Tracking 20 years of compound-to-target output from literature and patents

PatCID: an open-access dataset of chemical structures in patent documents

The automatic analysis of patent publications has potential to accelerate research across various domains, including drug discovery and material science. Within patent documents, crucial information often resides in visual depictions of molecule structures. PatCID (Patent-extracted Chemical-structure Images database for Discovery) allows to access such information at scale. It enables users to search which molecules are displayed in which documents. PatCID contains 81M chemical-structure images and 14M unique chemical structures. Here, we compare PatCID with state-of-the-art chemical patent-databases. On a random set, PatCID retrieves 56.0% of molecules, which is higher than automatically-created databases, Google Patents (41.5%) and SureChEMBL (23.5%), as well as manually-created databases, Reaxys (53.5%) and SciFinder (49.5%). Leveraging state-of-the-art methods of document understanding, PatCID high-quality data outperforms currently available automatically-generated patent-databases. PatCID even competes with proprietary manually-created patent-databases. This enables promising applications for automatic literature review and learning-based molecular generation methods. The dataset is freely accessible for download.

Opening up connectivity between documents, structures and bioactivity

Bioscientists reading papers or patents strive to discern the key relationships reported within a document "D" where a bioactivity "A" with a quantitative result "R" (e.g., an IC50) is reported for chemical structure "C" that modulates (e.g., inhibits) a protein target "P". A useful shorthand for this connectivity thus becomes DARCP. The problem at the core of this article is that the community has spent millions effectively burying these relationships in PDFs over many decades but must now spend millions more trying to get them back out. The key imperative for this is to increase the flow into structured open databases. The positive impacts will include expanded data mining opportunities for drug discovery and chemical biology. Over the last decade commercial sources have manually extracted DARCP from ≈300,000 documents encompassing ≈7 million compounds interacting with ≈10,000 targets. Over a similar time, the Guide to Pharmacology, BindingDB and ChEMBL have carried out analogues DARCP extractions. Although their expert-curated numbers are lower (i.e., ≈2 million compounds against ≈3700 human proteins), these open sources have the great advantage of being merged within PubChem. Parallel efforts have focused on the extraction of document-to-compound (D-C-only) connectivity. In the absence of molecular mechanism of action (mmoa) annotation, this is of less value but can be automatically extracted. This has been significantly accomplished for patents, (e.g., by IBM, SureChEMBL and WIPO) for over 30 million compounds in PubChem. These have recently been joined by 1.4 million D-C submissions from three major chemistry publishers. In addition, both the European and US PubMed Central portals now add chemistry look-ups from abstracts and full-text papers. However, the fully automated extraction of DARCLP has not yet been achieved. This stands in contrast to the ability of biocurators to discern these relationships in minutes. Unfortunately, no journals have yet instigated a flow of author-specified DARCP directly into open databases. Progress may come from trends such as open science, open access (OA), findable, accessible, interoperable and reusable (FAIR), resource description framework (RDF) and WikiData. However, we will need to await the technical applicability in respect to DARCP capture to see if this opens up connectivity.

Development of an Efficient Enzyme Production and Structure-Based Discovery Platform for BACE1 Inhibitors

BACE1 (Beta-site Amyloid Precursor Protein (APP) Cleaving Enzyme 1) is a promising therapeutic target for Alzheimer's Disease (AD). However, efficient expression, purification, and crystallization systems are not well described or detailed in the literature nor are approaches for treatment of enzyme kinetic data for potent inhibitors well described. We therefore developed a platform for expression and purification of BACE1, including protein refolding from E.coli inclusion bodies, in addition to optimizing a reproducible crystallization procedure of BACE1 bound with inhibitors. We also report a detailed approach to the proper analysis of enzyme kinetic data for compounds that exhibit either rapid-equilibrium or tight-binding mechanisms. Our methods allow for the purification of ∼15 mg of BACE1 enzyme from 1 L of culture which is higher than reported yields in the current literature. To evaluate the data analysis approach developed here, a well-known potent inhibitor and two of its derivatives were tested, analyzed, and compared. The inhibitory constants (K_i) obtained from the kinetic studies are in agreement with dissociation constants (K_d) that were also determined using isothermal titration calorimetry (ITC) experiments. The X-ray structures of these three compounds in complex with BACE1 were readily obtained and provide important insight into the structure and thermodynamics of the BACE1-inhibitor interactions.

