Markush Structures Research Articles

Exploring the unexplored chemical space: Rational identification of new Tafenoquine analogs with antimalarial properties

Patents tend to define a huge chemical space described by the combinatorial nature of Markush structures. However, the optimization of new principal active ingredient is frequently driven by a simple Free Wilson approach. This procedure leads to a highly focused study on the chemical space near a hit compound leaving many unexplored regions that may present highly biological active reservoirs. This study aims to demonstrate that this unveiled chemical space can hide compounds with interesting potential biological activity that would be worth pursuing. This underlines the value and necessity of broadening an approach beyond conventional strategies. Hence, we advocate for an alternative methodology that may be more efficient in the early drug discovery stages. We have selected the case of Tafenoquine, a single-dose treatment for the radical cure of P. vivax malaria approved by the FDA in 2018, as an example to illustrate the process. Through the deep exploration of the Tafenoquine chemical space, seven compounds with potential antimalarial activity have been rationally identified and synthesized. This small set is representative of the chemical diversity unexplored by the 58 analogs reported to date. After biological assessment, results evidence that our approach for rational design has proven to be a very efficient exploratory methodology suitable for the early drug discovery stages.

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.

Multi-modal chemical information reconstruction from images and texts for exploring the near-drug space.

Identification of new chemical compounds with desired structural diversity and biological properties plays an essential role in drug discovery, yet the construction of such a potential space with elements of 'near-drug' properties is still a challenging task. In this work, we proposed a multimodal chemical information reconstruction system to automatically process, extract and align heterogeneous information from the text descriptions and structural images of chemical patents. Our key innovation lies in a heterogeneous data generator that produces cross-modality training data in the form of text descriptions and Markush structure images, from which a two-branch model with image- and text-processing units can then learn to both recognize heterogeneous chemical entities and simultaneously capture their correspondence. In particular, we have collected chemical structures from ChEMBL database and chemical patents from the European Patent Office and the US Patent and Trademark Office using keywords 'A61P, compound, structure' in the years from 2010 to 2020, and generated heterogeneous chemical information datasets with 210K structural images and 7818 annotated text snippets. Based on the reconstructed results and substituent replacement rules, structural libraries of a huge number of near-drug compounds can be generated automatically. In quantitative evaluations, our model can correctly reconstruct 97% of the molecular images into structured format and achieve an F1-score around 97-98% in the recognition of chemical entities, which demonstrated the effectiveness of our model in automatic information extraction from chemical patents, and hopefully transforming them to a user-friendly, structured molecular database enriching the near-drug space to realize the intelligent retrieval technology of chemical knowledge.

Deconstructing Markush: Improving the R&D Efficiency Using Library Selection in Early Drug Discovery.

Most of the product patents claim a large number of compounds based on a Markush structure. However, the identification and optimization of new principal active ingredients is frequently driven by a simple Free Wilson approach, leading to a highly focused study only involving the chemical space nearby a hit compound. This fact raises the question: do the tested compounds described in patents really reflect the full molecular diversity described in the Markush structure? In this study, we contrast the performance of rational selection to conventional approaches in seven real-case patents, assessing their ability to describe the patent’s chemical space. Results demonstrate that the integration of computer-aided library selection methods in the early stages of the drug discovery process would boost the identification of new potential hits across the chemical space.

Mapping Markush

Markush structures are molecular skeletons containing not only specific atoms but al-so placeholders to represent broad sets of chemical (sub)structures. They allow a vast number of compounds (e.g., 10^16 different compounds within patent EP 0810 209) to be claimed in a patent application without having to specify every single chemical entity. While Markush structures raise important questions regarding the functioning of the patent system, innova-tion researchers have been surprisingly silent on the topic. This paper summarizes the ongo-ing policy debate about Markush structures and provides first empirical insights into how Markush structures are used in patent documents in the pharmaceutical industry and how they affect important outcomes in the patent prosecution process. While not causing frictions in the patent prosecution process, patent documents containing Markush structures have an increased likelihood to restrict the patentability of follow-on inventions and to hamper the entry of generic competition in the product market.

