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.

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ChEMBL: a large-scale bioactivity database for drug discovery

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.

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Convolutional networks on graphs for learning molecular fingerprints

Structural Basis of the Drug-Binding Specificity of Human Serum Albumin.

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.

The ChEMBL database in 2017.

An overview is given on the diverse uses of computational chemistry in drug discovery. Particular emphasis is placed on virtual screening, de novo design, evaluation of drug-likeness, and advanced methods for determining protein-ligand binding.

http://chekhov.cs.vt.edu/PAPERS/Jorgensen.DrugDiscovery.pdf

The Many Roles of Computation in Drug Discovery

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.

https://patentdocs.typepad.com/files/thousands-of-chemical-starting-points-for-antimalarial-lead-identification.pdf

Thousands of chemical starting points for antimalarial lead identification

Models to predict binding affinities to human serum albumin (HSA) should be very useful in the pharmaceutical industry to speed up the design of new compounds, especially as far as pharmacokinetics is concerned. We have experimentally determined through high-performace affinity chromatography the binding affinities to HSA of 95 diverse drugs and druglike compounds. These data have allowed us the derivation of quantitative structure−activity relationship models to predict binding affinities to HSA of new compounds on the basis of their structure. Simple linear, one-variable models have been derived for specific families of compounds (r2 ≥ 0.80; q2 ≥ 0.62):  β-adrenergic antagonists, steroids, COX inhibitors, and tricyclic antidepressants. Also, global models have been derived to be applicable to the whole medicinal chemical space by using the full database of HSA binding constants described above. For this aim, a genetic algorithm has been used to exhaustively search and select for multivariate and nonline...

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J.L. Lavandera

Papers

Thousands of chemical starting points for antimalarial lead identification

Cheminformatic models to predict binding affinities to human serum albumin.

Structure-guided discovery of phenyl-diketo acids as potent inhibitors of M. tuberculosis malate synthase.

Plasmodium falciparum: stage specific effects of a selective inhibitor of lactate dehydrogenase.