Caveat Usor: Assessing Differences between Major Chemistry Databases

The three databases of PubChem, ChemSpider, and UniChem capture the majority of open chemical structure records with February 2018 totals of 95, 63, and 154 million, respectively. Collectively, they constitute a massively enabling resource for cheminformatics, chemical biology, and drug discovery. As meta-portals, they subsume and link out to the major proportion of public bioactivity data extracted from the literature and screening center assay results. Therefore, they not only present three different entry points, but the many subsumed independent resources present a fourth entry point in the form of standalone databases. Because this creates a complex picture it is important for users to have at least some appreciation of differential content to enable utility judgments for the tasks at hand. This turns out to be challenging. By comparing the three resources in detail, this review assesses their differences, some of which are not obvious. This includes the fact that coverage is significantly different between the 587, 282, and 38 contributing sources, respectively. This not only presents the "who-has-what" question, but also the reason "why" any particular inclusion is considered valuable is rarely made explicit. Also confusing is that sources nominally in common (i.e., having the same submitter name) can have significantly different structure counts, not only in each of the three but also from their standalone instantiations. Assessing a series of examples indicates that differences in loading dates and structural standardization are the main causes of this inter-portal discordance.

Innovation in Small-Molecule-Druggable Chemical Space: Where are the Initial Modulators of New Targets Published?

It is well-established that the number of publications of novel small molecule modulators, and their associated targets, has increased over the years. This work focuses on publishing trends over the years with a particular focus on the comparison between patents and scientific literature which is accessible via the ChEMBL and GOSTAR databases. More precisely, the patents and scientific literature associated with bioactive molecules and their target annotations have been compared to identify where novelty (in the meaning of the first modulator of a protein target) originated from. Comparing the published date of the first small molecule modulator published in literature and patents for a particular target (with either identical or different structure) shows that modulators are usually published in both scientific literature and in patents (45%), or in scientific literature alone (51%), but rarely in patents only. When looking at the time when first modulators are published in both sources, 65% of the time they are disseminated in literature first. Finally, when analyzing just the novel small molecule modulators, regardless of the protein targets they have been published with, those structures representing novel chemistry tend to be published in patents first 61% of the time.

Prioritizing multiple therapeutic targets in parallel using automated DNA-encoded library screening

The identification and prioritization of chemically tractable therapeutic targets is a significant challenge in the discovery of new medicines. We have developed a novel method that rapidly screens multiple proteins in parallel using DNA-encoded library technology (ELT). Initial efforts were focused on the efficient discovery of antibacterial leads against 119 targets from Acinetobacter baumannii and Staphylococcus aureus. The success of this effort led to the hypothesis that the relative number of ELT binders alone could be used to assess the ligandability of large sets of proteins. This concept was further explored by screening 42 targets from Mycobacterium tuberculosis. Active chemical series for six targets from our initial effort as well as three chemotypes for DHFR from M. tuberculosis are reported. The findings demonstrate that parallel ELT selections can be used to assess ligandability and highlight opportunities for successful lead and tool discovery.

Encoded Library Technology (ELT) has streamlined the identification of chemical ligands for protein targets in drug discovery. Here, the authors optimize the ELT approach to screen multiple proteins in parallel and identify promising targets and antibacterial compounds for S. aureus, A. baumannii and M. tuberculosis.

Open innovation in early drug discovery: roadmaps and roadblocks

Open innovation in pharmaceutical R&D evolved from a triple helix of convergent paradigm shifts in academic, industrial and government research sectors. The birth of the biotechnology sector catalyzed shifts in location dynamics that led to the first wave of open innovation in pharmaceutical R&D between big pharma and startup companies. The National Institutes of Health (NIH) Roadmap was a crucial inflection point that set the stage for a new wave of open innovation models between pharmaceutical companies and universities that have the potential to transform the pharmaceutical R&D landscape. We highlight the attributes of leading protected open innovation models that foster the sharing of proprietary small molecule collections by lowering the risk of premature escape of intellectual property, particularly structure-activity data.

SureChEMBL: a large-scale, chemically annotated patent document database

SureChEMBL is a publicly available large-scale resource containing compounds extracted from the full text, images and attachments of patent documents. The data are extracted from the patent literature according to an automated text and image-mining pipeline on a daily basis. SureChEMBL provides access to a previously unavailable, open and timely set of annotated compound-patent associations, complemented with sophisticated combined structure and keyword-based search capabilities against the compound repository and patent document corpus; given the wealth of knowledge hidden in patent documents, analysis of SureChEMBL data has immediate applications in drug discovery, medicinal chemistry and other commercial areas of chemical science. Currently, the database contains 17 million compounds extracted from 14 million patent documents. Access is available through a dedicated web-based interface and data downloads at: https://www.surechembl.org/.