Novel dual action anti-neoplastic drugs

A novel drug structure (a Markush structure) is presented including molecules that can be effective as dual action drugs namely as inhibitors of epidermal growth factor kinase domain as well as having anti-angiogenic activity. The novel Markush structure includes molecules that might have dual anti-neoplastic activity leading to the development of a novel effective cancer drug for treating e.g., pancreatic cancer.

Mapping Markush

Markush structures are molecular skeletons containing not only specific atoms but also placeholders to represent broad sets of chemical (sub)structures. As genus claims, they allow a vast number of compounds to be claimed in a patent application without having to specify every single chemical entity. While Markush structures raise important questions regarding the functioning of the patent system, innovation researchers have been surprisingly silent on the topic. This paper summarizes the ongoing policy debate about Markush structures and provides first empirical insights into how Markush structures are used in patent documents in the pharmaceutical industry and how they affect important outcomes in the patent prosecution process. While not causing frictions in the patent prosecution process, patent documents containing Markush structures have an increased likelihood to restrict the patentability of follow-on inventions and to facilitate the construction of broad patent fences.

Supporting the fight against the proliferation of chemical weapons through cheminformatics

Abstract International frameworks have been put in place to foster chemical weapons nonproliferation and disarmament. These frameworks feature lists of chemicals that can be used as chemical weapons or precursors for their synthesis (CW-control lists). In these lists, chemicals of concern are described through chemical names and CAS Registry Numbers®. Importantly, in some CW-control lists, some entries, rather than specifying individual chemicals, describe families of related chemicals. Working with CW-control lists poses challenges for frontline customs and export control officers implementing these frameworks. Entries that describe families of chemicals are not easy to interpret, especially for non-chemists. Moreover, synonyms and chemical variants complicate the issue of checking CW-control lists through names and registry numbers. To ameliorate these problems, we have developed a functioning prototype of a cheminformatics tool that automates the task of assessing whether a chemical is part of a CW-control list. The tool, dubbed the Nonproliferation Cheminformatics Compliance Tool (NCCT), is a database management system (based on ChemAxon’s Instant JChem) with an embedded database of chemical structures. The key feature of the database is that it contains not only the structures of the individually listed chemicals, but also the generic structures that describe the entries relative to families of chemicals (Markush structures).

Intelligent pharmaceutical patent search on a near-term gate-based quantum computer

Pharmaceutical patent analysis is the key to product protection for pharmaceutical companies. In patent claims, a Markush structure is a standard chemical structure drawing with variable substituents. Overlaps between apparently dissimilar Markush structures are nearly unrecognizable when the structures span a broad chemical space. We propose a quantum search-based method which performs an exact comparison between two non-enumerated Markush structures with a constraint satisfaction oracle. The quantum circuit is verified with a quantum simulator and the real effect of noise is estimated using a five-qubit superconductivity-based IBM quantum computer. The possibilities of measuring the correct states can be increased by improving the connectivity of the most computation intensive qubits. Depolarizing error is the most influential error. The quantum method to exactly compares two patents is hard to simulate classically and thus creates a quantum advantage in patent analysis.

Comparative Case Studies in Markush Structure Search and Patent Search for Manufacturing Compounds

Comparative Case Studies in Markush Structure Search and Patent Search for Manufacturing Compounds

