL database in 2017.

Abstract: ChEMBL is an open large-scale bioactivity database (https://www.ebi.ac.uk/chembl), previously described in the 2012 and 2014 Nucleic Acids Research Database Issues. Since then, alongside the continued extraction of data from the medicinal chemistry literature, new sources of bioactivity data have also been added to the database. These include: deposited data sets from neglected disease screening; crop protection data; drug metabolism and disposition data and bioactivity data from patents. A number of improvements and new features have also been incorporated. These include the annotation of assays and targets using ontologies, the inclusion of targets and indications for clinical candidates, addition of metabolic pathways for drugs and calculation of structural alerts. The ChEMBL data can be accessed via a web-interface, RDF distribution, data downloads and RESTful web-services.

Applications of machine learning in drug discovery and development.

Abstract: Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development. Machine learning has been applied to numerous stages in the drug discovery pipeline. Here, Vamathevan and colleagues discuss the most useful techniques and how machine learning can promote data-driven decision making in drug discovery and development. They highlight major hurdles in the field, such as the required data characteristics for applying machine learning, which will need to be solved as machine learning matures.

Decarboxylative coupling reactions: a modern strategy for C–C-bond formation

Abstract: This critical review examines transition metal-catalyzed decarboxylative couplings that have emerged within recent years as a powerful strategy to form carbon–carbon or carbon–heteroatom bonds starting from carboxylic acids. In these reactions, C–C bonds to carboxylate groups are cleaved, and in their place, new carbon–carbon bonds are formed. Decarboxylative cross-couplings constitute advantageous alternatives to traditional cross-coupling or addition reactions involving preformed organometallic reagents. Decarboxylative reaction variants are also known for Heck reactions, direct arylation processes, and carbon–heteroatom bond forming reactions.