Managing expectations: assessment of chemistry databases generated by automated extraction of chemical structures from patents

Background

First public disclosure of new chemical entities often takes place in patents, which makes them an important source of information. However, with an ever increasing number of patent applications, manual processing and curation on such a large scale becomes even more challenging. An alternative approach better suited for this large corpus of documents is the automated extraction of chemical structures. A number of patent chemistry databases generated by using the latter approach are now available but little is known that can help to manage expectations when using them. This study aims to address this by comparing two such freely available sources, SureChEMBL and IBM SIIP (IBM Strategic Intellectual Property Insight Platform), with manually curated commercial databases.

Results

When looking at the percentage of chemical structures successfully extracted from a set of patents, using SciFinder as our reference, 59 and 51 % were also found in our comparison in SureChEMBL and IBM SIIP, respectively. When performing this comparison with compounds as starting point, i.e. establishing if for a list of compounds the databases provide the links between chemical structures and patents they appear in, we obtained similar results. SureChEMBL and IBM SIIP found 62 and 59 %, respectively, of the compound-patent pairs obtained from Reaxys.

Conclusions

In our comparison of automatically generated vs. manually curated patent chemistry databases, the former successfully provided approximately 60 % of links between chemical structure and patents. It needs to be stressed that only a very limited number of patents and compound-patent pairs were used for our comparison. Nevertheless, our results will hopefully help to manage expectations of users of patent chemistry databases of this type and provide a useful framework for more studies like ours as well as guide future developments of the workflows used for the automated extraction of chemical structures from patents. The challenges we have encountered whilst performing this study highlight that more needs to be done to make such assessments easier. Above all, more adequate, preferably open access to relevant ‘gold standards’ is required.

Electronic supplementary material

The online version of this article (doi:10.1186/s13321-015-0097-z) contains supplementary material, which is available to authorized users.

Activity, assay and target data curation and quality in the ChEMBL database

The emergence of a number of publicly available bioactivity databases, such as ChEMBL, PubChem BioAssay and BindingDB, has raised awareness about the topics of data curation, quality and integrity. Here we provide an overview and discussion of the current and future approaches to activity, assay and target data curation of the ChEMBL database. This curation process involves several manual and automated steps and aims to: (1) maximise data accessibility and comparability; (2) improve data integrity and flag outliers, ambiguities and potential errors; and (3) add further curated annotations and mappings thus increasing the usefulness and accuracy of the ChEMBL data for all users and modellers in particular. Issues related to activity, assay and target data curation and integrity along with their potential impact for users of the data are discussed, alongside robust selection and filter strategies in order to avoid or minimise these, depending on the desired application.

COMBINATION THERAPIES OR STANDALONE INTERVENTIONS? INNOVATION OPTIONS FOR PHARMACEUTICAL FIRMS FIGHTING CANCER

Innovation is key to the pharma model, from an initial discovery to the final development of a marketable drug. Here, we explore the innovation options in the fiercely contested therapeutic area of Oncology. We studied clinical development of drugs targeting PD-1 and PD-L1 pathway. In this interesting contest, companies seem to be focusing on standalone interventions (exploratory innovation) and combination therapy (exploitative innovation) in clinical development. Merck Sharp and Dohme, having a relatively weaker oncology pipeline seems to take exploratory approach of standalone intervention. However for some indications, MSD seems to harness exploitative innovation approach through combination therapies by collaborating with external partners. AstraZeneca in contrast seems to focus on exploitative approach using its strong oncology pipeline for combination therapies. Our work provides a framework to understand exploratory (standalone) and exploitative (combination therapy) innovation approaches and their relationship to capability, scientific networks and market access for a company entering a fiercely contested pharmaceutical market.

Expanding opportunities for mining bioactive chemistry from patents

Bioactive structures published in medicinal chemistry patents typically exceed those in papers by at least twofold and may precede them by several years. The Big-Bang of open automated extraction since 2012 has contributed to over 15 million patent-derived compounds in PubChem. While mapping between chemical structures, assay results and protein targets from patent documents is challenging, these relationships can be harvested using open tools and are beginning to be curated into databases.

Allosteric Modulation of G Protein-Coupled Receptors: An Emerging Approach of Drug Discovery

Allosteric modulation of G protein-coupled receptors (GPCRs) confers several significant advantages over the traditional targeting of orthosteric sites. While the field of allosteric modulation of GPCRs as we now know it will benefit from continued investigation, the explosion of interest has led to a more in-depth understanding as to precisely how allosteric modulators may usher in a new paradigm for drug discovery.