ChemTables: a dataset for semantic classification on tables in chemical patents

Chemical patents are a commonly used channel for disclosing novel compounds and reactions, and hence represent important resources for chemical and pharmaceutical research. Key chemical data in patents is often presented in tables. Both the number and the size of tables can be very large in patent documents. In addition, various types of information can be presented in tables in patents, including spectroscopic and physical data, or pharmacological use and effects of chemicals. Since images of Markush structures and merged cells are commonly used in these tables, their structure also shows substantial variation. This heterogeneity in content and structure of tables in chemical patents makes relevant information difficult to find. We therefore propose a new text mining task of automatically categorising tables in chemical patents based on their contents. Categorisation of tables based on the nature of their content can help to identify tables containing key information, improving the accessibility of information in patents that is highly relevant for new inventions. For developing and evaluating methods for the table classification task, we developed a new dataset, called ChemTables, which consists of 788 chemical patent tables with labels of their content type. We introduce this data set in detail. We further establish strong baselines for the table classification task in chemical patents by applying state-of-the-art neural network models developed for natural language processing, including TabNet, ResNet and Table-BERT on ChemTables. The best performing model, Table-BERT, achieves a performance of 88.66 micro-averaged F_1 score on the table classification task. The ChemTables dataset is publicly available at https://doi.org/10.17632/g7tjh7tbrj.3, subject to the CC BY NC 3.0 license. Code/models evaluated in this work are in a Github repository https://github.com/zenanz/ChemTables.

IntelliPatent: a web-based intelligent system for fast chemical patent claim drafting

The first step of automating composition patent drafting is to draft the claims around a Markush structure with substituents. Currently, this process depends heavily on experienced attorneys or patent agents, and few tools are available. IntelliPatent was created to accelerate this process. Users can simply upload a series of analogs of interest, and IntelliPatent will automatically extract the general structural scaffold and generate the patent claim text. The program can also extend the patent claim by adding commonly seen R groups from historical lists of the top 30 selling drugs in the US for all R substituents. The program takes MDL SD file formats as inputs, and the invariable core structure and variable substructures will be identified as the initial scaffold and R groups in the output Markush structure. The results can be downloaded in MS Word format (.docx). The suggested claims can be quickly generated with IntelliPatent. This web-based tool is freely accessible at https://intellipatent.cmdm.tw/.

A Primer of Deuterium in Drug Design

A Primer of Deuterium in Drug Design

Automatic generation of Markush structures from specific compounds

Markush structures play an important role in cheminformatics, especially in chemical patents. This paper presents a novel algorithm for automatically generating Markush structures from series of specific compounds. This method can effectively be used to assist patent drafting or to compose combinatorial libraries based on several molecules of interest. According to the authors’ knowledge, the presented algorithm is the first solution to this problem. It is available in multiple software products of ChemAxon.

Markush structure search mechanism on STN

Markush structure search mechanism on STN

A deep analysis of chemical structure-based patent searching in the Derwent index space

Structure-based patent searching with high precision and recall is the most important information request in chemistry. The different parts of chemical patent information are stored in different databases and the challenge is to bring the different parts together in a consistent manner and retrieve a complete set of structures and patents. With the application of set theory, it is possible to develop a method which enables the user to obtain a group of corresponding, consistent answer sets after subsequent searching in the different databases and utilizing the special features of both structure and concept searching. A second goal of this paper is to demonstrate the relevance of Markush structures for patent searches in a quantitative manner. Another goal is the development of a set of simple indicators which can be applied to the search results of structure-based patent searches. Four indicators have been chosen with special focus on the relevance of Markush structures for patent searches. The searches and the analysis were carried out with ten pharmaceutical examples. The results show in a quantitative manner, that a search in the Markush database yields a number of inventions (patent families) that cannot be found via searches in specific structure databases alone.

