The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu.

The concept of 'chemical space' reveals itself in two forms: the discrete set of all possible molecules, and multi-dimensional descriptor space encompassing all the possible molecules. Approaches based on this concept are widely used for the analysis and enumeration of compound databases, library design, and structure-activity relationships (SAR) and landscape studies. Visual representations of chemical space differ in their applicability domains and features and require expert knowledge for choosing the right tool for a particular problem. In this review, the authors present recent advances in visualization of the chemical space in the framework of current general understanding of this topic. Attention is given to such methods as van Krevelen diagrams, descriptor plots, principal components analysis (PCA), self-organizing maps (SOM), generative topographic mapping (GTM), graph and network-based approaches. Notable application examples are provided. With the growth of computational power, representations of large datasets are becoming more and more common instruments in the toolboxes of chemoinformaticians. Every scientist in the field can find the method of choice for a particular task. However, there is no universal reference representation of the chemical space currently available and expert knowledge is required.

We introduce a quantum–classical generative model for small-molecule design, specifically targeting KRAS inhibitors for cancer therapy. We apply the method to design, select and synthesize 15 proposed molecules that could notably engage with KRAS for cancer therapy, with two holding promise for future development as inhibitors. This work showcases the potential of quantum computing to generate experimentally validated hits that compare favorably against classical models.