Congenericity of Claimed Compounds in Patent Applications

Exploring SureChEMBL from a drug discovery perspective

In the pharmaceutical industry, the patent protection of drugs and medicines is accorded importance because of the high costs involved in the development of novel drugs. Over the years, researchers have analyzed patent documents to identify freedom-to-operate spaces for novel drug candidates. To assist this, several well-established public patent document data repositories have enabled automated methodologies for extracting information on therapeutic agents. In this study, we delve into one such publicly available patent database, SureChEMBL, which catalogues patent documents related to life sciences. Our exploration begins by identifying patent compounds across public chemical data resources, followed by pinpointing sections in patent documents where the chemical annotations were found. Next, we exhibit the potential of compounds to serve as drug candidates by evaluating their conformity to drug-likeness criteria. Lastly, we examine the drug development stage reported for these compounds to understand their clinical success. In summary, our investigation aims at providing a comprehensive overview of the patent compounds catalogued in SureChEMBL, assessing their relevance to pharmaceutical drug discovery.

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.

On the Value of Using 3D Shape and Electrostatic Similarities in Deep Generative Methods

Multiparameter optimization, the heart of drug design, is still an open challenge. Thus, improved methods for automated compound design with multiple controlled properties are desired. Here, we present a significant extension to our previously described fragment-based reinforcement learning method (DeepFMPO) for the generation of novel molecules with optimal properties. As before, the generative process outputs optimized molecules similar to the input structures, now with the improved feature of replacing parts of these molecules with fragments of similar three-dimensional (3D) shape and electrostatics. We developed and benchmarked a new python package, ESP-Sim, for the comparison of the electrostatic potential and the molecular shape, allowing the calculation of high-quality partial charges (e.g., RESP with B3LYP/6-31G**) obtained using the quantum chemistry program Psi4. By performing comparisons of 3D fragments, we can simulate 3D properties while overcoming the notoriously difficult step of accurately describing bioactive conformations. The new improved generative (DeepFMPO v3D) method is demonstrated with a scaffold-hopping exercise identifying CDK2 bioisosteres. The code is open-source and freely available.