First metabolic profile of PV8, a novel synthetic cathinone, in human hepatocytes and urine by high-resolution mass spectrometry

Novel psychoactive substances (NPS) are ever changing on the drug market, making it difficult for toxicology laboratory methods to stay current with so many new drugs. Recently, PV8, a synthetic pyrrolidinophenone, was detected in seized products in Japan (2013), The Netherlands (2014), and Germany (2014). There are no controlled PV8 administration studies, and no pharmacodynamic and pharmacokinetic data. The objective was to determine PV8's metabolic stability in human liver microsome (HLM) incubation and its metabolism following human hepatocyte incubation and high-resolution mass spectrometry (HRMS) with a Thermo Scientific Q-Exactive. Data were acquired with a full-scan data-dependent mass spectrometry method. Scans were thoroughly data mined with different data processing algorithms and analyzed in WebMetaBase. PV8 exhibited a relatively short 28.8min half-life, with an intrinsic 24.2μL/min/mg microsomal clearance. This compound is predicted to be an intermediate clearance drug with an estimated human 22.7mL/min/kg hepatic clearance. Metabolic pathways identified in vitro included: hydroxylation, ketone reduction, carboxylation, N-dealkylation, iminium formation, dehydrogenation, N-oxidation, and carbonylation. The top three in vitro metabolic pathways were di-hydroxylation > ketone reduction > γ-lactam formation. Authentic urine specimen analyses revealed the top three metabolic pathways were aliphatic hydroxylation > ketone reduction + aliphatic hydroxylation > aliphatic carboxylation, although the most prominent peak was parent PV8. These data provide useful urinary metabolite targets (aliphatic hydroxylation, aliphatic hydroxylation + ketone reduction, aliphatic carboxylation, and di-hydroxylation) for forensic and clinical testing, and focus reference standard companies' synthetic efforts to provide commercially available standards needed for PV8 biological specimen testing. Graphical Abstract Top four PV8 metabolites identified in vitro. Biotransformations highlighted in blue. Markush structures presented when exact location of biotransformation is unknown.

A Novel Concept for the Search and Retrieval of the Derwent Markush Resource Database.

The representation of and search for generic chemical structures (Markush) remains a continuing challenge. Several research groups have addressed this problem, and over time a limited number of practical solutions have been proposed. Today there are two large commercial providers of Markush databases: Chemical Abstracts Service (CAS) and Thomson Reuters. The Thomson Reuters "Derwent" Markush database is currently offered via the online services Questel and STN and as a data feed for in-house use. The aim of this paper is to briefly review the existing Markush systems (databases plus search engines) and to describe our new approach for the implementation of the Derwent Markush Resource on STN. Our new approach demonstrates the integration of the Derwent Markush Resource database into the existing chemistry-focused STN platform without loss of detail. This provides compatibility with other structure and Markush databases on STN and at the same time makes it possible to deploy the specific features and functions of the Derwent approach. It is shown that the different Markush languages developed by CAS and Derwent can be combined into a single general Markush description. In this concept the generic nodes are grouped together in a unique hierarchy where all chemical elements and fragments can be integrated. As a consequence, both systems are searchable using a single structure query. Moreover, the presented concept could serve as a promising starting point for a common generalized description of Markush structures.

Markush structure searching by information professionals in the chemical industry – Our views and expectations

Markush structures are a special representation of the chemical compounds covered in patent documents. Due to their highly generic nature they are more difficult to index and search than specific chemical structures since they require special indexing and search features to make them searchable. Only a few databases exist that offer such features. The currently available indexing systems have been developed mainly in the 1960s to the 1980s, have not been majorly enhanced since then and their future seems somewhat uncertain. However, due to the fact that such Markush systems index unique information describing the chemistry protected by patent claims they are a necessary and indispensable information source for patent information professionals in the chemical industry in order to be able to support important business processes in chemical companies. Therefore we at BASF see a clear need to keep providing and also to keep developing such systems in the future.

A System for Encoding and Searching Markush Structures

The encoding and searching of generic chemical structures, so-called Markush structures, have received little attention in the literature of late. The ability to encode and search these complex entities is of use in various branches of chemoinformatics. We describe a general language for encoding Markush structures and algorithms for searching them and give three examples of the utility of such a system: development of general Free-Wilson analyses of chemical series, detection of controlled substances within a large database of molecular structures, and searching of large databases of virtual compounds.