CIPSI: An open chemical intellectual property service for medicinal chemists.

The availability of patent chemical data offers public access to a chemical space that is not well covered by other sources collecting small molecules from scholarly literature. However, open applications to facilitate the search and analysis of biologically-relevant molecular structures present in patents are still largely missing. We have developed CIPSI, an open Chemical Intellectual Property Service @ IMIM to assist medicinal chemists in searching and analysing molecules in SureChEMBL patents. The current version contains 6,240,500 molecules from 236,689 pharmacological patents, of which 5,949,214 are confidently assigned to core chemical structures reminiscent of the Markush structure in the patent claim. The platform includes some graphical tools to facilitate comparative patent analyses between drugs, chemical substructures, and company assignees. CIPSI is available at https://cipsi.org.

Integrative GWAS and co-localisation analysis suggests novel genes associated with age-related multimorbidity.

Advancing age is the greatest risk factor for developing multiple age-related diseases. Therapeutic approaches targeting the underlying pathways of ageing, rather than individual diseases, may be an effective way to treat and prevent age-related morbidity while reducing the burden of polypharmacy. We harness the Open Targets Genetics Portal to perform a systematic analysis of nearly 1,400 genome-wide association studies (GWAS) mapped to 34 age-related diseases and traits, identifying genetic signals that are shared between two or more of these traits. Using locus-to-gene (L2G) mapping, we identify 995 targets with shared genetic links to age-related diseases and traits, which are enriched in mechanisms of ageing and include known ageing and longevity-related genes. Of these 995 genes, 128 are the target of an approved or investigational drug, 526 have experimental evidence of binding pockets or are predicted to be tractable, and 341 have no existing tractability evidence, representing underexplored genes which may reveal novel biological insights and therapeutic opportunities. We present these candidate targets for exploration and prioritisation in a web application.

Illuminating the Chemical Space of Untargeted Proteins.

According to the Illuminating the Druggable Genome (IDG) initiative, 90% of the proteins encoded by the human genome still lack an identified active ligand, that is, a small molecule with biologically relevant binding potency or functional activity in an in vitro assay. Under this scenario, there is an urgent need for new approaches to chemically address these yet untargeted proteins. It is widely recognized that the best starting point for generating novel small molecules for proteins is to exploit the expected polypharmacology of known active ligands across phylogenetically related proteins following the paradigm that similar proteins are likely to interact with similar ligands. Here, we introduce a computational strategy to identify privileged structures that, when chemically expanded, are highly probable to contain active small molecules for untargeted proteins. The protocol was first tested on a set of 576 currently targeted proteins having at least one protein family sibling the year before their first active ligand was reported. A privileged structure contained in active ligands that were identified in the following years was correctly anticipated for 214 (37%) of those targeted proteins, a lower-bound recall estimate when considering data completeness issues. When applied to a set of 1184 untargeted potential druggable genes in cancer, the identification of privileged structures from known bioactive ligands of protein family siblings allowed for extracting a priority list of diverse commercially available small molecules for 960 of them. Assuming a minimum success rate of 37%, the chemical library selections should be able to deliver active ligands for at least 355 currently untargeted proteins associated with cancer.

Congenericity of Claimed Compounds in Patent Applications.

A method is presented to analyze quantitatively the degree of congenericity of claimed compounds in patent applications. The approach successfully differentiates patents exemplified with highly congeneric compounds of a structurally compact and well defined chemical series from patents containing a more diverse set of compounds around a more vaguely described patent claim. An application to 750 common patents available in SureChEMBL, SureChEMBLccs and ChEMBL is presented and the congenericity of patent compounds in those different sources discussed.

Identification of the Core Chemical Structure in SureChEMBL Patents.

The SureChEMBL database provides open access to 17 million chemical entities mentioned in 14 million patents published since 1970. However, alongside with molecules covered by patent claims, the database is full of starting materials and intermediate products of little pharmacological relevance. Herein, we introduce a new filtering protocol to automatically select the core chemical structures best representing a congeneric series of pharmacologically relevant molecules in patents. The protocol is first validated against a selection of 890 SureChEMBL patents for which a total of 51,738 manually curated molecules are deposited in ChEMBL. Our protocol was able to select 92.5% of the molecules in ChEMBL from all 270,968 molecules in SureChEMBL for those patents. Subsequently, the protocol was applied to all 240,988 US pharmacological patents for which 9,111,706 molecules are available in SureChEMBL. The unsupervised filtering process selected 5,949,214 molecules (65.3% of the total number of molecules) that form highly congeneric chemical series in 188,795 of those patents (78.3% of the total number of patents). A SureChEMBL version enriched with molecules of pharmacological relevance is available for download at https://ftp.ebi.ac.uk/pub/databases/chembl/SureChEMBLccs.