SynPlanner: An End-to-End Tool for Synthesis Planning.

SynPlanner is an end-to-end tool for designing customized retrosynthetic planners from reaction data. It includes a reaction data curation pipeline (reaction atom-to-atom mapping, reaction standardization, and filtration), reaction rule extraction, retrosynthetic model training, and retrosynthetic planning. The tool is designed to be as flexible as possible, supporting the customization of each step of the pipeline to address different needs in the development of customized retrosynthetic planning solutions.

From High Dimensions to Human Insight: Exploring Dimensionality Reduction for Chemical Space Visualization.

Dimensionality reduction is an important exploratory data analysis method that allows high-dimensional data to be represented in a human-interpretable lower-dimensional space. It is extensively applied in the analysis of chemical libraries, where chemical structure data - represented as high-dimensional feature vectors-are transformed into 2D or 3D chemical space maps. In this paper, commonly used dimensionality reduction techniques - Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and Generative Topographic Mapping (GTM) - are evaluated in terms of neighborhood preservation and visualization capability of sets of small molecules from the ChEMBL database.

The freedom space - a new set of commercially available molecules for hit discovery.

The advent of high-performance virtual screening techniques nowadays allows drug designers to explore ultra-large sets of candidate compounds in search of molecules predicted to have desired properties. However, the success of such an endeavor heavily relies on the pertinence (drug-likeness and, foremost, chemical feasibility) of these candidates, or otherwise, virtual screening will return valueless "hits", by the garbage in/garbage out principle. The huge popularity of the judiciously enumerated Enamine REAL Space is clear proof of the strength of this Big Data trend in drug discovery.

Chemography-guided analysis of a reaction path network for ethylene hydrogenation with a model Wilkinson's catalyst.

Visualization and analysis of large chemical reaction networks become rather challenging when conventional graph-based approaches are used. As an alternative, we propose to use the chemical cartography ("chemography") approach, describing the data distribution on a 2-dimensional map. Here, the Generative Topographic Mapping (GTM) algorithm - an advanced chemography approach - has been applied to visualize the reaction path network of a simplified Wilkinson's catalyst-catalyzed hydrogenation containing some 10 structures generated with the help of the Artificial Force Induced Reaction (AFIR) method using either Density Functional Theory or Neural Network Potential (NNP) for potential energy surface calculations.

Implementation of a soft grading system for chemistry in a Moodle plugin: reaction handling.

Here, we present a new method for evaluating questions on chemical reactions in the context of remote education. This method can be used when binary grading is not sufficient as some tolerance may be acceptable. In order to determine a grade, the developed workflow uses the pairwise similarity assessment of two considered reactions, each encoded by a single molecular graph with the help of the Condensed Graph of Reaction (CGR) approach.